Australian Bird Index Phase 2 - Developing Waterbird Indices for National Reporting

Report for the Department of Environment and Energy

October 2019

Australian Bird Index Phase 2 – Developing Waterbird Indices for National Reporting

Rob Clemens, Joris Driessen, Glenn Ehmke

October 2019

Citation This publication should be cited as follows:

Clemens, R., Driessen, J. and Ehmke, G. (2019) Australian Bird Index Phase 2 – Developing Waterbird Indices for National Reporting. Unpublished report for the Department of the Environment. BirdLife Australia, Melbourne.

Copyright © BirdLife Australia This document is subject to copyright and may only be used for the purposes for which it was commissioned. The use or copying of this document in whole or part without the permission of BirdLife Australia is an infringement of copyright.

Disclaimer Although BirdLife Australia has taken all the necessary steps to ensure that an accurate document has been prepared, the organisation accepts no liability for any damages or loss incurred as a result of reliance placed upon the report and its content.

Cover image: Blue-billed Duck Oxyura australis, by Andrew Silcocks.

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Table of Contents

Acknowledgements ...... 4 Executive Summary ...... 5 Introduction ...... 7 Objectives ...... 8 Methods ...... 9 Defining waterbirds ...... 9 Identification of potential data-holding organisations ...... 13 Contact established with listed organisations ...... 14 Compilation of data sharing agreements ...... 14 Data suitability ...... 14 Available Data for Analysis ...... 20 Data Preparation ...... 22 Recent trends (short and medium term) ...... 23 Long-term trends ...... 26 Trends for individual areas ...... 30 Mapping and monitoring adequacy and trend reliability assessment ...... 30 Maps ...... 30 Results ...... 35 Index of population change by functional groups ...... 35 Trends for individual species ...... 37 Long-term species trends ...... 37 Short and medium-term species trends ...... 43 The differences in trends between inland wetlands and coastal habitats ...... 44 Monitoring adequacy and trend reliability ...... 46 Trends from smaller areas ...... 50 Discussion ...... 51 Literature Cited ...... 56 Appendices ...... 60 Appendix A. Alternative methods attempted Appendix B. Comparisons of coastal and inland trends Appendix C. East Australian Waterbird Survey trends Appendix D. Trends within drainage divisions Appendix E. Trends at Shorebird Areas, Ramsar Sites, Key Biodiversity Areas, or River Regions Appendix F. National species trends and spatial distribution of data Appendix G. Monitoring adequacy and trend reliability table Appendix H. List of scientific names

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Acknowledgements

This work was generously funded by the Australian Government’s National Environmental Science Program and was also supported by the Ian Potter Foundation.

A significant number of people contributed to this project in numerous ways through data provision, on-ground knowledge and general advice. Our thanks go to (name / affiliation, in alphabetical order): Richard Fuller (University of Queensland), Robbie Gaffney (DPIPWE, Tas), Heather Graham (Melbourne Water), Birgita Hansen (AWSG), Jason Higham (DEWNR, SA), Greg Hocking (DPIPWE, Tas), Richard Kingston (UNSW), Michael Lenz, Grainne Maguire (BirdLife Australia), Golo Maurer (BirdLife Australia), Peter Menkhorst (DELWP / ARI, Vic), Damian Milne (DENR, NT), James O’Connor (BirdLife Australia), David Paton, Adrian Pinder (DBCA, WA), John Porter (UNSW), Chris Purnell (BirdLife Australia), Danny Rogers (DELWP / ARI, Vic), David Roshier (AWC), Amelia Selles (EHP, QLD), Andrew Silcocks (BirdLife Australia), Marcus Singor, Jennifer Spencer (OEH, NSW), Dan Weller (BirdLife Australia), Mike Weston (Deakin University), Brad Woodworth (University of Queensland), the Australasian Wader Studies Group (AWSG), Canberra Ornithologists Group (COG), North Central Catchment Management Authority, Queensland Wader Study Group (QWSG), all Shorebird 2020 area coordinators and all state biodiversity atlas representatives.

Naomie Johnson, John MacDougall and Scott Laidlaw guided the project for the Department of Environment, showing significant patience with the long-winded ways of ornithologists.

We would also like to thank the thousands of volunteer counters who have contributed to our overarching waterbird knowledge. Without their ongoing efforts in the consistent collection and submission of waterbird count data over the last couple of decades, it would not have been possible to produce this report.

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Executive Summary

Waterbirds and shorebirds are uniquely adapted to the temporary and coastal wetlands of the Australian continent. Unfortunately, Australia’s wetlands and their associated bird communities are under increasing pressure from over-extraction of water, climate change, and intensifying land use patterns. Waterbird and shorebird population trends are highly indicative of the state of Australian wetland habitat as they change in time with the boom and bust cycles of Australia’s temporary wetlands while also reflecting the state of coastal and other permanent wetlands.

In order to keep track of these bird communities and through them the health of our wetlands there is a strong need for robust, long-term monitoring data, a need that is not easily aligned with highly dynamic bird populations capable of utilising the wetland landscape at a continental scale. Such data needs to be able to inform species specific local management decisions (e.g. environmental watering) as well as track species populations and progress toward conservation targets. Phase 2 of the Australian Bird Index reviewed all available waterbird data to develop a nationwide index reflecting population trends at different spatial and temporal scales.

Key stakeholders were approached to contribute monitoring data to allow large scale analysis and index development, resulting in a collection of over 4 million waterbird and shorebird observations, half of which were suitable for further analyses. Several different analytical approaches were tested to capture the huge temporal and spatial variation inherent in waterbird data.

Our review of available Australian waterbird data shows large drops in waterbird abundance since the 1980s, a continuing decline of migratory shorebirds in recent years, and low waterbird numbers since the boom in many species populations observed following the 2011 floods in eastern Australia, all in line with previously reported (Kingsford and Porter 2009, Kingsford et al. 2018, Studds et al. 2017). These large-scale declines point toward the need to review the conservation targets of waterbird species in order to ensure large flocks of waterbirds persist in Australia. There is also a need to flag those species that are at increasing risk of extinction while expanding efforts to apply management actions to restore waterbird populations. This review highlights that monitoring efforts will need to be increased if the success or failure of such actions are to be tracked in all regions and at a variety of spatial scales. The consolidation of the best available data in this review highlighted how waterbird species trends can be reported at a variety of spatial scales including individual wetlands, Ramsar sites, Key Biodiversity Areas, drainage divisions, river regions as well as nationally. This kind of reporting could be automated and made available at the click of the button if data were consistently consolidated within an on-line data base. While such reporting should only be viewed as exploratory with a need for verification and consideration against the available data, such automation would provide instantaneous updates of what the best available data suggests about waterbird populations at a variety of spatial scales. This marks the first-time species trends have been reported for many of these areas, and continued automated reporting would improve monitoring efforts as they expand. We strongly encourage centralised storage of all waterbird data and development of automated graphical and quantitative trend reporting.

Results reported here were based on an automated approach resulting in trend estimates that could be different with a more meticulous vetting of the data used in analysis. In addition, different methods

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can use different amounts of the available data which may contain different signals, so more inclusive methods may produce more global results for some species. Finally, some results vary depending on the assumptions made in the analysis, and such assumptions have not been fully tested, but the assumptions made do result in different estimates of trend. Continued work on optimising species- and site-specific filters on which data to include, and which methods to employ will continue to improve the precision of these kinds of estimates, and eventually we can see an automated trend reporting method working well if those steps are followed. In the meantime, the results presented here need to be viewed along with other analyses that have been conducted, and as one step along a journey toward presenting automated trend results.

The need for improved monitoring efforts was clear from this review. Despite having 4 million waterbird records available, we only identified widespread trends for a handful of additional species to those counted in either the East Australian Waterbird Survey (EAWS) or Shorebirds 2020 programs. This is not surprising given there are few waterbird monitoring programs that have persisted for over a decade with targeted sampling aimed at understanding waterbird populations. When clear questions, targets, and long-term consistent effort are lacking, data collected are often unable to deliver answers (Legge et al. 2018).

This review demonstrates the utility of broad scale systematic monitoring programs, and here we have identified a number of monitoring gaps, which should be filled in order to track our understanding of how waterbird numbers can be maintained in a changing Australia. BirdLife Australia is currently in the early stages of working towards a national waterbird monitoring programme, which will build on existing and historic survey efforts. This expanded waterbird monitoring will need to focus on those areas where additional monitoring could immediately strengthen our ability to calculate robust trends, as well as those areas where water and wetlands can be managed for waterbirds but where additional data is needed to determine population change at local and regional scales.

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Introduction

Waterbirds are iconic Australian birds, uniquely adapted to a landscape in which resources are often scarce and unpredictably available, occasionally in abundance. Given their capacity to exist in highly dynamic environments, involving the ability to undertake long-range movements in response to rainfall or droughts or delay of breeding until optimal conditions arise, the group at large poses unique challenges to maintaining long-term, rigorous monitoring efforts. Logistics, accessibility and limited human resources pose challenges to maintaining such efforts, particularly in remote areas. Nevertheless, the need to understand population dynamics is particularly important when considering the current context of climate change, increasing water use across society, land use patterns and wetland habitat loss (The State of the Environment report - SOE 2016). Waterbirds are uniquely placed as wetland indicators given their relative abundance and almost instantaneous reaction time to changes in conditions. There is also a need to better understand the effects of environmental watering on waterbird populations. By reporting national trends and local trends, factors leading to local population declines are more likely to be distinguished from broad continental wide changes in abundance. The 2011 State of the Environment report assessed Australia’s capacity to report nationally on biodiversity trends as generally poor, but it was noted that birds represented a good opportunity to provide a faunal component in the accounts. Since the development of the Australian Bird Index partnership project in 2013, and particularly the development of the Australian Bird Index for terrestrial birds for most major bioregions in Australia in 2015 (BirdLife Australia, 2015), consensus that national bird indices can and should be developed for national environment reporting has strengthened considerably. Historically, large scale faunal indicators that provide quantifiable measurements in an easy to understand format have not been available for Australia. The intent of the underlying project is to extend the work on terrestrial bird indices commenced with the support of the federal government environment department in 2013 under the Natural Environmental Research Programme (NERP). The current project was funded Natural Environmental Science Programme (NESP) to develop the foundations for national indices for waterbirds and resident shorebird species in Australia. This project brings us one step closer to development of a comprehensive set of indices representing all major bird groups. Bird indices can provide a tool for tracking Australia’s environmental sustainability, informing policy development and management of natural resources, as they have elsewhere (Gregory et al. 2008; https://www.gov.uk/government/statistics/wild-bird-populations-in-the-uk). High level “headline” indicators such as these provide relatively simple, immediate information to policy-makers, decision- makers and the general public. Although crucial to our wider understanding and general information dissemination, these indicators are not generally intended to provide detailed ecological information (Biodiversity Indicators Partnership, 2010; Gregory et al., 2008; Bibby, 1999), and will not remove the need for species-specific assessments. These index-based projects have advanced our capacity to conduct such species assessments. In this context, this project aimed to collate, assess and analyse waterbird monitoring data from across the country, to test analytical techniques suitable for building a Waterbird Index for Australia, and initiating an ongoing collaboration between stakeholders to ensure ongoing production and use and dissemination of such an Index. The project also generated trend estimates for waterbird species at a variety of spatial and temporal scales, a process that could be automated, and would facilitate regular reporting for areas not previously reported.

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Objectives The primary objective of this project was to develop transparent and repeatable methods for quantifying population trends of Australia’s waterbird populations through population indices, to track the overall health of these bird groups at national, regional and local scales, and for use as indicators of wetland and coastal environments. To this end, this project will aggregate structured datasets, refine methodologies developed for the terrestrial bird indices, and produce a set of new national and regional indices for waterbirds.

Secondary objectives to which project outcomes can contribute:

• incorporation of waterbird population indices in National State of Environment (SOE) Reporting;

• provide ‘early-warning’ signals of waterbird species in decline in regions, through the waterbird population indices data, informing managers and other end-users in the fields of freshwater and coastal ecology, water management, and threatened wetland, waterbird management and conservation;

• engagement of stakeholders and community in wetland and habitat protection, conservation, management, and structured monitoring; and,

• increased community awareness of the population status and trends of Australia’s waterbirds

The project outcome is intended to enhance our ability to:

• design an effective holistic monitoring program for waterbirds

• set conservation priorities for species;

• measure the impacts of land use change on birds;

• track overall population numbers as a broad indicator of the success of conservation actions;

• provide quantitative data for potential use in Criterion A for IUCN and EPBC conservation assessments; and,

• inform wildlife conservation plans at regional scales.

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Methods

Defining waterbirds

Despite its wide and frequent usage, an objectively definition of a ‘waterbird’ is something that seems to have eluded scientists over the years, as evidenced by the almost constant difference in species listed in waterbird reports and publications. This is partly a function of Australia’s huge size and the ecological breadth of its avifauna – there is almost always an exception to any rule (a situation not unique to waterbirds). While endlessly debating such details is perhaps counter-productive, failing to holistically define what you are reporting on usually leads to substantial numbers of species falling through the cracks of our collective knowledge. Rather than analyzing whatever data is conveniently accessible and reporting those findings, we have sought to systematically define and list all bird taxa which are waterbirds in the broad sense and we report trends (where possible), but also define where data is deficient.

To achieve this goal however, we first need a definition of a “waterbird”. Despite decades of cumulative ecological knowledge through publications such as the Handbook of Australian, New Zealand and Antarctic birds (HANZAB, 2006), this is anything but a straightforward task. Taxonomic (evolutionary) groupings are often used to group birds. However as evolutionary classifications reflect dynamics over geological time scales and given that taxonomic techniques are themselves constantly evolving, taxonomic classifications are inherently problematic when applied to the current slice of time we occupy. Taxonomic groupings for instance do not always reflect threatening processes, managements required or even the habitats in which birds live in the present. Perhaps more importantly taxonomic classifications are notoriously unstable, even at higher levels such as family or order (Joseph & Buchanan 2015) and this often renders groupings obsolete as they are published and hampers conservation efforts (Garnett and Christidis 2017). In this report, we defined ‘waterbirds’ holistically and considered any species that depend on wetlands, waterways or shorelines for feeding or breeding habitats. This includes shoreline-dependent birds (but not all ‘shorebirds”, e.g. not Plains-wanderers or Bush Stone-curlew) as they all rely on wet habitats. Although wetland-dependent passerines (Cisticolas, Grassbirds and Reed-Warblers), the wetland-dependent raptor Swamp Harrier and some marine piscivores which occur commonly around shorelines (e.g. Black-faced Cormorant, Little and Fairy Tern) are often ignored in waterbird research these taxa are clearly waterbirds and are listed as such in this report. Terrestrial floodplain birds (e.g. Superb and Eastern Regent Parrots) were not listed as although they do depend on wetlands (floodplains), they are generally considered woodland-dependent birds and are well covered by terrestrial monitoring programs. Another group of wetland-associated birds often missing from waterbird research and conservation are passerines that depend on northern mangrove habitats. According to the Handbook of Australian, New Zealand and Antarctic Birds (HANZAB) there are only four Australian bird taxa which breed and feed exclusively in mangrove habitats; Collared Kingfisher, Dusky Gerygone, White-breasted Whistler, Mangrove Robin (Garnett et al 2015). However, HANZAB also lists around 100 other taxa as breeding and/or feed in mangroves but which also breed and/or feed in other habitats. Therefore it is not immediately clear, without deeper research, whether these birds are most appropriately classed as waterbirds or terrestrial species. Consequently, mangrove waterbirds are not considered here and are therefore a key knowledge gap in waterbird research and conservation.

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Table 1 provides a listing overview of all species considered for inclusion in this project as well as Environment Protection and Biodiversity Conservation Act (EPBC) and Australian International International Union for Conservation of Nature (IUCN) Red List conservation status (BirdLife Australia Threatened Species Committee). The table also includes functional and habitat groupings which afford the opportunity to calculate trends of average abundance for groups to use as proxy indicators for unrepresented species and inform monitoring adequacy and knowledge gaps. Two main groupings are listed. The first comprises five groups (Kingsford and Porter 1994), and is referred to henceforth as “functional groups”. This grouping is based on mix of taxonomic and feeding guilds and can be compared directly with previous literature. A second grouping based on habitat associations was also defined. These groupings were based on the habitats on which waterbird taxa depend or are primarily associated with and are drawn from published information on species feeding/breeding habitat preferences as summarized in (Garnett et al. 2015). This approach affords the opportunity to correlate trend patterns with habitat-based management units more directly. Non-migratory (i.e. breeding in Australia) and migratory (non-breeding) shorebirds separated because of their fundamentally different life histories, prevalent threats and monitoring requirements.

Species are used as the primary taxonomic unit for all analyses here, however it is noteworthy that many waterbirds have substantive subspecific variation and in some cases extinction risk varies markedly between (see table for details). Hence all waterbird taxa (species and subspecies) are listed below (Table 1) and where subspecific differences are pertinent, monitoring adequacy and trend reliability is assessed on a subspecies basis.

Taxa which are vagrant to Australia were not considered here. Vagrancy is defined as a taxon which does not breed in Australia and which has a non-breeding population of <1% of the estimated global population. Vagrant taxa are included in Appendix H.

Table 1. Species considered for inclusion into waterbird index, their current conservation status and associated functional and habitat groupings. Subspecies are shown where subspecific differences are substantiative and are identified in non-bold inset text. See Appendix H for a list of scientific names and list of vagrant taxa not considered here.

Australian IUCN EPBC Taxon name status status Functional groups Habitat groups Magpie Goose Herbivores Open fresh waters Spotted Whistling-Duck Ducks, small grebes and jacanas Open fresh waters Plumed Whistling-Duck Ducks, small grebes and jacanas Open fresh waters Wandering Whistling-Duck Ducks, small grebes and jacanas Open fresh waters Blue-billed Duck NT Ducks, small grebes and jacanas Open fresh waters Pink-eared Duck Ducks, small grebes and jacanas Open fresh waters Cape Barren Goose Herbivores Upland Eastern Cape Barren Goose Herbivores Upland Recherche Cape Barren Goose VU VU Herbivores Upland Black Swan Herbivores Open fresh waters Radjah Shelduck Ducks, small grebes and jacanas Open fresh waters Australian Shelduck Ducks, small grebes and jacanas Open fresh waters Hardhead Ducks, small grebes and jacanas Open fresh waters Australasian Shoveler Ducks, small grebes and jacanas Open fresh waters Pacific Black Duck Ducks, small grebes and jacanas Open fresh waters Grey Teal Ducks, small grebes and jacanas Open fresh waters

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Chestnut Teal Ducks, small grebes and jacanas Open fresh waters Freckled Duck Ducks, small grebes and jacanas Open fresh waters Musk Duck Ducks, small grebes and jacanas Open fresh waters Australian Wood Duck Ducks, small grebes and jacanas Open fresh waters Cotton Pygmy-goose Ducks, small grebes and jacanas Open fresh waters Green Pygmy-goose Ducks, small grebes and jacanas Open fresh waters Australasian Grebe Ducks, small grebes and jacanas Open fresh waters Hoary-headed Grebe Ducks, small grebes and jacanas Open fresh waters Great Crested Grebe Piscivores Open fresh waters Red-necked Crake Crakes and rails Thick wetland vegetation Lewin's Rail Crakes and rails Thick wetland vegetation Tasmanian Lewin's Rail Crakes and rails Thick wetland vegetation Eastern Australian Lewin's Rail Crakes and rails Thick wetland vegetation Chestnut Rail Crakes and rails Thick wetland vegetation Buff-banded Rail Crakes and rails Thick wetland vegetation Cocos Keeling Buff-banded Rail VU EN Crakes and rails Thick wetland vegetation Australian Buff-banded Rail Crakes and rails Thick wetland vegetation Australian Spotted Crake Crakes and rails Thick wetland vegetation Baillon's Crake Crakes and rails Thick wetland vegetation Spotless Crake Crakes and rails Thick wetland vegetation Pale-vented Bush-hen Crakes and rails Wetland generalist White-breasted Waterhen Crakes and rails Wetland generalist White-browed Crake Crakes and rails Thick wetland vegetation Purple Swamphen Herbivores Wetland generalist Western Australian Purple Swamphen Herbivores Wetland generalist Australasian Purple Swamphen Herbivores Wetland generalist Dusky Moorhen Crakes and rails Wetland generalist Black-tailed Native-hen Herbivores Wetland generalist Tasmanian Native-hen Herbivores Wetland generalist Eurasian Coot Herbivores Open fresh waters Sarus Crane Large wading birds Mid-depth fresh waters Brolga Large wading birds Mid-depth fresh waters Bush Stone-curlew Shorebirds Terrestrial Beach Stone-curlew Shorebirds Shoreline (resident) Australian Pied Oystercatcher Oystercatchers Shoreline (resident) Sooty Oystercatcher Oystercatchers Shoreline (resident) Southern Sooty Oystercatcher Oystercatchers Shoreline (resident) Northern Sooty Oystercatcher Oystercatchers Shoreline (resident) Banded Stilt Shorebirds Shallow waters Red-necked Avocet Shorebirds Shallow waters Black-winged Stilt Shorebirds Shallow waters Grey Plover NT Shorebirds Shoreline (migratory) Pacific Golden Plover Shorebirds Shoreline (migratory) Red-capped Plover Shorebirds Shoreline (resident) Double-banded Plover Shorebirds Shoreline (migratory) Lesser Sand Plover EN EN Shorebirds Shoreline (migratory) Mongolian Lesser Sand Plover EN Shorebirds Shoreline (migratory) Kamchatkan Lesser Sand Plover EN Shorebirds Shoreline (migratory) Greater Sand Plover VU VU Shorebirds Shoreline (migratory) Mongolian Greater Sand Plover VU Shorebirds Shoreline (migratory) Oriental Plover Shorebirds Shoreline (migratory) Hooded Plover VU Shorebirds Shoreline (resident) Eastern Hooded Plover VU VU Shorebirds Shoreline (resident) Western Hooded Plover Shorebirds Shoreline (resident) Black-fronted Dotterel Shorebirds Shoreline (resident)

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Banded Lapwing Shorebirds Upland/wetland Masked Lapwing Shorebirds Upland/wetland Northern Masked Lapwing Shorebirds Upland/wetland Southern Masked Lapwing Shorebirds Upland/wetland Red-kneed Dotterel Shorebirds Shoreline (resident) Inland Dotterel Shorebirds Upland Australian Painted-snipe EN EN Shorebirds Shallow waters Comb-crested Jacana Ducks, small grebes and jacanas Open fresh waters Whimbrel Shorebirds Shoreline (migratory) Little Curlew Shorebirds Shoreline (migratory) Far Eastern Curlew CR CR Shorebirds Shoreline (migratory) Bar-tailed Godwit Shorebirds Shoreline (migratory) Western Alaskan Bar-tailed Godwit VU VU Shorebirds Shoreline (migratory) Northern Siberian Bar-tailed Godwit EN CR Shorebirds Shoreline (migratory) Black-tailed Godwit NT Shorebirds Shoreline (migratory) Ruddy Turnstone NT Shorebirds Shoreline (migratory) Great Knot EN CR Shorebirds Shoreline (migratory) Red Knot EN EN Shorebirds Shoreline (migratory) New Siberian Islands Red Knot EN Shorebirds Shoreline (migratory) North-eastern Siberian Red Knot EN Shorebirds Shoreline (migratory) Broad-billed Sandpiper Shorebirds Shoreline (migratory) Sharp-tailed Sandpiper Shorebirds Shoreline (migratory) Curlew Sandpiper CR CR Shorebirds Shoreline (migratory) Long-toed Stint Shorebirds Shoreline (migratory) Red-necked Stint NT Shorebirds Shoreline (migratory) Sanderling Shorebirds Shoreline (migratory) Pectoral Sandpiper Shorebirds Shoreline (migratory) Asian Dowitcher NT Shorebirds Shoreline (migratory) Latham's Snipe Shorebirds Thick wetland vegetation Pintail Snipe Shorebirds Shoreline (migratory) Swinhoe's Snipe Shorebirds Shoreline (migratory) Terek Sandpiper VU Shorebirds Shoreline (migratory) Common Sandpiper Shorebirds Shoreline (migratory) Grey-tailed Tattler Shorebirds Shoreline (migratory) Wandering Tattler Shorebirds Shoreline (migratory) Common Greenshank Shorebirds Shoreline (migratory) Wood Sandpiper Shorebirds Shoreline (migratory) Marsh Sandpiper Shorebirds Shoreline (migratory) Red-necked Phalarope Shorebirds Shoreline (migratory) Australian Pratincole Shorebirds Upland/wetland Oriental Pratincole Shorebirds Shoreline (migratory) Silver Gull Open fresh/saline waters Little Tern Piscivores Marine Western Pacific Little Tern Piscivores Marine Fairy Tern VU Piscivores Marine New Caledonian Fairy Tern EN Piscivores Marine Eastern Australian Fairy Tern Not yet assessed* Piscivores Marine Western Fairy Tern Not yet assessed* Piscivores Marine Common Gull-billed Tern Piscivores Marine Asian Gull-billed Tern Piscivores Marine Australian Gull-billed Tern Piscivores Open fresh/saline waters Caspian Tern Piscivores Marine Whiskered Tern Piscivores Open fresh/saline waters White-winged Black Tern Piscivores Open fresh/saline waters Common Tern Piscivores Marine

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Lesser Crested Tern Piscivores Marine Crested Tern Piscivores Marine Black-necked Stork Large wading birds Mid-depth fresh waters Australian Pelican Piscivores Open fresh/saline waters Australasian Bittern EN EN Large wading birds Thick wetland vegetation Australian Little Bittern Large wading birds Thick wetland vegetation Black Bittern Large wading birds Thick wetland vegetation Nankeen Night-Heron Large wading birds Mid-depth fresh waters Striated Heron Large wading birds Shoreline (resident) Cattle Egret Large wading birds Upland/wetland White-necked Heron Large wading birds Mid-depth fresh waters Great-billed Heron Large wading birds Shoreline (resident) Great Egret Large wading birds Mid-depth fresh waters Intermediate Egret Large wading birds Mid-depth fresh waters Pied Heron Large wading birds Mid-depth fresh waters White-faced Heron Large wading birds Mid-depth fresh waters Little Egret Large wading birds Mid-depth fresh waters Eastern Reef Egret Large wading birds Shoreline (resident) Australian White Ibis Large wading birds Upland/wetland Straw-necked Ibis Large wading birds Upland/wetland Yellow-billed Spoonbill Large wading birds Mid-depth fresh waters Royal Spoonbill Large wading birds Mid-depth fresh waters Glossy Ibis Large wading birds Mid-depth fresh waters Little Pied Cormorant Piscivores Open fresh/saline waters Great Cormorant Piscivores Open fresh/saline waters Little Black Cormorant Piscivores Open fresh/saline waters Black-faced Cormorant Piscivores Open fresh/saline waters Pied Cormorant Piscivores Open fresh/saline waters Australasian Darter Piscivores Open fresh waters Swamp Harrier Wetland raptor Wetland raptor Golden-headed Cisticola Passerines Thick wetland vegetation Zitting Cisticola Passerines Thick wetland vegetation Tawny Grassbird Passerines Thick wetland vegetation Little Grassbird Passerines Thick wetland vegetation Australian Reed-Warbler Passerines Thick wetland vegetation * Note the Australian Fairy Tern (now obsolete) is currently classified as vulnerable under Australian IUCN and EPBC lists.

Identification of potential data-holding organisations A large number of organisations and individuals are or have been involved in aspects of waterbird monitoring in Australia, something which made the identification of stakeholders and data custodians challenging. BirdLife Australia’s internal and external networks of contacts were used extensively to identify as many data holders as possible. To maintain a workable project workload, a long list of possible contacts was whittled down to a list of key contacts, these in turn being able to utilise their own regional / local points of contact. A total of 30 organisations or departments thereof, as well as six state government databases, the Atlas of Living Australia, the BirdLife Australia Atlas of Australian Birds and several private individuals were identified as potential data holders of monitoring data. Although a number of smaller organisations – in particular catchment authorities and departments within state governments – doubtless hold local monitoring data, for this report the focus has largely been on known potential ‘big’ data holders whose contributions are crucial to project feasibility. Future iterations of the index should consider inclusion

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of such relatively fine-scale monitoring data and target datasets for species and groups that are poorly monitored, and which have at least ten years of data collected in the same way. Contact established with listed organisations All potential data holders were contacted by BirdLife Australia between approximately June 2016 and December 2017, with follow up communication as required. As of September 2018 additional datasets were still being received. Overlap with the ongoing NESP Project 3.1 (Threatened Species Index, NESP Threatened Species Recovery Hub, 2018) was utilised to maximise efficiency of data collation for specific species under consideration for both projects (Australasian Bittern, Australian Painted-snipe, Blue-billed Duck and migratory shorebirds) as well as full access to state biodiversity databases. Without exception data holders responded in a positive fashion to the project’s request for data. Timeliness of actual data delivery varied widely due to data holders’ resource limitations in relation to data processing required. In a number of cases the authors processed and returned ‘clean’ datasets as an in-kind favour. Several datasets did not become available until after the cut-off date required for final analysis. A number of historical datasets not currently in digital format are likely to become available as well in the near future. It is the intention these data will be incorporated in a future analytical iteration. Such additions will further strengthen the index work undertaken to date. Focus was on gathering abundance data, and further work would be needed to analyse presence-absence data (for which well-established methods exist – e.g. ABI stage 1) and methods for demographic monitoring such as survival rates (from colour banded birds) or breeding monitoring (which are critical for several waterbird species). Compilation of data sharing agreements Using existing synergies between NESP 3.1 and the Waterbirds Index Project, a data sharing agreement was drafted between BLA, the Threatened Species Recovery Hub representative at the University of Queensland and UQ’s legal department. Given the sensitivity of data sharing in general, multiple drafts were required to produce an approved final draft applicable to waterbirds specifically (July 2016). This represented a significant resource investment. Subsequent dealings with potential data providers in the context of this agreement were largely straightforward, though a number of organisations required additional clauses to the document in relation to utilisation of shared datasets. Additional requirements largely focussed on site-specific data (and use thereof) and not on species sensitivity, given the majority of waterbirds and resident shorebirds are not individually of particular conservation concern.

Data suitability Understanding whether, and where, species are increasing or declining is crucial for monitoring progress towards global biodiversity conservation targets, justifying management resourcing, and stimulating a targeted response to environmental problems. However, any two monitoring programs collecting data to inform such trends for a given species are likely to differ in the data collectors, populations and locations being monitored, and monitoring protocols (e.g. timing, frequency, spatial coverage, equipment). Moreover, the way in which biological data are stored are highly inconsistent and metadata standards generally focus on spatial applications at the expense of the much more detailed requirements necessary to construct time series for trend analysis. This makes collating and using existing monitoring data to inform indicators of national or global trends in biodiversity change particularly challenging. While there are many criteria for the qualities of a useful biodiversity change

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indicator in terms of sensitivity, specificity, and responsiveness, most frameworks assume that monitoring data is representative and appropriate for the creation of legitimate indicators are established a priori. However, this is rarely the case when disparate data sources are used. Therefore, significant data processing and filtering based on diagnostics are critical steps when calculating trends from multiple data sources. Perhaps the most critical part of assessing the suitability of data for population trend analysis is the grouping of raw data points (which are usually largely unstructured) into time series. A ‘time series’ can be defined as “a collection of observations of well-defined data items obtained through repeated measurements over time (ABS 2018). For wildlife populations this equates to longitudinal sequence of data points collected in a comparable (i.e. methodologically standardized) way at the same location (e.g. a site) over time. Without applying the structure of repeated measures time series approach to ecological data, estimated trends are subject to untold amounts of confounding and bias (Gregory and van Strien 2010; Voříšek et al. 2008). The development of the Waterbird Index clearly illustrated the challenges when dealing with species population data gathered from multiple sources, with a wide range of different methods and survey effort evident in the data collated (Table 2). In order to maximise the quality and consistency of data taken forward and form legitimate time series for analysis, a set of criteria for assessing the suitability of available long-term monitoring datasets for inclusion in trend analyses was developed, including the level of standardisation of monitoring methods/effort, consistency in turnover of monitored sites, frequency and timing of monitoring, and spatial accuracy of recorded species locations. An overview of the suitability criteria used is outlined below. Data was excluded from analyses if any of the following criteria applied:  Monitoring method was not provided;  There was no standardisation of monitoring method and/or surveys were not site-based;  Overall temporal coverage of time-series was less than 5 years;  The consistency of monitoring was too biased/confounded for analyses  Thresholds for each analysis were not met i.e. too many missing values, too few years of data, too few birds observed regularly;  Spatial accuracy was not defined, and no spatial metadata exists;  Unit of abundance was lacking (monitoring which records presence/absence only);  Unit of measurement was an incidental sighting or otherwise non-standardised observation.

Table 2 provides an overview of the 24 stakeholders from which data were requested, of which 35 datasets were received or accessed, totalling roughly 4 million records. The relatively low number of stakeholders masks the involvement of numerous smaller groups (BirdLife Australia branches, local ornithological / bird watching groups etc.) who have been contributing to these projects for many years. The majority of datasets were considered suitable in essence, having been undertaken over a sufficient number of years, using fixed sites as well as standardised methods and survey effort. One organisation did not hold any structured waterbird monitoring data. Eight datasets – largely state atlases - were not taken forward as these contained insufficient suitable data or metadata from which to define time series.

Australian Bird Index Phase 2 – Developing Waterbird Indices for National Reporting 15

Table 2 Overview of all datasets collated and assessed for inclusion in analysis (in grey: considered suitable)

Provider Monitoring Received / Time Period Survey Species Range Notes (TBI: Standardised Standardised Fixed True Unit Suitable? type accessed frequency to be method? effort? sites absences included) used? recorded?

Wetland Yes 1981-2016 2-6 times / All Two Additional Yes Yes Yes Yes Counts Yes year shorebirds wetland data complexes received in Vic after cut-off date, TBI in future DELWP/ARI iteration Duck Yes 2007-2017 Annual All species Wetlands Further 20 Yes Yes Yes Yes Counts Yes counts throughout years VIC available, TBI in future iteration DPIPWE Duck Yes 1987-2017 Annual Duck Wetlands Yes Yes Yes Yes Counts Yes counts species only throughout north and east Tasmania DEWNR Wetland No 2003-2017 Annual Duck 85 Data Yes Yes Yes Yes Counts Yes counts species only wetlands processing throughout undertaken se SA internally, TBI in future iteration David Paton Wetland Yes 2000-2017 Annual All species Coorong Yes Yes Yes Yes Counts Yes counts

OEH Wetland Yes 2008-2017 1-2 times / All species 4 major Yes Yes Yes Yes Counts Yes counts year wetland complexes in inland NSW DENR Waterbird Yes 1980-2015 Largely Colonial Range of Yes No Yes Yes Counts Yes colony one-off waterbirds wetlands in counts coastal NT

Australian Bird Index Phase 2 – Developing Waterbird Indices for National Reporting 16

Atlas Yes 1998-2018 Variable All species Nationwide Yes Variable Yes Variable Counts / Yes Occupancy Species- Yes 1998-2013 Variable Australian Across Variable Variable Variable Yes Counts No specific Painted- distribution counts snipe range

Species- Yes 1998-2018 Variable Australasia Across Variable Variable Variable Yes Counts Yes specific n Bittern distribution counts range Species- Yes 1980-2014 Biennial Beach- 400-500 Yes Yes Yes Yes Counts Yes specific nesting coastal sites counts shorebirds across BirdLife mainland Australia SE Australia Wetland Yes 1994-1997 Annual to All species Wetlands Yes Variable Yes Yes Counts Yes Counts tri-annual across (Murray inland NSW Darling and Basin Northern Database) Vic Wetland Yes 1987-1992 Annual to All species 100s of Yes Variable Yes Yes Counts Yes counts monthly wetlands (Victorian across VIC Waterbird Database) BirdLife Wetland Yes 1973-2017 Annual, bi- All species 100s Data has Yes Variable Yes Yes Counts Yes Australia / counts annual to wetlands coastal AWSG (Shorebirds monthly across bias. 2020 Australia Waterbirds project) in most areas only more consistently recorded since 2008 Wetland Yes 1997-2013 Annual All species Selected Yes Yes Yes Yes Counts Yes counts wetlands in DBCA (salinity SW WA study)

Australian Bird Index Phase 2 – Developing Waterbird Indices for National Reporting 17

Wetland Yes 1981-1987 Annual to All species 100s of Yes Variable Yes Yes Counts Yes counts (SW monthly wetlands WA) across SW WA Wetland No 1987-1992 Annual All species 100s of Not digitally Yes Variable Yes Yes Counts Yes counts wetlands available at across SW time of WA writing, TBI in future iteration Wetland Yes 2006-2015 Bi-annual All species Wetland Yes Variable Yes Yes Counts Yes counts complex in Esperance region VBA Range of Accessed NA Variable All species Vic Variable Variable Variable No Variable No datasets

ALA Range of Accessed NA Variable All species Nationwide Variable Variable Variable Variable Variable No datasets

BDBSA Range of Yes 2003-2015 Variable All species SA Variable Yes Yes Yes Counts No (time datasets series too short)

NVA WA Range of Accessed NA Variable All species WA Incidental Variable Variable No Incidental No datasets NVA Tas Range of Accessed NA Variable All species Tas Incidental Variable Variable No Incidental No datasets WildNet Range of Accessed NA Variable All species QLD Incidental Variable Variable No Incidental No datasets Bionet Range of Accessed NA Variable All species NSW Incidental Variable Variable No Incidental No datasets East Yes 1983-2017 Annual All species Eastern 1/3 Yes Yes Yes Yes Counts Yes Australian of Waterbird continent UNSW Surveys Murray Yes 2007-2013 Annual All species MDB Yes Yes Yes Yes Counts Yes Icon counts wetlands

Australian Bird Index Phase 2 – Developing Waterbird Indices for National Reporting 18

Hydro Yes 2010-2013 Annual All species MDB Yes Yes Yes Yes Counts Yes Indicators wetlands counts AWC No data NA NA NA NA NA NA NA NA NA NA NA

Melbourne Wetland Yes 2012-2017 Annual to All species Wetlands in Yes Yes Yes Yes Counts Yes Water counts monthly Greater Melbourne North Wetland Yes 2009-2017 Up to 6 All species 25 Yes Yes Yes Yes Counts Yes Central counts times / year wetlands in CMA northern VIC QWSG Wetland Yes 1993-2017 Annual to All species 100s of QLD Yes Yes Yes Yes Counts Yes counts monthly wetlands Marcus Species- Yes 1991-2015 Annual to Western Inland lake Yes Yes Variable Yes Counts Yes Singor specific monthly Hooded counts WA counts Plover COG Wetland Yes 1987-2016 At least All species Wetlands in Yes Yes Yes Yes Counts Yes counts annually ACT / NSW Danny Coastal Yes 2004-2016 Tri-annual Shorebirds 80 Mile Yes Yes Yes Yes Counts Yes Rogers / counts Beach, MYSMA Roebuck Bay DELWP/ARI: Department of Environment, Land, Water & Planning / Arthur Rylah Institute; DPIPWE: Department of Primary Industries, Parks, Water and Environment; DEWNR: Department for Environment and Water; OEH: Office of Environment and Heritage; DENR: Department of Environment and Natural Resources; DBCA: Department of Biodiversity, Conservation and Attractions; VBA: Victorian Biodiversity Atlas; ALA: Atlas of Living Australia ; BDBSA: Biological Databases of South Australia; Natural Values Atlas Western Australia: ; Natural Values Atlas Tasmania: ; WildNet: Queensland WildNet ; Bionet: NSW Bionet; UNSW: University of New South Wales; AWC: Australian Wildlife Conservation; MYSMA: Monitoring Yellow Sea Migrants in Australia; COG: Canberra Ornithologists Club

Australian Bird Index Phase 2 – Developing Waterbird Indices for National Reporting 19

Available Data for Analysis

Waterbirds in Australia are a very diverse group of species that use a wide variety of wetland habitats (HANZAB, 2006). Many of these species are well adapted to seeking out temporary wetlands within the interior of the continent, an area where the availability of water can vary tremendously at large spatial and temporal scales (Kingsford & Norman, 2002). Species distributed primarily along the coasts can be found far more consistently at individual wetlands from year to year but can vary tremendously seasonally as international migrants occur in peak numbers only during the non-breeding season (Clemens et al., 2012). At coastal areas, many local populations can be measured repeatedly and independently by counting relatively small contiguous wetlands, or groups of wetlands (Clemens et al., 2014). However, at inland wetlands waterbirds often move between wetlands hundreds of kilometres apart (Roshier et al., 2001), and some species appear to regularly move over 1000 kilometres (Pedler et al., 2014). Non-migratory waterbirds also move and form larger flocks during the non-breeding season (Griffioen & Clarke, 2002; Taylor et al., 2014). As a result, it is not uncommon for a time series for a species at an individual inland wetland to primarily consist of zero counts, with a few small counts, and perhaps one or two huge counts in the tens of thousands.

Fortunately, many waterbirds have been systematically surveyed across 1/3 of the Australian continent for over three decades, resulting in a dataset which overcomes the extreme temporal and spatial variation observed at local scales (Kingsford & Porter, 2009a). Known as the East Australian Waterbird Survey (EAWS), this long-term data-set provides the most robust data on waterbird populations that frequent inland wetlands in Australia. These data have been critical in growing our understanding of the impact that water extraction is having on these birds (Kingsford, Bino et al. 2017).

Along the coastlines, the best available waterbird data has focussed on migratory shorebirds (Clemens et al., 2012) as part of the “Shorebirds 2020” project (BirdLife Australia / Australasian Wader Study Group / Queensland Wader Studies Group). These data go further back in time in the south of the continent, but have grown to include many critical locations further north in recent decades and population trend analyses from these data have been the foundation of recent threatened species listings (Reid & Park, 2003; Creed & Bailey, 2009; Wilson et al., 2011; Minton et al., 2012; Hansen et al., 2015; Clemens et al., 2016; Studds et al., 2017). Interestingly, all waterbirds are increasingly being counted as part of these repeated surveys, but the non-shorebird data is far more variable than the shorebird data as other waterbirds are less consistently found at many of the high tide roosts counted in the shorebird surveys and in many instances counting anything more than shorebirds is impossible within the time available as dictated by tides. Nonetheless, this is becoming a growing source of coastal waterbird data being collected at many areas throughout Australia.

However, while these two sources of data are generally good for congregatory species such as migratory shorebirds and waterfowl, the sampling methods employed in these monitoring programs do not cater for all waterbirds. Small, cryptic or dispersed species are not generally detectable from aerial surveys and are not generally systematically counted in waterbird surveys. Similarly, targeted survey effort for species such as migratory shorebirds results in counts occurring at high-tide at the small areas used for roosting, something which will fail to capture consistent counts of many other waterbirds. For example dispersed-nesting shorebirds (which generally prefer to breed away from high- tide roosts), cryptic species hidden in thick wetland vegetation (e.g. crakes, rails, wetland passerines, swamphen etc.), nomadic species (e.g. Oriental Plover) or inland species (e.g. Dotterels) are generally

not well sampled in these schemes (Kingsford et al., 2011; Clemens et al., 2012). For waterbird groups such as terns, high tide shorebird roosts do form important habitat. However, the timing of shorebird surveys is not necessarily ideal as roosting shorebirds move to roosts at high tides whereas terns move to roosts during the middle of the day which may not coincide with tides – also see Monitoring adequacy and trend reliability section. We therefore sought out additional waterbird abundance datasets which had been consistently collected within Australia. Initially this resulted in over 4 million waterbird records being brought together, but roughly half those records were omitted due to a lack of sufficient information for analyses (Table 3). It should be noted that while this listing represents a close to comprehensive inventory of systematic monitoring data for some groups (e.g. migratory shorebirds and waterfowl), for other groups there likely exists significant datasets not listed here, for example nest monitoring (Lacey and O’Brien 2015), the Beach-nesting Birds nest monitoring program (BirdLife Australia - https://portal.mybeachbird.com.au) among several. Please see section the Monitoring adequacy and trend reliability section for an elucidation of knowledge gaps by functional and habitat groupings and taxa.

Table 3 Datasets taken forward to analysis

Data provider Monitoring type Time Period Range # records East Australian Waterbird Eastern 1/3 of Australian 1983 - 2017 49,502 Surveys continent UNSW Murray Icon counts 2007 - 2013 MDB wetlands 468 Hydro Indicators counts 2010 - 2013 MDB wetlands 4,690 Wetland counts (Shorebirds Wetlands (340+) across Australia, BirdLife Australia / AWSG 2020 project, primarily 1971-2017 530,878 primarily coastal shorebirds)

Beach-nesting birds 1980-2014 Mainland SE Australia

Atlas 1981 - 2018 Locations throughout Australia 84,554 BirdLife Australia Wetland Counts (Murray Wetlands across inland NSW and 1994-1997 Darling Basin Database) Northern Vic Wetland counts (Victorian 1987-1992 100s of wetlands across VIC 13,497 Waterbird Database)

Wetlands in north and east DPIPWE Duck counts 1987-2017 14,156 Tasmania

Shorebird and waterbird Wetland complex (Western 1981-2016 11,476 counts Treatment Plant, VIC)

DELWP / ARI

Duck counts 2007-2017 Victorian wetlands 15,225

David Paton Wetland counts 2000-2017 Coorong 2,482

DENR Waterbird colony counts 1991-2011 Range of wetlands in coastal NT 1,039

Queensland wetlands, mostly QWSG Wetland counts 1993 - 2017 1,346,431 coastal 4 major wetland complexes in OEH Wetland counts 2008-2017 7,095 inland NSW

Australian Bird Index Phase 2 – Developing Waterbird Indices for National Reporting 21

North Central CMA Wetland counts 2009 - 2017 Northern Victoria 15,469

Wetland counts (salinity 1997 - 2013 Western Australia, selected lakes 2,430 study) DBCA

Wetland counts 2006 - 2015 Wetlands in SW Western Australia 7,148

COG Wetland counts 1998 - 2013 ACT wetlands 1,988 80-mile Beach, Roebuck Bay, Bush MYSMA Coastal shorebird counts 2004 - 2016 21,234 Pt Total 2,129,762

Data Preparation

Around half of the more than four million data points collated had sufficient metadata and at least five years of data to form legitimate time series and thus were able to be considered for analyses. Most wetlands or wetland complexes counted included records from multiple sites within an area. For those areas with complete coverage of all the sites within any wetland complex across time, records were summed to give a total number, and these areas were given the site name of the larger area. Areas where this was done included the East Australian Waterbird Survey (EAWS), and smaller local wetlands such as Corner Inlet, Western Port, and the Coorong. The time series for most areas, however, had varying number of sites visited within each area over time. Data from these less consistently visited sites were not aggregated.

Missing values from any species count at any site were coded as zero if that species had been recorded at least once at that site based on the assumption that each survey recorded all species present. This was not done for data sets such as those from the BirdLife Atlas and Atlas of Living Australia if it was not clear that all species present were counted. Due to the extreme variation present in most waterbird data, temporal missing values were not interpolated in years where no survey was conducted. Such interpolations are sometimes calculated in other index methods (Gregory et al. 2008).

A number of additional temporal variables were created for each record. First, the year and month were both identified as two new variables. To avoid having the year variable splitting a single breeding or non-breeding season into two, records collected in September, October, November or December were assigned to the following calendar year in a variable termed the ‘seasonal year’. For example, October 1980 was coded as 1981. All data was pooled by year as changes in abundance from month to month are more likely due to changes in detection probability than actual changes in population. Then 1980 (the year when many time series started) was subtracted from each seasonal year. A variable termed ‘year transformed’ was then derived by subtracting the mean ‘seasonal year’ from the ‘seasonal year’ of each record resulting in intercepts approximately centred within each time series. For migratory shorebirds, data were excluded if they were collected outside the period when most migratory species are in Australia (October through to March). For Double-banded Plover, a migratory shorebird species which visits Australia in the winter, data outside the period of May through to August was removed. The number of years of data in the time series for each area and species was also added to each record.

Australian Bird Index Phase 2 – Developing Waterbird Indices for National Reporting 22

Figure 1. Number of counts per year in all available waterbird data.

Each record’s latitude and longitude was used to extract the spatial areas related to each record. All records were assigned to one of Australia’s 13 drainage basins (Geoscience Australia), and to one of the smaller ‘river regions’ (Geoscience Australia) within Australia. Points that fell beyond Australia’s shoreline were assigned the nearest drainage basin and river region. Records were also assigned the Ramsar name (Australian Department of the Environment), or Key Biodiversity Area name (BirdLife Australia), if the point location fell within either of these kinds of areas. Records were also assigned the “Shorebird Area” (Shorebirds 2020, BirdLife Australia) they fell within, and locations that were outside mapped shorebird areas were assigned to the ‘River Region’ (i.e. River Basin, Geoscience Australia) the point fell within as a proxy grouping variable. A shorebird area is defined as the boundary around the wetland or group of wetlands used by a local population of migratory shorebirds (Clemens et al., 2014), and these areas likely capture separate populations of some other waterbird populations as well. A ‘River Region’ is a sub-catchment of Australia’s larger drainage divisions. While many local waterbird populations use larger areas than those defined by migratory shorebirds, they also represent the scale at which much of the available data is collected. Data from the EAWS is collected at a larger scale than any of these spatial areas, so EAWS was entered as the area name for all area variables. Records within Australia that occurred greater than 1km from the coastline were classified as inland records, and all other records were classified as coastal.

Additional variables included source, count type (i.e. aerial or ground), the max, median and mean counts for each species at each site over time, and the number of sites within each larger area. Additional filtering and aggregation of data was conducted for different analyses, and all data collation, formatting, aggregation and spatial variable assignments were conducted in R software (Zeileis & Grothendieck, 2005; Venables, 2013; Wickham & Francois, 2014; R Development Core Team, 2015; Bivand et al., 2017).

Recent trends - short (2005 to 2017) and medium (1997 to 2017) term

Many methods were attempted in this project to identify trends over 13 and 21 years in the available waterbird data (Appendix A). The chosen method was selected because results were most consistent across sub-sets of data in sensitivity analyses (Appendix A), model assumptions were broadly met, and largely agreed with previously reported population trends (Kingsford & Porter, 2009b; Clemens et al., 2016; Studds et al., 2017).

Australian Bird Index Phase 2 – Developing Waterbird Indices for National Reporting 23

The prepared data (see above) was first filtered to only include records between the periods of either 1997 to 2017, or 2005 to 2017. The resulting data had a site name for each count, which was often nested within a larger area (Shorebird area, or River Region). Within those two remaining datasets, the maximum annual count per site (seasonal year as defined above) was used for each species. Data for each species and site was then removed if there was not an average of more than two individuals and not a maximum count of at least 10 individuals recorded at that site for either of the two periods being analysed. This step prevented sites with very few birds having more influence on overall results than the percentage of population present would warrant. This step also reduced zero inflation. Admittedly, this step removed some finely dispersed species from consideration in these analyses, but some of these species are more represented in ATLAS presence data which were not considered here, and would require a different approach to analyse. EAWS data included total annual counts for the entire group of transects surveyed. These systematic repeated surveys were viewed as representative of changes in abundance in the areas between the transects being surveyed, an area approximately seven times larger than the area surveyed. In order to appropriately weight these data, all annual EAWS counts were multiplied by seven. Sites were then removed if there was not a count in each year.

For each species the annual arithmetic average was then taken across all sites where the species was recorded. Individual species trends were calculated from these averages. For each functional group the annual geometric mean was then taken across all species’ annual averages within each functional group. Finally, this averaging process for species and functional groups was repeated for all records within one kilometre of the coast, all inland records and records collected within each of 13 drainage divisions (Figure 2).

Australian Bird Index Phase 2 – Developing Waterbird Indices for National Reporting 24

Figure 2. Drainage divisions of Australia.

This data processing resulted in annual indices of abundance for each species and functional group for both short and medium lengths of time at national, coastal or inland, and drainage division spatial scales. A generalised additive model (GAM) was then applied on each of these individual time series (Eqn. 1).

(Eqn. 1) Yi = α + f (Xi ) +εi where εi ∼ N(0,σ2)

The term f (Xi ) equates to a penalised cubic spline regression term where the optimal amount of smoothing is determined through cross-validation. Implemented in R software using the MGCV package these splines can result in a straight line if that fit is optimal (Wood, 2004; Wood, 2006a; Zuur et al., 2009; R Development Core Team, 2015). The maximum number of knots in these models was set to 5 for medium-term data, and 4 for short-term data. These limits were set to avoid over-fitting variation which was assumed to be too great to represent actual population changes in data spanning two to four years. Residual plots were checked visually and models that did not converge are not presented (Appendix A).

Australian Bird Index Phase 2 – Developing Waterbird Indices for National Reporting 25

Long-term (1983-2017) trends

For the 53 species well represented in EAWS data, long-term trends were depicted using a GAM (as above) applied to the total annual EAWS counts collected between seasonal years 1984 – 2018 (October 1983 – October 2017). The only change to the above method was that a maximum number of knots was not set, as fitted splines did not show unreasonable short-term fluctuations. There were eight other datasets with complete counts over this period, but they were all located in coastal southeast Australia with efforts targeting migratory shorebirds, and only three sites in Victoria had counts of waterbirds other than migratory shorebirds over that period. For this reason, we did not include these counts when looking to determine long-term trends in species covered by EAWS data. Trends for EAWS data reflect changes in the eastern 1/3 of the continent, but do not account for trends elsewhere, notably the north, northwest and to a lesser degree the southwest of Australia (Kingsford et al., 2011).

For another 26 waterbird species there are data going back to the early 1980s and 1970s at a few places, and with at least ten years of data from at least 30 shorebird areas. Most of these 26 waterbird species are migratory shorebirds, and in the north of the country there is far less data before the 1990s (Clemens et al., 2012). All but eight of the available data sets in the southeast of the country have varying amounts of missing data, with areas like the Coorong recording some large migratory shorebird counts in the early 1980s followed by decade-long gaps in counts before eventually all waterbirds were recorded regularly. Further, while most of the available data has been collected at coastal wetlands, some non-EAWS data is available from inland wetlands which often tends to show larger boom and bust cycles in counts, which are related to the dynamic changes in inland water availability. Finally, the number of surveys conducted each year varies from one area to another, as does the degree to which surveys cover the entire wetland being used by any waterbird species.

Previous efforts have tackled this highly heterogeneous data by using a log transformed, multi-level linear model (Clemens et al., 2016). This kind of approach works well with data collected at different spatial scales and automatically down-weights data from areas with high variation and low abundance. However, this method does not account for highly non-linear data, nor does it account for the negative- binomial distribution of some of the available count data. To overcome these issues a meta-analysis approach was taken where different models are fit to data from each area, and the best model for each area is then used to calculate a local trend and standard error which is used to calculate a global trend estimate. The resulting trends reflect the average rate of change observed in the available data at any of a minimum of 30 shorebird areas or River Regions with at least ten years of data. Alternative methods were also attempted (Appendix A).

The first step of this method required filtering out unsuitable data from the over 2 million collated records (see above). The maximum count at each site in each month was taken to reduce the impact of poorly timed and duplicate counts. Then shorebird areas were removed if they did not have at least ten years of data available, and sites within any shorebird area were removed if they did not have at least five years of data. Then site data was removed if the average number of that species at that site each year was not greater than two. Results were sensitive to inclusion of data below these thresholds.

For each species and each shorebird area a Simple (log-transformed +0.9) Linear Regression (SLR) was estimated, and data were pooled by year (seasonal year) and site if there were counts from more than

Australian Bird Index Phase 2 – Developing Waterbird Indices for National Reporting 26

one site or month in that area. This resulted in an estimated trend expressed roughly as the percent rate of change and a standard error for that estimate for each species and shorebird area.

Generalised least square regression was used for time series from areas that did not include data from multiple sites, which had a median count of at least ten and had more than 15 years of data. Previous work on shorebird trends in northeast Tasmania indicated that GLS models overcame linear regression assumption violations such as a lack of linearity, lack of independence or normality in the errors over time, and heteroscedasticity (Cooper et al., 2012). The first GLS model used for each time series included a residual correlation structure of an auto-regressive model of order 1 (AR1) term, and a weighting term which assumed the variation in residuals varied in each five-year interval. If the model converged with no errors, the parameters of slope, standard error of the slope and confidence intervals were reported. If the model did not converge, the GLS model was run again without the (AR1) term. If the second GLS model did not converge, GLS parameters from that species and wetland were not considered and the LSR estimates were used. These GLS methods are believed to account for the kinds of variation expected in shorebird count data, and therefore tend to produce trend estimates with smaller standard errors. However, waterbird data often includes large but legitimate outliers and when assuming variation in residuals varies in five year periods, the extreme change in a five-year period can mask the overall change in abundance leading to slope estimates that do not reflect the long-term trend. Therefore, if the 95% confidence intervals of GLS and LSR slope estimates did not overlap we used the LSR estimate.

Then to guard against the possibility that non-linear trends were significantly different than results from linear methods, a generalised additive model (GAM) was run on each time series (EQ 1) with no maximum number of knots. This model was run on each time series once with a Gaussian distribution and again with a negative-binomial distribution. An abundance of zeros was common in these waterbird data, and the specification of a negative binomial distribution is one method of dealing with zero- inflated data. If both models converged, parameters were estimated from the model with the lowest AIC value. If only one model converged, parameters were estimated from that model. The mean of the predicted values and their standard errors were then calculated from fitted values in the last five years and again in the first five years. The log of those means was then differenced and divided the number of years between the two means resulting in an estimated trend. Logged mean predicted standard errors in the two five-year periods were used to calculate confidence intervals. If the confidence intervals of the GAM trend estimate did not overlap the confidence interval of the linear method, then the estimated trend from the GAM was used while disregarding trends from selected linear methods.

For areas with sampling from multiple sites, both the number of individuals of a given species and their change in abundance over time can vary widely between sites. For this reason, linear mixed model regressions (LMM) were used to account for the variation between sites. Multilevel linear regression accounts for the spatial hierarchy in the data, it allows for time series that differ in lengths or have missing data, it accounts for data that varies in length of time-series and it inherently gives more weight to those time-series with larger abundances and less variation. These models include fixed effects for the area intercept and slope, as well as correlated random effects for intercepts and slopes that varied by site within an area (Eqn. 2).

Australian Bird Index Phase 2 – Developing Waterbird Indices for National Reporting 27

= (Eqn. 2) Yisa β0 + β1 Ts + (B0s + B1s Ts) + εisa

where:

Yis is count i (log +0.9) in site s, calculated for each area and each species separately;

Ts is a temporal predictor (the time of the count, measured in years from the midpoint of the recording years ‘year transformed’ for each site s);

β0, β1, are the fixed effect coefficients for temporal terms;

(B0s + B3s Ts) is a random effect term (B0s and B1s are correlated random perturbations to the fixed coefficients β0

and β1 respectively);

εis is the random error term at the individual observation level.

To guard against the possibility that non-linear trends were present in areas with data from multiple sites, a generalised additive mixed model (GAMM) was run on each time series for each area and each species which included more than 15 years of data and a median count of ten or more. All GAMM’s used a smooth for the entire area and smooths for sites represented as penalized regression terms (Eqn. 3). Two GAMMs were attempted for each time series, with one specifying a negative binomial distribution and one specifying a Gaussian distribution. If both models converged without errors, parameters were estimated from the model with the lowest AIC value. If only one model converged, parameters were estimated from that model. Then the last five and first five predicted values were averaged, as were the standard errors over those two periods. A log-transformed slope and average standard error were computed from those estimates, and the 95% confidence interval was calculated. If those confidence intervals did not overlap with the LMM confidence intervals above, the GAMM derived slope was used as the best slope estimate. The significance of the GAMM slope was assessed by testing whether the confidence intervals from the first five years overlapped with the confidence intervals of the last five years.

= (Eqn. 3) Yis β0 + f (T)+ (B0a + f ( Ts)) + εis

where:

Yis is count i (log +0.9) in site s, calculated for each area and each species separately;

Ts is a temporal predictor (the time of the count, measured in years ‘seasonal year’ for each site s);

β0, β1, are the fixed effect coefficients for temporal terms;

(B0s + B3s Ts) is a random effect term (B0s and B1s are correlated random perturbations to the fixed coefficients β0

and β1 respectively);

εis is the random error term at the individual observation level.

The above methods resulted in one estimate of slope and its standard error for each shorebird area or river region and each species. For areas with consistent sampling over time these parameters were estimated from either SLR, GLS, or GAM. For areas with varying numbers of sites visited over time

Australian Bird Index Phase 2 – Developing Waterbird Indices for National Reporting 28

parameters were estimated using SLR, LMM or GAMM. Each of these slope estimates was then assigned a precision weighting (Eqn. 4). Confidence intervals and standard errors of these weighted means were then calculated by boot-strapping the weighted mean 10000 times, resulting in an overall estimate of trend from the available data which were found to be significantly different from zero if confidence intervals did not span zero.

∑ (Eqn. 4). = ∑

where:

t is the weighted mean of each iteration t, for the Australia-wide long-term trend estimate; n is the number of shorebird areas that were included for each species; i is the index of summation equivalent to each shorebird area; t is the bootstrap iteration (out of 10000);

Xit is the estimated trend for each shorebird area at each iteration; and

Wit is . for each slope estimate from each area, and iteration.

All statistical analyses were completed within R software (R Development Core Team, 2015), using the ‘nlme’ R package for GLS analyses (Pinheiro et al., 2011), LMM analyses largely followed examples and code (Gelman et al., 2012; Kuznetsova et al., 2014; Bates et al., 2015), the ‘mgcv’ package for GAM and GAMM (Wood, 2003, 2004; Wood, 2006b), with statistical analyses often following examples (Zuur et al., 2009). Sensitivity analyses included running the above models with different subsets of data derived from different filtering. Alternative methods were also tested (Appendix A).

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Trends for individual areas

Trends were also reported for all individual areas for each species with sufficient data within the following areas: Ramsar Sites, Shorebird Areas, River Regions, and Key Biodiversity Areas (KBA). In some cases, the spatial area of these different areas is identical, in others it overlaps, but most are completely separate areas. To maximise reporting we include any area with at least 5 years of data, an average count of more than two individuals, and as with previous subsets of data take the maximum monthly count recorded at each site to remove poorly timed counts and duplicates. For each time series and each species, we apply a LM, a GAM and a LMM as specified above. The slope and its standard error are reported from the LM unless a GAM with pooled data results in significantly different slope, or is an area with multiple sites where the model converges using the LMM method.

Mapping and monitoring adequacy and trend reliability assessment

Maps

Several maps are presented (alongside trend graphs) for each species in Appendix F. The first map shows the overall distribution of available data (taken from the overall filtered dataset - see Table 3 Datasets taken forward to analysis in ‘Available Data for Analysis’). This map shows the relative intensity of species data points as estimated by Quartic Kernel Density Estimates (KDEs), weighted by the number of years of data per unique location (i.e. site) using a bandwidth of 0.75 decimal degrees (see Figure 3 below for examples). Because KDEs require point data, EAWS survey bands were clipped to species ranges (including vagrant/irregular and irruptive ranges) and the resultant transects then converted to points at half of the KDE bandwidth along each transect line. Each EAWS transect point was allocated 33 years as its Kernel Density Estimate weight resulting in a dataset that is comparable with ground survey data. Overlaying KDE sampling intensity layers are Extent of Occurrence (EOO) range polygons showing overall species ranges. Core species ranges are shown in orange while areas in which species irregularly occur are depicted in light orange (Figure 3).

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Figure 3. Examples of overall data intensity maps. Shading represents sampling intensity (as determined by Kernel Density Estimates) and polygons show the core and irregular ranges of species. Examples shown; Royal Spoonbill (left) and Red-necked Stint (right).

The other maps in Appendix F show overall species ranges (EOO range polygons as described above) against data coverage for each species that had usable trend estimates. The data used in each trend estimate is represented by red dots (denoting shorebird areas) and blue lines (denoting EAWS transects) which are shown against a background layer of drainage divisions shaded by the number of areas sampled per division (Figure 4).

Figure 4. Example trend data maps. Examples; Medium term trend data maps for Royal Spoonbill (left) and Red-necked Stint (right).

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To facilitate a rudimentary basis for evaluating the representativeness and adequacy of the data that make up trend estimates, trend data maps are presented alongside trend graphs in Appendix F and are arranged according to the template below (Figure 5).

Figure 5. Template for maps/graphs presented in Appendix F.

Monitoring adequacy and trend reliability assessment

Trend estimates are only ever as good as the data used in their formulation and trends presented here run the breadth of the reliability spectrum, from highly robust estimates derived from decades of structured monitoring across entire species ranges, to highly speculative estimates from unsuitable monitoring techniques or from very small proportions of a species spatial range. Because we have calculated and presented trends in bulk here – i.e. we have not filtered out trends based on any specific suitability criteria – it is absolutely crucial to consider, at minimum, a few key diagnostics relating to data structure (such as the maps described above) if spurious conclusions are to be avoided.

We encourage thorough evaluation of monitoring adequacy and trend reliability assessment on a case- by-case basis, however as a quick reference to trends presented here all national trends (for the short,

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medium and long term) were assessed and scored for monitoring adequacy and trend reliability by GE and RC. Trends for individual areas were not assessed. Assessment was largely qualitative but was conducted in relation to objective information as much as possible, and in all cases rationales are provided for scores (see Appendix G). The principal criteria used for assessments related to spatial representativeness of data (which usually varies hugely), but also included consideration of the appropriateness/effectiveness of monitoring techniques as they relate to species attributes such as size (particularly in relation to aerial surveys), crypsis, habitat preference and movement ecology (congregatory or dispersed) - see example Table 4 and Figure 6 for some examples.

An ordinal scale (0-5) was used to score monitoring adequacy and trend reliability in each of the time periods (i.e. long, medium or short term). A score of 0 was allocated where no trend could be produced or the data used in the trend was so marginal or unrepresentative, or monitoring methods used were fundamentally inappropriate, that the trend produced is almost certain to be spurious. A score of 5 represents very high confidence in a trend and little or no room for substantial improvement in monitoring. Scores in between the ends of this spectrum are relative and can be interpreted, broadly speaking, proportionally (i.e. 20% increments), with 5 indicating an 80-100% confidence that the trend produced is generally a true trend, a score of 4 indicating a 60-80% confidence and so on. Occasionally, monitoring is scored for subspecies. This was done for species that have subspecies of conservation significance or subspecies that are clearly regionally distinct (e.g. Eastern and Recherche Cape Barren Goose – see Appendix H for scientific names) given that monitoring efforts are hugely divergent across the continent (in some cases covering the range of one subspecies well but totally missing another). In these instances, the polytypic species is not included to avoid duplication (see Appendix G for full details).

Table 4 Example of monitoring adequacy and trend reliability assessment Short Medium Long Taxon term term term Short term score Medium term score name score score score rationale rationale Long term score rationale Data from SE Data from SE Australia, Australia and Perth Perth, and Top End including 2 of 9 areas Red Knot 4 3 2 including 4 of 9 areas of of international international significance. No data significance from northern Australia Majority of core range (eastern mainland Aust) covered by EAWS, however species often inhabits thick wetland vegetation and may not be EAWS + data from 2 EAWS + 1 ground- Dusky observed from Aerial surveys as 2 2 2 ground-based survey based survey location Moorhen evidenced by low counts locations in SE Qld in SE Qld (generally <50 birds total per aerial transect), compared with 500m area ground-based surveys which often entail counts of >50.

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Figure 6. Areas of international significance (denoted by stars; top) in relation to medium term (lower left) and short term analysis datasets (lower right) for Red Knot. In this case the medium- term trend is drawn from 2 of the 9 areas of international significance and the short-term trend from 4 of 9 internationally significant areas.

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Results

Index of population change by functional groups

Indices of population change by functional groups were only reported in the medium-term as data were too patchy to yield precise non-linear estimates when extended further back in time to the long- term, and short-term trends took the same shape as the medium-term trends despite having more data. It is also important to note that where group abundances are presented (Figure 7), these analyses are adequate to the extent that they include data of the overall range of species within that functional group.

Analyses indicate that many waterbird functional groups showed large and obvious increases following the 2011 floods (Figure 7). Unsurprisingly shorebirds, most of which rely on coastal habitats, did not show similar patterns of increasing abundance following the 2011 floods. Within each functional group there was often variation in apparent trends for individual species (Figures 8 and 9). Some species match the functional group trend very well, while other species are markedly different. We therefore report on individual trends in a separate section below.

Figure 7. Average abundance of individual waterbirds within functional groups observed over time (seasonal year) from the best available Australian data: A) group inclusive of all waterbirds which had sufficient data for trends to be calculated except migratory shorebirds; B) group inclusive of herbivores (i.e. Black Swan, moorhen, coot, and some ducks); C) group of large wading birds (i.e. egrets, herons, ibis and cranes); D) group including: ducks, small grebes, and jacana; ; E) group of piscivores or fish eaters (i.e. cormorants, Silver Gulls, and terns); F) shorebirds (both migratory and non-migratory).

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Figure 8. Average abundance of individual waterbirds within the piscivore functional group observed over time from the best available Australian data; A) geometric mean of average abundances of all piscivores; B) average abundance of Australian Pelicans; C) average abundance of Little Black Cormorant; D) geometric mean of the average abundance of species classified as gulls or terns; E) average abundance of Little Tern; F) average abundance of Crested Tern.

Figure 9. Average abundance of individual waterbirds within the shorebirds functional group observed over time from the best available Australian data; A) geometric mean of average abundances of all shorebird species with sufficient data for analysis; B) abundance of small waders (shorebirds) from the East Australian Waterbird Survey (EAWS); C) abundance of Red-necked Avocet; D) average abundance of Masked Lapwing observed in Australia; E) average abundance of Black-winged Stilt observed in available Australian data between 1997 and 2017; F) average abundance of Red-necked Stint observed in Australia between 1997 and 2017.

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Trends for individual species

Species trends for all of Australia along with maps of overall data intensity and data used in indices are presented in Appendix F. Trends within drainage divisions are reported in Appendix D, and trends from individual shorebird areas, river regions, Ramsar sites, and KBA’s are reported in Appendix E.

Long-term species trends

Long-term trends reported here largely reflect trends reported previously (Kingsford and Porter 2009, Clemens, Rogers et al. 2016, Porter, Kingsford et al. 2018). This was not surprising as there is very little abundance data that extends back to the 1980s aside from the EAWS data and Shorebirds 2020 data. For waterbirds there are a few sites in southeast Australia with consistent data going back to the 1980s but we simply reported EAWS results (Figure 10, Appendix C) to avoid weighting trends geographically. Twenty-seven species of waterbird, not including migratory shorebirds, and two species groups, showed evidence of significant long-term declines in EAWS data (Table 5), while 26 species showed no trend, three showed increasing trends, and two species groups showed no trend (Table 6).

Widespread trends in species not covered in EAWS data or Shorebirds 2020 data were available for a handful of species. Declines in available data with ten or more years of counts were evident in Black- fronted Dotterel and Crested Tern, while no trend was evident in Sooty Oystercatcher, Little Tern, Australian Pied Oystercatcher, Hoary-headed Grebe, and Red-capped Plover (Table 5 and Table 6). However, having sufficient data to calculate a trend does not necessarily indicate that the trend (or lack thereof) is accurate. Indeed, some of the trends reported reflect data included here which is, in some cases, poorly representative of a species’ distribution range over space and time (see Monitoring adequacy section), or are drawn from surveys which do not employ appropriate methods or sample appropriate locations within a species range to detect change. In fact, no Australian waterbird species are censused annually across their entire range so it is critical to consider results reported here in terms of representativeness and appropriateness of the available data. For instance, no significant change was detected here for Hooded Plovers in the short-term, and data was insufficient to calculate Australia-wide longer-term trends. However, the Vulnerable (IUCN and EPBC) Eastern Hooded Plover (Thinornis cucullatus cucullatus) is known to be in long-term decline, having declined by well over 50% on the mainland between 1984 and 2004 (biennial count surveys analysis, in prep). In this case the lack of trend detected here likely results from several factors. Firstly, the entire species (including the non- threatened Western Hooded Plover T. c. tregellasi) was included in this species level analysis. Secondly, wader studies groups conduct counts primarily from high-tide roosts which Hooded Plovers actively avoid during their breeding season (when surveys wader studies surveys are conducted). Thirdly, comprehensive, dedicated (biennial) surveys for the Eastern Hooded Plover are only now being collated and analysed (in prep) and so data were not immediately available for this project. Finally monitoring for this species is conducted biennially (for logistical reasons) and so would be filtered out of short and medium term national analyses here by criteria employed here which require samples in every year or the criteria for a mean count of >10 individuals per site, which would often not be met given the species

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highly territorial and dispersed nature (Weston et al. 2009). Situations such as this are in the minority, however careful consideration of results is required in some instances.

As the number of shorebird areas being counted has grown over the decades, medium and short-term trends are often somewhat more geographically representative with consistently collected annual data available in medium and still more in short-term trends. However, survey methodologies and sampling locations, even within species ranges, must always be considered in relation to species ecological/life history traits when interpreting results (see Appendices F and G).

Trends of decline in Australian White Ibis, Masked Lapwing and Silver Gull were obvious in the available data, which indicates significant declines at eastern Australia’s inland wetlands. However, these three species use habitats that would not be well represented in EAWS data; i.e. upland habitats, sub-urban habitats, and some coastal regions. Australian White Ibis at individual wetlands throughout Australia are largely not showing obvious trends with equal numbers of areas where counts are increasing or decreasing (Appendix E). While data were not available from many upland and sub-urban habitats, available trends from individual areas for Silver Gull and Masked Lapwing indicate far more significant decreases than increases (Appendix E). Nonetheless, anecdotal evidence, and explorations with some of the noisier available data, does suggest it is possible that declines in the areas being sampled may have been offset somewhat by increases in poorly sampled habitats. Declines were significant in all three periods for Australian White Ibis and Shelduck, while declines continued in just the long and medium-term for Australasian Shoveler and Musk Duck.

Five species reported to have no statistically significant trend had some evidence of trends (Tables 5 and 6). Great Crested Grebe and Straw-necked Ibis had declines in average abundance identified (Appendix C), but large outliers late in the time series result in no statistically significant trend identified. Declines would be evident if outliers had been removed. Caspian Tern did not show statistically significant declines, but high variation and outliers early in the time series suggest they may be present if future analyses can account for more of the variation. Sooty Oystercatcher and Pied Oystercatcher were also found to have no trend in these analyses, but had been found to be increasing in previous work (Clemens, Rogers et al. 2016).

Thirteen migratory shorebirds were showing evidence of long-term decline, while six showed no evidence of a widespread trend in data over more than ten years of counts (Table 7). These results were largely consistent with previous efforts to look at trends inclusive of data that went back four decades in some cases (Clemens, Rogers et al. 2016). There were also several dissimilarities with previously reported trends. Marsh Sandpiper was not found to be declining in previous estimates (Clemens, Rogers et al. 2016) but was found to be declining in these analyses, while Pacific Golden Plover was found to be declining previously (Clemens, Rogers et al. 2016) but not in these analyses. It is possible that these differences relate to having a few more years of data, but the methodologies used also have different strengths, and one may be better suited to the available data than the other. The mixed- linear modelling approach taken previously has the advantage that it does a more complete job at accounting for hierarchical correlations, while also accounting for variation at sites and their larger shorebird areas. The meta-analyses approaches used here have the advantage of capturing non-linear trends which are much more common in other waterbird data. Great Knot trends were also similar here and in previous analyses (Clemens, Rogers et al. 2016), but this method missed the large declines evident in less patchy data (Studds, Kendall et al. 2017). This is due in part to the large variation present

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in Great Knot data from places where they are most abundant, such as northwest Western Australia, which are best dealt with if we account for variation related to detection probability (Studds, Kendall et al. 2017). We note, however, that even simpler methods pick up significant declines in Great Knot over the medium-term (21 years; Table 7) as well as Red Knot in the long and medium term. Shorebird sampling is heterogeneous in space and time with a tendency to have more data available in recent years from more geographic areas, and the distribution of shorebirds is also heterogeneous across Australia with species more abundant in different regions. As a result, any analysis which attempts to use patchier data which covers wider geographic areas and includes longer time series also risks results being sensitive to the sub-set of data used. However, if we exclude data from areas such as the Coorong which includes counts in the early 1980s but then has an over a decade long gap in data, we would not include the loss of tens of thousands of Red-necked Stint and Sharp-tailed Sandpiper. Excluding a number of such sites due to data patchiness would fail to capture long-term declines in species such as Red-necked Stint and Sharp-tailed Sandpiper. We did find results to be sensitive to both the sub-set of data used, and the method used (Appendix A). For that reason, trend coefficients are not thought to be precise, and we avoid reporting them here aside from graphically (Figures 10 & 11). Most species found to be either increasing or decreasing significantly were found to be significant in most methods, and sub-sets of data used. The following species, however, also varied in whether they were found to have no trend or to be decreasing significantly; Red Knot, Great Knot, Pacific Golden Plover, and Double- banded Plover. Examination of local area trends points to the locations likely driving differences in overall results (Appendix E).

An additional 21 species had sufficient data to calculate medium or short-term trends, but did not have enough for long-term trends (Table 8). We were able to calculate trends averaged over multiple areas for 96 species and four species groups. This included Black-faced Cormorant, Caspian Tern, Common Tern, Crested Tern, Lesser Crested Tern, Little Tern, and Fairy Tern; however, see Appendices F and G as some of these trends are drawn from very marginal data. There were 34 species that we targeted initially which we did not find sufficient data to even calculate a trend (of any quality/reliability) including: Australasian Bittern, Australian Little Bittern, Australian Painted Snipe, Australian Pratincole, Australian Reed-Warbler, Australian Spotted Crake, Baillon's Crake, Beach Stone-curlew, Black Bittern, Buff-banded Rail, Chestnut Rail, Eastern Reef Egret, Golden-headed Cisticola, Great-billed Heron, Green Pygmy-goose, Inland Dotterel, Lewin's Rail, Little Grassbird, Oriental Pratincole, Pale-vented Bush-hen, Pied Heron, Red-necked Crake, Sarus Crane, Spotless Crake, Spotted Whistling-Duck, Striated Heron, Swamp Harrier, Tasmanian Native-hen, Tawny Grassbird, Wandering Tattler, White-breasted Waterhen, White-browed Crake, and Wood Sandpiper (see Appendix G).

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Figure 10. Long-term trends evident from East Australian Waterbird Survey data, the y-axis is roughly equivalent to the percent change per year.

Figure 11. Meta-analysis results for species not included in EAWS data, estimated from data from at least 30 areas for each species with at least ten years of data and a mean count above two for each species, the y-axis is roughly equivalent to the percent change per year.

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Table 5. Waterbirds, not including migratory shorebirds, showing wide-spread long-term population declines in Australia, with medium term trends (last 21 years), and short-term trajectories (last 5 years) also reported: Declining = 95% confidence intervals of slope were negative and did not span zero, no trend = insignificant results, increasing = 95% confidence intervals of slope were positive and did not span zero, trajectory in last 5 years was judged visually from 13 year smoothed averaged.

Species Long-term Trend Medium-term Trend Short-term trajectory Australasian Shoveler Declining Declining flat Musk Duck Declining Declining flat Black-tailed Native-hen Declining No trend down Blue-billed Duck Declining No trend flat Australian Shelduck Declining Declining down Purple Swamphen Declining No trend down Radjah Shelduck Declining No trend down Small Waders Declining No trend down Chestnut Teal Declining No trend down Small Grebes Declining No trend down slightly Whiskered Tern Declining No trend down White-faced Heron Declining No trend down Freckled Duck Declining No trend down Glossy Ibis Declining No trend down Australian Wood Duck Declining No trend down White-necked Heron Declining No trend down Red-necked Avocet Declining No trend flat Yellow-billed Spoonbill Declining No trend down Pacific Black Duck Declining No trend down Eastern Great Egret Declining No trend down Grey Teal Declining No trend down Black Swan Declining No trend down Little Pied Cormorant Declining No trend down Crested Tern Declining Declining up Black-winged Stilt Declining No trend down Black-fronted Dotterel Declining No trend flat Australian White Ibis Declining* Declining down Masked Lapwing Declining* No trend down Silver Gull Declining* No trend flat * = Evidence of a different trend (see results)

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Table 6. Waterbirds, not including migratory shorebirds, showing no evidence of long-term population declines in Australia, with medium term trends (last 21 years), and short-term trajectories (last 5 years) also reported: Declining = 95% confidence intervals of slope were negative and did not span zero, no trend = insignificant results, Increasing = 95% confidence intervals of slope were positive and did not span zero, trajectory in last 5 years was judged visually from 13 year smoothed averaged.

Species Long-term Trend Medium-term Trend Short-term trajectory Straw-necked Ibis No trend* No trend flat Hoary Headed-Grebe No trend No trend flat Caspian Tern No trend* No trend flat Australian Pelican No trend No trend down Banded Stilt No trend No trend down Plumed Whistling-duck No trend No trend down Banded Lapwing No trend No trend down Eurasian Coot No trend No trend down Magpie Goose No trend No trend flat Pink-eared Duck No trend No trend down Hardhead No trend No trend down Large Waders No trend No trend flat Great Cormorant No trend No trend down Dusky Moorhen No trend No trend up Australasian Darter No trend No trend down Black-necked Stork No trend No trend down Brolga No trend No trend flat Royal Spoonbill No trend No trend down slightly Gull-billed Tern No trend Increasing up Egrets No trend No trend down Little Black Cormorant No trend No trend down Nankeen Night Heron No trend No trend flat Pied Cormorant No trend No trend down Great Crested Grebe No trend* No trend flat Australian Pied Oystercatcher No trend* No trend down Sooty Oystercatcher No trend * No trend flat Little Tern No trend Declining up Red-capped Plover No trend Declining down Cotton Pygmy-goose Increasing No trend down Wandering Whistling-duck Increasing No trend flat Cape Barren Goose Increasing Increasing up * = Evidence of a different trend (see results)

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Table 7. Evidence of long-term population declines in migratory shorebirds in Australia, with medium term trends (last 21 years), and short-term trajectories (last 5 years) also reported: Declining = 95% confidence intervals of slope were negative and did not span zero, No trend = insignificant results, Increasing = 95% confidence intervals of slope were positive and did not span zero, trajectory in last 5 years was judged visually from 13 year smoothed averaged.

Species Long-term Trend Medium-term Trend Short-term trajectory Sharp-tailed Sandpiper Declining No trend flat Lesser Sand Plover Declining Declining flat Grey Plover Declining Declining down Curlew Sandpiper Declining Declining down Marsh Sandpiper Declining* Declining down Red-necked Stint Declining No trend down Ruddy Turnstone Declining Declining down Eastern Curlew Declining Declining down Black-tailed Godwit Declining Declining down Double-banded Plover Declining Increasing flat Common Greenshank Declining Declining flat Bar-tailed Godwit Declining Declining down Red Knot Declining Declining up Great Knot No trend * Declining flat Pacific Golden Plover No trend * No trend down slightly Grey-tailed Tattler No trend No trend flat Greater Sand Plover No trend Declining flat Whimbrel No trend No trend up slightly Sanderling No trend No trend down * = Evidence of a different trend (see results)

Short and medium-term species trends

Of the 70 waterbirds or waterbird groups (not including migratory shorebirds) with enough data to test for medium-term trends, seven were decreasing, 5 were increasing and the remaining 58 species did not have significant medium-term trends. This 10% of species tested showing declines contrasts sharply with the 48% of species of waterbirds showing long-term declines. It also contrasts with the 57% of species showing downward trajectories in the last five years, with another 4% showing slightly downward trajectories, 27% showing fairly flat trajectories, 10% showing upward trajectories, and another 2% showing slightly upward trajectories (Tables 5 to 7). While some of these differences may relate slightly to the increasing number of areas with data as we go from long-term trends to short- term trends, the pattern is consistent with the patterns in EAWS data (Porter, Kingsford et al. 2018). Those patterns in count data often fall dramatically in the mid-1980s and continue to fall until the peak of the millennium drought. Counts then often tend to rise again following the 2011 floods, but have fallen sharply again as drought has taken hold in much of eastern Australia (Appendix C; Figure 7). As counts around the 2011 flood were usually lower than they were in the early 1980s a long-term decline is still evident despite the boom in numbers around 2011. Medium-term trends which do not extend to those high counts in the 1980s tend not to show a trend, while the trajectory over the last five years is generally downward as a drought continues. Long-term declines in waterbirds have been shown to be present in the Murray-Darling Basin (MDB) and absent from the less developed Eyre Basin

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(Kingsford, Bino et al. 2017), so it is possible that our understanding of waterbird trends are driven most by changes in the huge MDB. However, as the number of areas sampled outside the MDB have increased patterns in average abundance have largely continue to match the EAWS data. The lack of negative correlation between coastal and inland data further suggests that the trends witnessed in the MDB are somewhat mirrored in much of eastern Australia. It is not clear, however, how the large waterbird populations in the north and northwest of the continent might be faring (Kingsford, Porter et al. 2011).

Twelve of 20 migratory shorebirds with enough data to test were showing medium-term declines, and one was increasing. Eleven of 25 migratory shorebirds were showing downward trajectories in the last five years, with five showing upward trajectories. Eight migratory shorebirds were declining in all three periods, while encouragingly Double-banded Plover were increasing in the medium term, and Red Knot were showing an upward trajectory in the last five years.

Table 8. Waterbirds with insufficient data to assess long-term population declines in Australia, with medium term trends (last 21 years), and short-term trajectories (last 5 years) also reported: Declining = 95% confidence intervals of slope were negative and did not span zero, No trend = insignificant results, Increasing = 95% confidence intervals of slope were positive and did not span zero, trajectory in last 5 years was judged visually from 13 year smoothed averaged.

Species Long-term Trend Medium-term Trend Short-term trajectory Australasian Gannet NA NA flat Australasian Grebe NA Increasing down Black-faced Cormorant NA NA up Bush Stone-curlew NA NA down Cattle Egret NA Increasing down Comb-crested Jacana NA NA down Common Tern NA No trend down Fairy Tern NA NA up slightly Intermediate Egret NA No trend down Latham’s Snipe NA No trend down Lesser Crested Tern NA Increasing down Little Egret NA No trend flat Pacific Gull NA No trend flat Red-kneed Dotterel NA No trend down White-winged Black Tern NA No trend down Asian Dowitcher NA NA up Broad-billed Sandpiper NA NA up Common Sandpiper NA NA up Little Curlew NA NA down Oriental Plover NA NA flat Terek Sandpiper NA No trend slightly down

The differences in trends between inland wetlands and coastal habitats

We expected to find large and obvious negative correlation in waterbird data between coastal and inland wetlands due to reported evidence of birds movement to and from the coast (Alcorn, Alcorn et al. 1994, Wen, Saintilan et al. 2016). Available data, however, did not show much evidence of negative correlation (mean r = 0.16; range: -0.23 to 0.76). Trends were often similar between coastal data and

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inland areas, with only occasional evidence of trends going in opposite directions over long or short periods (Figure x6, Appendix B). While available data are patchy, there was no evidence to suggest coastal and inland trends were notably different in most species.

The following species occur primarily along the coast so are not included in comparisons: Australian Pied Oystercatcher, Bar-tailed Godwit, Black-tailed Godwit, Common Greenshank, Common Sandpiper, Common Tern, Crested Tern, Curlew Sandpiper, Double-banded Plover, Eastern Curlew, Fairy Tern, Great Knot, Greater Sand Plover, Grey Plover, Grey-tailed Tattler, Hooded Plover, Latham’s Snipe, Lesser Crested Tern, Lesser Sand Plover, Little Tern, Pacific Golden Plover, Pacific Gull, Red Knot, Ruddy Turnstone, Sanderling, Sooty Oystercatcher, Terek Sandpiper, Whimbrel, and White-winged Black Tern. Black-tailed and Tasmanian Native-hen were not included in comparisons as they occur in primarily inland locations.

Coastal wetlands were defined in this review as any area within one kilometre off the coast, or tidal wetland. Many species are found in coastal and inland wetlands but only had sufficient data from one wetland type. The following species had data from the coast, but insufficient data from inland wetlands: Australasian Grebe, Black-fronted Dotterel, Buff-banded Rail, Cattle Egret, Eastern Great Egret, Hoary- headed Grebe, Intermediate Egret, Little Egret, Marsh Sandpiper, and Red-kneed Dotterel. Species which would occur at wetlands within one kilometre from the coast, but only had sufficient data from inland data: Cotton Pygmy-goose, Great Crested Grebe, Freckled Duck, Pink-eared Duck, Wandering Whistling-duck, White-necked Heron, and Yellow-billed Spoonbill.

Figure 12. Comparisons of average abundance observed between coastal and inland wetlands for three species: average abundances of Australasian Shoveler show different trends A) at inland wetlands and D) coastal wetlands; average abundance of Pacific Black Duck show similar trends despite some negative correlation from year to year B) at inland wetlands and E) coastal wetlands; average abundance of Musk Duck show declines in both areas with little negative correlation at C) inland wetlands and F) coastal wetlands.

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Monitoring adequacy and trend reliability A total of 140 taxa (species and subspecies where appropriate) were assessed in over the three monitoring periods (short, medium and long term). Of the 420 assessments conducted, monitoring adequacy and trend reliability scores ranged from 0 to 4 – no species was scored at 5 for any period (see “Monitoring adequacy and trend reliability assessment” section in methods above for definition of categories). In general scores increased in shorter time periods due to there being a greater spatial representation of species overall ranges in available data (and more time series overall) and birds with a northern or western Australian distributions were generally very poorly represented in waterbird monitoring data (see Appendix F for details).

When grouped into functional or habitat categories stark differences in monitoring adequacy and trend reliability emerge. Comparing congregatory versus non-congregatory species (i.e. those that do not usually congregate in large numbers), particularly glaring differences emerges. The vast majority of the 50 non-congregatory taxa assessed here (72-84%) had insufficient abundance data to use with the methods employed here in the short to long term (i.e. scored 0) and the average score for non- congregatory waterbirds was <1 (Figure 13). No non-congregatory species were rated above a score of 2 in the long or medium term datasets and only Southern Masked Lapwing and Common Greenshank (both of which do actually form flocks at times) were the only two non-highly congregatory species to be scored as high as 3 (both in the short term). In contrast, few congregatory waterbirds were bereft of any trend information with under 20% having a score of 0 in the short and medium term and only 24% having a 0 score in the long term. The average score for congregatory waterbirds was 2.17.

Figure 13. Monitoring adequacy and trend reliability scores for congregatory and non- congregatory waterbirds. Columns represent the proportion of taxa within the group scored at a particular level (0-5).

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Breaking these patterns down further into functional and habitat groupings revealed more clear patterns. Open water species (fresh and saline), Mid-depth fresh water species and Shallow water species, almost all of which are congregatory, had relatively high monitoring adequacy and trend reliability scores (Figure 14). Very few of these taxa scored 0, especially in the short term and these groups featured the highest scores on average with open fresh waters species averaging 2.39, open fresh/saline waters species averaging 2.43 and shallow water species averaging 2.17. In fact, at least half of the birds in each of these 3 groups scored 3 or 4 in the short-term (Figure 14).

Migratory shoreline birds also scored relatively high with 10 species rated at 4 in the short term (the most species at this score of any group) and >half scoring above 3 in the short term. Only one migratory shorebird (Curlew Sandpiper) scored 4 in the medium or long term and, as with all groups, scores for migratory shoreline birds decreased significantly for the medium and long term with <20% scoring 3 or above in the long term (Figure 14). However, it is noteworthy that while none of the 21 migratory shorebird species that are vagrant to Australia were considered in this report at all, there are several migratory shoreline birds which regularly migrate to Australia such as Broad-billed Sandpiper, Long- toed Stint, Pintail Snipe, Swinhoe's Snipe, Common Redshank, and Red-necked Phalarope, which were too rare in Australia to be effectively monitored, and hence score 0. If these species were not included in the group average for migratory shoreline species that group score would average among the highest group scores (along with open fresh and saline waters and shallow waters species).

In contrast to the relatively robust trends available for migratory shoreline waterbirds, no resident shoreline birds (i.e. those which breed in Australia) or marine waterbirds (terns) rated more than 2 for any time period. In the medium and long term all 7 marine species were scored at 0 or 1, half of the nation’s 12 resident shoreline birds scored 0 in the short and medium term and 8 of the 12 scored 0 in the long term (Figure 14).

The lowest waterbird monitoring adequacy and trend reliability scores were thick wetland vegetation birds. Comprising 8 crakes and rails, 4 wetland passerines along with bitterns and Latham’s Snipe, all of these species scored 0 in the long term and only Latham’s Snipe scored 1 in the medium/short term (Figure 14).

Overall, it is notable how strongly the monitoring adequacy and trend reliability scores declined from short term to medium and long term in particular. Only 6 species scored 4 in the long term and only 7 scored 4 in the medium term – compared with 26 species being assessed at 4 in the short term (Figure 14).

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Figure 14. Monitoring adequacy scores by habitat groups. Columns represent the proportion of taxa within the group scored at a particular level (0-5).

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Figure 15. Monitoring adequacy scores by functional groups. Columns represent the proportion of taxa within the group scored at a particular level (0-5).

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Trends from smaller areas

In the above analyses outliers, missing data and negative binomial distributions resulted in trends from one area that were occasionally incorrect. For example, when using a negative binomial distribution, occasionally trend estimates showed increases due the high weighting of changes in small numbers while not accounting for the handful of outliers that clearly showed a long-term decline in the total population. These incorrect trends only occurred occasionally and did not have an impact on the global trend estimates, but here we wanted to avoid reporting such instances in trends for individual areas. Therefore, only LM, LMM or GAM were used depending on the available data. While some of the apparent trends could be spurious in these automatically generated results, it provides a quick way to explore available data at a variety of scales and allows reporting from areas that do not have trends reported otherwise. Further, this scale of reporting allows for rough comparisons of where in Australia species appear to be doing better or worse (Appendix E). We caution that these results should be treated as exploratory, with a chance that trends are not precise due to poorly fit values, outliers, or are different from one another because of the quality of sampling rather than actual changes in abundance. In some cases, these results point to obvious or surprising results that are useful to highlight. For example, it is clear that the area that has lost the most Sharp-tailed Sandpipers over time is the Coorong, or that species like Red-necked Avocet may be declining in areas due to changing local conditions, or that there are places where species are clearly trending in a different direction in available data (Figure 16). It is worth noting, for example, that very large counts of the critically endangered Curlew Sandpiper have been recorded at Lake MacLeod, and numbers appear to have increased over a short time period.

Figure 16. Examples of species trends from individual areas: A) demonstrates that the Coorong is likely the place in Australia where the most Sharp-tailed Sandpipers have disappeared, B) suggested increases in the Critically endangered Eastern Curlew near Townsville, C) indicates a large population that is increasing of the critically endangered Curlew Sandpiper, D) apparent increases in the endangered Great Knot in Cairns, E) apparent increases in the widely declining Blue-billed Duck, and F) declines in Red-necked Avocet that might point to changing local conditions.

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Discussion

Our review of available Australian waterbird data shows large drops in waterbird and migratory shorebird abundance since the 1980s, both of which have been reported previously (Kingsford and Porter 2009, Clemens, Rogers et al. 2016). Our review also shows the continuing decline of many migratory shorebirds in recent years, and the low number of other waterbirds being observed since the boom in many species populations observed following the 2011 floods in eastern Australia; things which have also been reported previously (Studds, Kendall et al. 2017, Porter, Kingsford et al. 2018). These large-scale declines point toward the need to review the conservation status of species to flag those that are at increasing risk of extinction. These declines also flag the need to identify underlying causes in order to apply management actions at a variety of scales to restore numbers. Given the growing threat that climate change will pose to Australia’s wetlands (Kingsford 2011, Finlayson 2013, Finlayson, Davis et al. 2013, Junk, An et al. 2013), as well as proposed development in the north (Bino, Kingsford et al. 2016), monitoring data will also be increasingly relied on to understand growing impacts and the success or failures of attempted mitigation strategies (e.g. environmental watering). Waterbirds that form large flocks are fortunate in that existing conservation frameworks such as those outlined in the Ramsar convention focus on maintaining waterbird abundance (Convention on Wetlands 1999). If more of these species become recognised as threatened, the targets shift to avoidance of decline and ultimately extinction. Ironically, if declines were halted in some of these species, success might resemble species populations persisting at 80% lower numbers than they had been at historically (cf early 1980s). Quite aside from the need to conserve biodiversity and prevent extinctions, one could argue there is a societal responsibility to aim for restoration of historic population levels for future generations to enjoy and experience. Such targets, however, would require radically different land use patterns and management actions, and more comprehensive monitoring to ensure such targets are met. We would encourage a dialogue on what waterbird targets for each species should look like.

Fortunately, there are a wide variety of well-established management actions that can be taken to help maintain or restore waterbird populations (Lawler 1996, Anderson and Smith 2000, Erwin and Beck 2007, Purnell, Peter et al. 2012), as a well as a growing understanding of wetland conditions, and how we might restore wetlands, including Australia’s impacted floodplain ecosystems (Puckridge, Sheldon et al. 1998, Pittock and Finlayson 2011, Bino, Kingsford et al. 2015, Bino, Kingsford et al. 2016). A recent example is of BirdLife Australia and the Victorian Environmental Waterholder being involved in devising and applying monitoring strategies in line with active environmental watering at Lake Cullen in northern Victoria. Given the growing threats to wetlands and waterbirds, an expansive monitoring program will be required to guide efforts toward meeting yet to be quantified targets.

Monitoring gaps and effort required

This review has identified many strong and seemingly robust trends arising from impressive long-term monitoring efforts. However, while our knowledge of population trends for open water species and migratory shorebirds is reasonable, there are many species, indeed several entire groups of waterbirds for which we have little, if any, knowledge of trends in the short, medium or long terms. Wetland passerines, crakes and rails and Bitterns (which make up the thick wetland vegetation dependent

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group) along with the one wetland dependent raptor (Swamp Harrier) have not featured in any large- scale waterbird monitoring programs to date. Because these species also do not occupy the attention of terrestrial bird monitoring programs, the state of these species populations represent a complete knowledge void in the context of population trends for Australia’s avifauna; although there are some specialist monitoring programs for species such as Capricorn Yellow Chat which have yielded excellent population trends. Our knowledge of population trends for resident shorebirds (most of which are endemic to Australia) is also poor, with the majority of these birds having no trend information at all, and only marginally reliable trends available for the remainder. Trends for the Eastern Hooded Plover, although not included here, are in fact reasonably well established at least for mainland Australia (in prep) and there is a large and ever-growing amount of detailed nesting and demographic information available for coastal plovers and oystercatchers through the Beach Nesting Birds Program (BirdLife Australia) which is promising. Trends for marine waterbirds (terns) are also largely unknown. Terns may benefit from frequent inclusion in Shorebirds 2020 surveys, as well as more targeted surveys at breeding colonies, although in some cases more specialist monitoring of breeding colonies is likely required; something that programs such as BirdLife Australia’s Beach Nesting Birds Program (which specialises in nest monitoring and the ethical and scientific requirements that entails) are well suited to.

For other groups which scored higher on monitoring adequacy and trend reliability, there is no shortage of species for which monitoring representativeness can be improved. Many of these gaps have also been identified previously (Kingsford, Porter et al. 2011, Clemens, Kendall et al. 2012), and it is clear that expanded aerial surveys in the north, northwest, and to a lesser degree the southwest of the continent would go a long-way toward yielding a continental understanding of how waterbird numbers are tracking. Indeed, we identified 57 species whose trends would be based on more representative sampling if aerial monitoring based on UNSW’s approach was expanded. However aerial surveys will never cater for small, dispersed and/or cryptic species. For such species groups increasing ground wetland surveys is required.

Although generally well monitored, most migratory shorebirds could also benefit from expanded ground surveys in either the north or central parts of the continent; although these areas are usually very hard to access in a cost efficient manner and so volunteer-based monitoring may not be possible and monitoring at existing areas should not be abandoned lest it compromise our current knowledge base.

Given the naturally high variation in Australian waterbird numbers across both space and time and relatively long climatic cycles of boom and bust, efforts to fill knowledge gaps will need to be inclusive of huge spatial scales over decade-long time periods to generate high quality information. Indeed, many species for which we were able to estimate trends have sufficient data only to detect disappearance or catastrophic declines. We estimate this is the case for at least 35 waterbird trends presented here. Repeated ground surveys over long time periods would likely increase our understanding of population change in these species. Fortunately, in the last decade, expanding waterbird monitoring efforts are providing growing spatial representativeness, and if those efforts continue and expand further, we will have a much-improved understanding of changes in waterbird numbers.

There are also a number of waterbird species that are monitored which were not analysed here, as data did not meet the thresholds for inclusion in the methods employed; because methods developed here

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were developed to suit general situations. Consideration of this data using more bespoke methods is likely to yield significant additional knowledge. For instance, efforts to quantify Eastern Hooded Plover population changes using biennial count data are underway (in prep). For other species more monitoring is necessary, be this a continuation of existing levels of effort to build longer time series and/or expansion of coverage. Expanding systematic Australasian Bittern counts is an example of a monitoring program that requires continuation (of existing sites) and expansion into new areas through initiatives such as the Bitterns in Rice program (https://www.bitternsinrice.com.au). Expanding the application of ATLAS type data through may also fill several key knowledge gaps, e.g. for low density cryptic species. We estimate that trends maybe definable through the use of this kind of data for an additional 17 waterbird species. For other waterbirds, application of spatial distribution models (SDMs) across space and time may provide usable broad trend proxies (Clemens, Beher et al. In prep).

While some species are likely best monitored at national scales due to their large and unpredictable movements (Pedler, Ribot et al. 2014), many species can be monitored effectively at smaller scales. Most of the species that would benefit from expanded aerial surveys, would also benefit from expanded surveys at a regional level. In most drainage divisions and river regions there are currently too few areas being sampled to yield reliable trends. While most of Australia is too remote for ground surveys to be practical, there are a number of drainage divisions where ground surveys could be employed to give more reliable trend estimates at that scale. Further, drainage divisions near urban population centres are also places where water is actively managed, so management actions that target waterbirds can more readily be applied. Pilot trials of waterbird monitoring in the Brisbane region indicate there is an appetite among members and the public to help monitor waterbirds. In this context regular on- ground waterbird monitoring could provide detailed fine-scale data, representative of year-round fluctuations in waterbird populations. BirdLife Australia is currently considering how to best take forward a national wetland monitoring programme in the context of new as well as existing and historical monitoring efforts.

While large gaps remain in our understanding of how waterbird populations are changing at a continental scale, by bringing all available data together it is possible to report on what the best available data indicates at a variety of temporal and spatial scales. This provides a way to compare apparent trends between areas and time periods, and may point toward areas where management or sampling are better or worse across Australia.

Future reporting – the need for centralisation

In this review reporting was conducted at national scales, and within drainage divisions, river regions, key biodiversity areas, Ramsar sites, and shorebird areas. The benefits of reporting on the available data regularly and automatically at a variety of spatial and temporal scales include: 1) increasing the chances of identifying surprises in the data that would otherwise be missed; 2) generating initial reporting which is rarely ever undertaken that can be verified as required; 3) identifying gaps in our knowledge or understanding of how or why populations are changing; 4) providing feedback to those collecting the data which may encourage vetting of available data or efforts to improve monitoring efforts; 5) reporting on what the best available data are suggesting so that actions can be taken and modified; 6) providing more comparable results for more areas; and 7) determining if Australia is meeting conservation targets. To date such an overview has been hard to establish as (on-ground) waterbird monitoring efforts across the country are highly fragmented, with limited communication between

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stakeholders and a number of key datasets not normally easily available. For these reasons we strongly encourage centralised storage of all waterbird data and development of automated graphical and quantitative trend reporting. These steps would likely lead to improvement of monitoring efforts especially as they expand.

Improving monitoring, analysis and explanatory power

The need for improved monitoring efforts was clear from this review. Despite having 4 million waterbird records available, this review only identified widespread (from 8 or more sites) trends for a handful of additional species to those counted in either the East Australian Waterbird Survey (EAWS) or Shorebirds 2020 programs. Those additional species included eight extra species in which long-term trends could be identified, 17 in the medium-term, and 28 in the short-term. This is not surprising given there are few waterbird monitoring programs that have persisted for over a decade with targeted sampling aimed at understanding waterbird populations. When clear questions, targets, and long-term consistent effort are lacking, data collected are often unable to deliver answers (Legge, Robinson et al. 2018). None-the-less, increasing count effort over the years is leading to a growing volume of waterbird data which is more representative of a variety waterbird species distributions. This increasing capacity to report trends does not necessarily correlate with reporting accurate trends for populations. It is critical that automatically generated results such as many of these are viewed against the data adequacy scores, as the best available data may yield a result, but not one that should be trusted to represent how that species population is changing.

One of the clearest messages from our review, was that results only became somewhat consistent in sensitivity analyses, when data were collected over long-time periods and over very large spatial scales. This was especially true for non-migratory waterbirds, some of which track sporadic availability of temporary wetlands across vast distances (Kingsford and Norman 2002). Similarly, those trends became still more consistent when averaged across species belonging to a functional group. Taking the geometric mean from individual species abundance estimates for functional groups not only provides less variable estimates, it also indicates how species without sufficient data in different functional groups may be changing in abundance over time. However, the species within each of these functional groups are highly diverse in their habitat requirements and in their geographic distributions (HANZAB. 2006). It is therefore not surprising that some species trends can be very different to the average trend of its functional group. This issue also underlines why we do not consider the construction of a single national index comprising all waterbirds to be of particular use: at a continental scale a single index would largely cease to provide meaningful information. Regardless of the taxonomic level of analysis, many waterbirds (particularly large and/or congregatory open water species), the EAWS provided the foundation for trends in waterbirds in Eastern Australia. Not surprisingly, when data from this long- term, systematic survey of one third of the Australian continent were not included, results often varied. Results were also sensitive to the length of the time series. When assessing longer term trends, the obvious declines in waterbirds over the last 34 years largely disappeared in data from the last ten or twenty years (Appendix A). There were cases when inclusion of recently collected data from large populations in Western Australia markedly changed the estimated trajectory of that species’ population in recent years; i.e. Gull-billed Tern. However, for the most part additional data had little impact on the trajectories indicated in EAWS data. This is perhaps not surprising given that most additional data would come from wetlands or coastlines in eastern Australia. Within eastern Australia there was also little evidence to suggest that birds that go missing from the EAWS survey data are simply moving to

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the coast. This was surprising given previous reporting (Alcorn, Alcorn et al. 1994, Wen, Saintilan et al. 2016). While results here are not conclusive, aside from some evidence of limited movement to the coasts in species like Australasian Shoveler, most species that declined recently in EAWS survey data, did not obviously shift to the coast. This suggests that the boom and bust cycles reflected in EAWS data reflect periods of high recruitment and mortality.

Available data on migratory shorebirds includes a core group of sites that have been monitored consistently for over three decades, but also includes many other areas with variable spatial and temporal coverage (Clemens, Kendall et al. 2012). Non-linear trends in medium and short-term periods yielded very similar results when areas were excluded from analysis. This indicates that shorebird sampling in the last twenty years is largely adequate in number of areas being monitored, although certainty in available trends for some species would increase markedly with more sampling in the north of Australia (Driscoll 2001, Chatto 2003). In data spanning over three decades, sampling is far more spatially and temporally heterogenous. All the count areas within any shorebird area were often not counted in all years, and some shorebird areas have missing data for periods exceeding a decade. This patchier data resulted in trends that were highly sensitive to data sub-setting and methods. By taking a meta-analysis approach data could be fit with models that appropriately captured more of the variation over time. Admittedly, results would likely be further improved by tailoring each method more precisely to the available data from each area. The meta-analyses reported here did allow for data with missing values and non-linear trends to be captured. There are a wide variety of techniques that can be applied to available migratory shorebird data (Rogers, Rogers et al. 2007, Cooper, Clemens et al. 2012, Minton, Dann et al. 2012, Hansen, Menkhorst et al. 2015, Clemens, Rogers et al. 2016, Studds, Kendall et al. 2017), and it is important to realise some techniques are more appropriate for some species, time periods, or questions than others. Given the heterogeneous data available, a flexible analysis framework provides better local trend estimates. Such an approach allows inclusion of much older records from some areas, and maximises the spatial coverage of data. Without such an approach long- term declines in species like Red-necked Stint and Sharp-tailed Sandpiper would be masked through the omission of data from sites like the Coorong. The trade off with such an approach is that trends are less precise, and for some species, significant declines can disappear entirely depending on how data are sub-set or analysed. It is also worth highlighting that this approach works on migratory shorebird data because birds generally return to the same definable area each year (Clemens, Herrod et al. 2014), and trends are relatively consistent across both space and time (Clemens, Rogers et al. 2016).

Many waterbird results were highly sensitive to missing data, number of areas surveyed, treatment of zero inflated data and length of time series. Results were more consistent when data were available at extremely large spatial scales and long periods. Results were also sensitive to large outliers in the data which were often a true reflection of the number of waterbirds present in a given area at a specific time. At finer scales we did not find any model that performed well describing these occasional high counts but averaging across large spatial scales reduced or eliminated the zeros, collapsed the distribution of counts, and provided far-improved residual plots when site-based estimates of abundance were averaged annually.

Both the EAWS and the Shorebirds 2020 program clearly demonstrate the utility of broad scale systematic monitoring programs to track changes in many congregatory (flock forming) waterbirds. However, these two monitoring programs, while among Australia’s most successful and valuable sources of knowledge for birds, are not sufficient in themselves to provide a fully representative picture

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of waterbird health in Australia. Bringing all the available data together has helped identify gaps in spatial coverage, and species with too little data for trend analyses. BirdLife Australia is currently in the early stages of working towards a national waterbird monitoring programme, which will build on existing as well as historic survey efforts with a particular emphasis on targeting priority areas – i.e. those areas where additional monitoring could have immediate impact on our ability to calculate robust trends.

In addition, while we note that any population trend estimate should always be viewed against data adequacy diagnostics, the amount of reporting that can be automatically generated when all available data are brought together highlights to utility of centrally storing all available waterbird data. Automated reporting such as this could be generated at the click of a mouse from a website tied to a central database. These kinds of improvements can facilitate positive feedback to improve monitoring and provide comparisons an indication of where management actions or further investigations are needed.

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Appendices

Appendix A. Alternative methods attempted Appendix B. Comparisons of coastal and inland trends Appendix C. East Australian Waterbird Survey trends Appendix D. Trends within drainage divisions Appendix E. Trends at Shorebird Areas, Ramsar Sites, Key Biodiversity Areas, or River Regions Appendix F. National species trends and spatial distribution of data Appendix G. Monitoring adequacy and trend reliability table Appendix H. List of scientific names

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