Conserving Migratory and Nomadic Species

Conserving migratory and nomadic species

Claire Alice Runge

A thesis submitted for the degree of Doctor of Philosophy at

The University of Queensland in 2015

School of Biological Sciences

Abstract

Migration is an incredible phenomenon. Across cultures it moves and inspires us, from the first song of a migratory bird arriving in spring, to the sight of thousands of migratory wildebeest thundering across African plains. Not only important to us as humans, migratory species play a major role in ecosystem functioning across the globe. Migratory species use multiple landscapes and can have dramatically different ecologies across their lifecycle, making huge contributions to resource fluxes and nutrient transport. However, migrants around the world are in decline. In this thesis I examine our conservation response to these declines, exploring how well current approaches account for the unique needs of migratory species, and develop ways to improve on these. The movements of migratory species across time and space make their conservation a multidimensional problem, requiring actions to mitigate threats across jurisdictions, across habitat types and across time. Incorporating such linkages can make a dramatic difference to conservation success, yet migratory species are often treated for the purposes of conservation planning as if they were stationary, ignoring the complex linkages between sites and resources. In this thesis I measure how well existing global conservation networks represent these linkages, discovering major gaps in our current protection of migratory species. I then go on to develop tools for improving conservation of migratory species across two areas: prioritizing actions across species and designing conservation networks.

Protected areas are one of our most effective conservation tools, and expanding the global protected area estate remains a priority at an international level. Globally, about 12.9% of the landscape is covered by protected areas, and in Chapter 2 I examine how well migratory birds are represented within current protected areas, specifically taking into account their need for protection across the annual cycle. I discover that just 9% of migratory birds meet standard protection targets across all parts of their migratory distribution, their breeding, non-breeding and passage distributions, in stark contrast to the 45% of non-migratory birds meeting the same targets. There is currently a major push to increase the size and comprehensiveness of the global protected area estate, and these findings highlight the need for greater emphasis on collaboration across nations to incorporate migratory connectivity into our approaches to protected area placement.

One of the challenges to incorporating migratory connectivity into systematic conservation planning is that often we have only a poor understanding of the patterns of movements of migratory species

i in space and time. In Chapter 3 and 4 I develop a tool for discovering spatial dynamics in highly mobile yet data-poor species, unlocking valuable information for improved extinction risk assessment and conservation planning. Using Australian arid-zone nomadic birds as a case study, I reveal enormous variability in predicted spatial distribution over time. This variability has huge impacts on both our ability to estimate extinction risk accurately in migratory species; and the way that we plan conservation actions across the landscape. In Chapter 3 I discover that several species not currently classified as globally threatened contracted to very small areas during times of poor environmental conditions despite their normally large geographic range size, raising questions about the adequacy of conventional assessments of extinction risk based on static geographic range size (e.g. for IUCN Red Listing). I develop guidelines to better estimate extinction risk for nomadic species.

In Chapter 4 I examine how the spatial configuration of conservation actions changes when we incorporate these spatial and temporal movements of mobile species. Using the time series of predicted distributions of Australian nomadic birds developed in Chapter 3, I show that accounting for movements changes the spatial pattern of conservation investment and increases the overall area needed for conservation measures. This presents both a challenge and an opportunity for conservation practitioners, who will need to work with landholders to provide sustainable conservation outcomes in activities such as agriculture, grazing and mining. It suggests a need for a shift in the current paradigm of conservation happening in ‘special places’ such as protected areas to conservation being a whole-of-landscape activity.

Underpinning my thesis is the need to evaluate and rethink how we plan and apply conservation actions across the full annual cycle in migratory species. In Chapter 5 I summarise state-of-the-art advances in conservation planning for migratory species, outline gaps and suggest new directions for research. I discover that very few large-scale conservation projects incorporate the needs of migrants, and yet incorporating migratory connectivity is essential to long-term success of conservation actions for these species. I describe some recently developed tools to incorporate migratory connectivity into conservation planning; and discover that several useful tools relevant to the problem of migratory species conservation already exist in other disciplines.

Throughout this thesis I use the terms ‘migratory’ and ‘mobile’ interchangeably. In doing so, I use the broadest definition of the term migratory, to encompass any species making cyclical or

ii predictable movements, from the seasonally predictable movements of classic migrants to the environmentally predictable movements of nomadic and irruptive species.

Migratory species use large geographic areas, and their effective conservation is undoubtedly a major challenge. While imperfect knowledge is often cited as a barrier to conserving migratory species, I have shown that we can make considerable progress based on existing knowledge, and that substantial action is needed now to ensure that one of nature’s great phenomena remains for future generations.

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Declaration by author

This thesis is composed of my original work, and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution by others to jointly-authored works that I have included in my thesis.

I have clearly stated the contribution of others to my thesis as a whole, including statistical assistance, survey design, data analysis, significant technical procedures, professional editorial advice, and any other original research work used or reported in my thesis. The content of my thesis is the result of work I have carried out since the commencement of my research higher degree candidature and does not include a substantial part of work that has been submitted to qualify for the award of any other degree or diploma in any university or other tertiary institution. I have clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.

I acknowledge that an electronic copy of my thesis must be lodged with the University Library and, subject to the General Award Rules of The University of Queensland, immediately made available for research and study in accordance with the Copyright Act 1968.

I acknowledge that copyright of all material contained in my thesis resides with the copyright holder(s) of that material. Where appropriate I have obtained copyright permission from the copyright holder to reproduce material in this thesis.

Publications during candidature

Runge, CA, TG Martin, HP Possingham, SG Willis, and RA Fuller. 2014. Conserving mobile species. Frontiers in Ecology and the Environment. 12: 395-402. doi:10.1890/130237

Runge, CA, AI Tulloch, E Hammill, HP Possingham, and RA Fuller. 2015. Geographic range size and extinction risk assessment in nomadic species. Conservation Biology. doi: 10.1111/cobi.12440

Other articles:

Runge, C. 2012 Making decisions on migrating species. Decision Point 61:14

Tulloch, A. & Runge C. 2013. Common Myna – scourge or just another bird? Warbler 12.

Conference abstracts:

Runge et al. 2014 Designing a protected area network for Australian arid-zone birds. Ecological Society of Australia 2014 National Conference. Alice Springs, Australia.

Runge et al. 2014 Global patterns in the conservation of migratory birds. International Ornithological Congress 2014. Tokyo, Japan.

Runge et al. 2013 Conserving tropical nomads. Association of Tropical Biology and Conservation 2013 Conference, San Jose, Costa Rica.

Runge et al. 2013 Conserving nomadic birds. Birds of Tropical and Subtropical Queensland Conservation Conference, Brisbane, Australia.

Runge et al. 2012 Unravelling the mysteries of nomadic species: refugia vs resource tracking. Ecological Society of Australia 2012 National Conference, Melbourne Australia

Runge et al. 2011 Species Distribution Modelling for predicting migration patterns. International Congress on Conservation Biology 2011. Auckland, New Zealand.

Publications included in this thesis

Runge, CA, TG Martin, HP Possingham, SG Willis, and RA Fuller. 2014. Conserving mobile species. Frontiers in Ecology and the Environment. 12: 395-402. doi:10.1890/130237

Contributor Statement of contribution Runge CA (Candidate) Designed study (50%) Visualised data (100%) Wrote manuscript (60%) Martin TG Wrote and edited paper (10%) Possingham HP Wrote and edited paper (5%) Willis SG Designed study (20%) Wrote and edited paper (5%) Fuller RA Designed study (30%) Wrote and edited paper (20%)

Runge, CA, AI Tulloch, E Hammill, HP Possingham, and RA Fuller. 2015. Geographic range size and extinction risk assessment in nomadic species. Conservation Biology. doi: 10.1111/cobi.12440

Contributor Statement of contribution Runge CA (Candidate) Designed study (60%) Created models (100%) Analysed and interpreted data (100%) Wrote the paper (70%) Tulloch AI Wrote and edited paper (15%) Hammill E Statistical analysis of data in Figure 3.3 (80%) Wrote and edited paper (5%) Possingham HP Designed study (20%) Wrote and edited paper (5%) Fuller RA Designed study (20%) Statistical analysis of data in Figure 3.3 (20%) Wrote and edited paper (5%)

Contributions by others to the thesis

Chapter 2 Runge, CA, JW Watson, SHM Butchart, HP Possingham, JO Hanson and RA Fuller. In preparation. Migratory birds are largely unprotected.

CAR, RAF and HPP designed the study. JOH wrote scripts and assisted with data analysis. CAR post-analysed data, created figures and wrote the manuscript. All authors discussed the results and edited the manuscript.

Chapter 4 Runge, CA, AI Tulloch, V Tulloch, HP Possingham, and RA Fuller. In preparation. Incorporating dynamic distributions into spatial prioritization.

CAR created models, ran analysis, interpreted results, created figures and wrote the manuscript. All authors designed the study, discussed the results and edited the manuscript.

Statement of parts of the thesis submitted to qualify for the award of another degree

None.

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Acknowledgements

I started this PhD with a decade’s experience in chemistry but having never studied biology, ecology or anything remotely related to conservation. What I did have was a fascination and deep love of birds, and a desire to dedicate my life to their conservation. I’m immensely thankful to my supervisors, Richard Fuller and Hugh Possingham, who took a gamble on me.

Rich, you are an inspiration and a fabulous supervisor. You taught me to find the story hidden in the detail, and to think big. Like any good relationship, we didn’t necessarily always see eye to eye, but came out the other side with a smile and a laugh, and a thesis much improved as a result. It’s been an absolute pleasure.

Hugh, you taught me that it’s all a game, and a fun one at that. From you I learnt patience, that conservation wins can take a long time. I’ll always be indebted to your generosity and willingness to donate ideas.

This work would not have been possible without the contribution of my many colleagues, who made this a vibrant, exciting time. I’m indebted to your endless generosity in sharing and shaping this work.

Thanks to all my friends and family for being there when I needed, for the hugs, the treats and for listening.

Jason, my nomad, your unending support means the world to me.

This work was supported financially by a BirdLife Australia Stuart Leslie Award, Birds Queensland, an Australian Research Council (ARC) Postgraduate Award, and a Centre of Excellence in Environmental Decisions (CEED) scholarship.

Finally, to the birds - thanks for being so interesting and so very surprising.

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Keywords

Conservation planning, migration, migratory connectivity, nomadic, movement, species distribution modelling, threat listing, spatial prioritisation, extinction risk.

Australian and New Zealand Standard Research Classifications (ANZSRC)

ANZSRC code: 050202, Conservation and Biodiversity, 60%

ANZSRC code: 050211, Wildlife and Habitat Management, 30%

ANZSRC code: 060208, Terrestrial Ecology, 10%

Fields of Research (FoR) Classification

FoR code: 0502, Environmental Science and Management, 80%

FoR code: 0602, Ecology, 20%

Table of contents

Abstract ...... i

1 Introduction ...... 2

1.1 The challenge of conservation planning for migratory species ...... 3

1.1.1 Accounting for migratory connectivity ...... 4

1.1.2 Effectiveness of protected areas as a tool for conservation of migratory species ...... 5

1.1.3 Protection of resources and connectivity is essential ...... 6

1.2 How well are migrants faring? ...... 7

1.3 Conserving the phenomenon of migration ...... 8

1.4 The ecology of migration ...... 9

1.5 Nomads: Extreme migrants ...... 12

1.6 Conservation planning for nomadic species ...... 14

1.6.1 Species prioritisation in nomadic species ...... 14

1.7 Tools for conservation planning of migratory species ...... 15

1.8 Thesis overview ...... 16

2 Protected areas and the world’s migratory birds ...... 18

2.1 Abstract ...... 18

2.2 Introduction ...... 18

2.3 Methods and data...... 20

2.3.1 Birds ...... 20

2.3.2 Protected areas ...... 20

2.3.3 IBAs ...... 21

2.3.4 Analysis ...... 21

2.4 Results ...... 22

2.4.1 Protected area coverage of migratory species ...... 22

2.4.2 Protected area coverage of Important Bird and Biodiversity Areas (IBAs) ...... 23

2.4.3 Protected area coverage of threatened species ...... 23

2.4.4 Evaluation of representation targets for protected area coverage ...... 25

2.4.5 Sensitivity analysis excluding seabirds ...... 26

2.5 Discussion ...... 27

2.6 Conclusions ...... 31

3 Geographic range size and extinction risk assessment in nomadic species ...... 34

3.1 Abstract ...... 34

3.2 Introduction ...... 35

3.3 Methods ...... 37

3.3.1 Case study area and species ...... 37

3.3.2 Species distribution models ...... 38

3.3.3 Extinction risk ...... 41

3.4 Results ...... 41

3.5 Discussion ...... 47

3.6 Conclusions ...... 52

4 Incorporating dynamic distributions into spatial priority setting for conservation ...... 54

4.1 Abstract ...... 54

4.2 Introduction ...... 54

4.3 Methods and data...... 57

4.3.1 The study region...... 57

4.3.2 Conservation features and targets ...... 58

4.3.3 Prioritizing habitats for nomads ...... 59

4.3.4 Coverage of current protected area estate ...... 60

4.4 Results ...... 61

4.5 Discussion ...... 65

4.6 Conclusion ...... 69

5 Conserving mobile species ...... 72

5.1 Abstract ...... 72

5.2 Introduction ...... 72

5.3 Accounting for dependencies among sites ...... 74

5.4 Conservation objectives for mobile species ...... 78

5.5 Conserving mobile species with incomplete and uncertain information ...... 81

5.6 Defining an appropriate suite of actions...... 83

5.7 Conclusions ...... 84

6 General Discussion ...... 86

6.1 Thesis summary ...... 86

6.2 Synthesis ...... 89

6.3 Assumptions and model limitations ...... 89

6.4 Future directions ...... 90

6.5 Final words ...... 92

7 References ...... 93

8 Appendices ...... 116

8.1 Appendix 1: Proportion of species meeting targets, by country...... 116

8.2 Appendix 2: Species movement classifications, modelled range size metrics and model validation statistics for 74 Australian inland birds...... 123

8.3 Appendix 3: Range dynamics across time for 74 nomadic arid-zone birds ...... 127

8.4 Appendix 4: Mathematical formulation of Marxan with temporal dynamics ...... 137

List of Figures

Figure 1.1 Types of migration and Australian examples ...... 10

Figure 1.2 Global bird species richness, as measured by the number of species occurring with each ~23,332 km2 hexagon. Reproduced from (Somveille et al. 2013) ...... 11

Figure 1.3 The number of bird species occurring with each country that exhibit the three major movement strategies, (a) classic (full) migration (as percent), (b) classic (full) migration (as number of species) (c) altitudinal migration, and (d) nomadism. The species distribution data underlying this map are from BirdLife International and NatureServe (2012) and include all seasonal components of species’ distributions...... 11

Figure 2.1 Global inequity in protected area coverage of migratory birds, as shown by (a) the percentage of migratory bird species within each country meeting targets for protected area coverage for each part of their migratory range within that country, (b) the percentage of migratory bird species within each country meeting targets for each part of their migratory range globally and (c) the percentage area covered by protected areas in that country. Targets are scaled by the size of each part of the seasonal distribution. Note the difference in the range of the color ramp between the three maps...... 28

Figure 3.1 Spatial and temporal bias in survey effort within the study region ...... 38

Figure 3.2 Examples of temporal dynamics in geographic range size for birds in arid Australia: (a) Scarlet-chested Parrot (b) Black Honeyeater (c) Letter-winged Kite (d) Yellow Chat (e) Gibberbird. Dotted line shows mean annual rainfall for Australia for the period. Also plotted is (f) mean annual rainfall (dotted line); mean annual fraction of photosynthetic vegetation (solid line); mean annual fraction of non-photosynthetic vegetation (dashed line) across Australia...... 43

Figure 3.3 Mean modelled geographic range size plotted against order of fluctuation (maximum range size divided by minimum range size) for all 43 nomadic species. Those species showing fluctuation between minimum and maximum range size of more than one order of magnitude are labelled - Aphelocephala pectoralis (AP), Neophema splendida (NS), Heteromunia pectoralis (HP), Nymphicus hollandicus (NH), Elanus axillaris (EA), Elanus scriptus (ES), Lichmera indistincta (LI), Epthianura tricolor (ET), Melopsittacus undulatus (MU), Purnella albifrons (PA), Conopophila whitei (CW)...... 44

Figure 3.4 The relationship between pooled geographic range size and the time-sliced estimates of maximum (y~0.70x-2.6x104, p<0.001) ,mean,(y~0.40x-4.4x104, p<0.001) and minimum.(y~0.17x-1.6x104, p<0.001) Bounding lines indicate 95% confidence intervals ...... 46

Figure 3.5 Geographic range size dynamics for (a) Scarlet-chested Parrot and (b) Chestnut- breasted Whiteface. Dashed lines indicate thresholds under IUCN Red List guidelines B2ii (Area of occupancy, AOO: CR <10 km2; EN <500 km2; VU < 2000 km2), and the minima are magnified in the middle plot. The lower panel plots the occurrence records for each species across time...... 48

Figure 3.6 Trends in habitat suitability fluctuate according to geographic location; Assuming a linear relationship between habitat suitability and population size, the overall trend (a) shows population dynamics are somewhere in between that estimated by monitoring at the core (b) and edges (c-d) of the overall species range for the Black Honeyeater ...... 50

Figure 4.1 Selection frequencies and difference maps of spatial prioritization under five scenarios. Selection frequency (how often a planning unit (PU) is chosen across 100 runs) under scenario: a) static b) time-sliced c) annual d) monthly e) bottleneck; Dark blue = PU chosen in 100 runs, white = PU never chosen; .and difference maps of static vs f) time- sliced g) annual h) monthly i) bottleneck. Colours indicate the difference in selection frequency between the static scenario and the current scenario. Blue = PU chosen in current scenario, but not in static scenario, red = PU chosen more often in the static scenario. White = PU selected (or not selected) equally in both...... 63

Figure 4.2 Percent of distribution held within current protected area estate for 42 nomadic species. Whiskers show minimum and maximum protection across the 11 years, boxes show interquartile range and mean protection is indicated by grey vertical line...... 64

Figure 4.3 Maps of nomadic bird species diversity a) during bottlenecks b) total aggregated across time c) average diversity at any point in time...... 66

Figure 4.4 Priority areas for protected area expansion a) sites prioritized under time-sliced scenario b) sites prioritized under bottleneck scenario c) robust sites irreplaceable under all five scenarios ...... 67

Figure 5.1 In this theoretical example, habitat loss has affected one-eighth of the total habitat available to a species that occurs in two patches. If habitat quality and population

abundance are evenly distributed within and among patches, we might predict that a sedentary species (a) will decline in total population size by one-eighth as a result of the habitat loss. Where the two patches are linked by migration (b), we might predict a population decline of one-quarter because the entire population passes through the affected patch at some point during its life cycle. If one habitat patch is lost altogether, extinction of the migratory species will result...... 74

Figure 5.2 The use of migration corridors or stopover sites makes mobile species vulnerable to changes in habitat quality in relatively small and briefly used area. A decline in quality or loss of access to small sites can result in disproportionately large population losses. Panels (a), (b), and (c) represent scenarios in which two breeding populations of a migratory species pass through stopover sites en route to overlapping non-breeding sites. In each of the three scenarios, only two stopover sites are lost; however, the population implications are highly dependent on the spatial configuration of that loss. Understanding migratory connectivity can be crucial to managing mobile species effectively...... 75

Figure 5.3 Analysis of tracking data for Mongolian saiga (Saiga tatarica mongolica) reveals the presence of bottlenecks in their migration. Migration is funneled by geographical constraints through a small valley, leaving these migration pathways at risk of being blocked off by changing human use. As anti-poaching measures improve prospects for this species, maintaining these migration pathways will be essential for the long-term management of these animals. Adapted from Berger et al. (2008)...... 77

Figure 5.4 Eastern curlews (Numenius madagascariensis) migrate each year from the Arctic to Australia, stopping to feed and rest at tidal flats across the East Asian–Australasian Flyway (EAAF). The species has recently been uplisted to globally Vulnerable, and habitats across its migration and non-breeding range are susceptible to degradation and loss through prey species declines, reclamation, changes in sedimentation patterns, and sea-level rise. Managing these multiple interacting threats requires conservation actions that take account of migratory connectivity, and that operate in many countries across the Flyway. One important conservation initiative has been the formation of the EAAF Partnership, an alliance of 30 governments and non-governmental organizations working across the region. The Partnership has already listed a network of more than 100 important sites across the Flyway in 16 countries. Image credit: ©Dean Ingwersen ...... 79

Figure 5.5 Stable isotope analysis was used to map the spatial connections between five non- breeding populations and five breeding regions for the American redstart (Setophaga ruticilla). This map shows the distribution of the most likely breeding region (NW = Northwest; MW = Midwest; NE = Northeast; CE = Central-East; SE = Southeast) for individual redstarts at each non-breeding region (M = Mexico; C = Central America; W = Western Greater Antilles; E = Eastern Greater Antilles; L = Lesser Antilles). Black dots indicate sampling locations and bars indicate the proportion of individuals assigned to each breeding region. For example, the entire Northwest breeding population migrates to Mexico; failing to protect non-breeding habitat in Mexico will therefore likely doom the Northwest breeding population of redstarts to extinction. Adapted from Martin et al. (2007). Image credit: ©M Reudink ...... 82

List of Tables

Table 2.1 Mean rates of protection for migratory and non-migratory bird species, number of gap species and proportion of species meeting targets for protected area coverage. Rates of protection are calculated as the proportion of the species distribution contained within protected areas. Gap species are those where none of their distribution is held within protected areas, with ‘any part of cycle’ being the number of species which lack protection in any one of their resident, breeding, non-breeding or passage distributions. The rows below ‘Part of annual cycle’ refer to full migrants...... 22

Table 2.2 Global protected area coverage of Important Bird and Biodiversity Areas (IBAs), shown for those migrants and non-migrants for which IBAs have been identified ...... 23

Table 2.3 Global protected area coverage of threatened species ...... 24

Table 2.4 Global protected area coverage of Important Bird and Biodiversity Areas (IBAs), shown for those migrants and non-migrants for which IBAs have been identified; threatened species...... 24

Table 2.5 Comparison of number of species meeting targets under different scenarios. Targets are either (i) based on the size of the distribution in each part of the annual cycle separately or (ii) based on the overall distribution size...... 25

Table 2.6 Global protected area coverage and number of species meeting targets; seabirds ...... 26

Table 3.1: Reclassification of NVIS Major Vegetation Groups into 12 vegetation variables for species distribution models ...... 39

Table 3.2: Range size and extinction risk metrics for 43 nomadic bird species ...... 45

Table 4.1 Cost and area prioritized for each of the five scenarios...... 61

Table 4.2 Comparison of spatial dissimilarity of the five scenarios, Bray–Curtis dissimilarity 0 = identical, 100 % = completely dissimilar...... 61

Table 4.3 Ten most poorly protected species. Species in bold are the least protected both averaged across time and during bottlenecks...... 64

Table 5.1 Descriptions of large-scale animal movements ...... 73

List of Abbreviations used in the thesis

AOO Area of occupancy CBD Convention on Biological Diversity 1992 CMS Convention on the Conservation of Migratory Species of Wild Animals (also known as the Bonn Convention) EOO Extent of occurrence FPV Fractional photosynthetic vegetation IBRA Interim Biogeographic Regionalisation for Australia IUCN International Union for Conservation of Nature NPV Non- photosynthetic vegetation PV Photosynthetic vegetation SDM Species distribution modelling

Definition of common terms

Arid: The region encompassing the arid-zone, semi-arid zone and rangelands of Australia.

Classic migration: Regular, seasonally predictable movement from breeding grounds to non- breeding grounds.

Migratory: Cyclical or predictable movements, from the seasonally predictable movements of classic migrants to the environmentally predictable movements of nomadic and irruptive species.

Mobile: Throughout this thesis I use the terms ‘migratory’ and ‘mobile’ interchangeably.

Nomadic: Seasonally irregular, environmentally predictable movements outside a home range.

“Birds were flying from continent to continent long before we were. They reached the coldest place on Earth, Antarctica, long before we did. They can survive in the hottest of deserts. Some can remain on the wing for years at a time. They can girdle the globe. Now, we have taken over the earth and the sea and the sky, but with skill and care and knowledge, we can ensure that there is still a place on Earth for birds in all their beauty and variety — if we want to… And surely, we should.”

Sir David Attenborough, The Life of Birds

Chapter 1 Introduction

1 Introduction

From the writings of Aristotle (Aristotle 1910), to the musings of Gilbert White in Georgian England (White 1789), migratory species have fascinated humans for centuries. Their movements inspire and awe us, from the sight of vast herds of migrating wildebeest roaming the African savannah, to the long-distance flight of arctic terns Sterna paradisaea, who over the course of their lifetime fly the equivalent of travelling to the moon and back three times. Migratory species occur on every continent and in every ocean. Along their travels, migrants make major contributions to resource fluxes, nutrient transport, predator-prey interactions and food-web structure in and across ecosystems (Bauer & Hoye 2014), but despite this, and their role in human culture, their special requirements for conservation planning are often overlooked, and many migratory species are suffering critical declines (Kirby et al. 2008; Harris et al. 2009; Craigie et al. 2010).

Over the past century, the human footprint on our planet has expanded enormously such that we have now substantially impacted 83% of the Earth’s surface (Sanderson et al. 2002). These increases in human impact on landscapes across the globe have degraded natural ecosystems and species that inhabit them. Consequently, 20% of all species are listed as threatened and this number is rising (Hoffmann et al. 2010). Simultaneously however, we have seen a growing moral and practical drive to manage human land use to allow ecosystems and species to persist and thrive in the face of global change; and the rise of modern conservation science.

The past two decades have seen the growth of systematic conservation planning, in both theory and practice (Moilanen et al. 2009). Systematic conservation planning is the science and art of choosing conservation actions in space and time, and doing so in a way that maximises both their efficiency and effectiveness. This science operates within the paradigm that we live in a world where conservation must compete with other human demands on natural resources and space; answering a basic question: How do we choose where and on what to focus our conservation investments?

Conservation science has tended to assume that the targets of management, such as species or ecosystems, are static in space and time (Pressey et al. 2007). However, more than 12% of the world’s vertebrates frequently make long distance movements, whether regular classic migratory movements or nomadic wanderings, and the distributions of such mobile species span every continent and ocean (Robinson et al. 2009). These migratory species are subject to the same pressures and threats as their sedentary counterparts, and many are in decline (Butchart et al. 2004;

Sanderson et al. 2006; Szabo et al. 2012). Crucially, migratory species depend on a chain of intact habitats across the areas traversed during their migrations, making them vulnerable to habitat degradation or loss at any part of their annual cycle. The often large scale movements of migrants and nomads require us to account for spatial dependencies in the way we prioritise conservation across species; in the design of conservation networks; and in the implementation of conservation actions. This thesis directly addresses the first two of these, evaluating current practice and designing new approaches to both. There are currently only a few examples of conservation planning specific to migratory or nomadic species (Martin et al. 2007; Grantham et al. 2008; Klaassen et al. 2008; Sawyer et al. 2009; Sheehy et al. 2011; Singh & Milner-Gulland 2011; Iwamura et al. 2013) though the theoretical framework for dealing with these issues is improving rapidly (Hodgson et al. 2009; Hole et al. 2011).

In this thesis I incorporate migratory movements into conservation planning in three distinct ways. Firstly I evaluate the representation of migratory species in the global protected area network (Chapter 2). Secondly, using Australian nomadic birds as a case study, I explore the impacts of migratory movements on extinction risk assessment and conservation planning. In Chapter 3 I explore how movements affect extinction risk metrics and the way we prioritise conservation for highly mobile species, developing guidelines for better risk assessment. In Chapter 4 I examine the impacts of movements on the design of conservation networks (spatial prioritization) for data-poor migratory and nomadic species. Finally, I suggest tools and approaches that will help solve the problem of how to incorporate migratory connectivity into conservation planning (Chapter 5). In doing so I consider the full range of animal movements, developing approaches applicable to mobile species from predictable classic migrations to the irregular movements of nomads and the seasonal fluctuations of dispersive and irruptive species.

1.1 The challenge of conservation planning for migratory species

Migratory species rely on multiple sites throughout their lifecycle; the breeding and non-breeding grounds, and the corridors and stopover sites that connect them. Their population persistence therefore depends on a whole suite of sites remaining intact, the resources they require at those sites being available at the right time and the connections between sites remaining within reach given a species’ movement capability (summarised by the term ‘migratory connectivity’; Webster et al. 2002). It is these critical factors that make migratory species uniquely vulnerable, and present significant challenges for the conservation of migratory species.

1.1.1 Accounting for migratory connectivity

Movement among sites makes it impossible to evaluate the conservation value of any particular location in isolation for a migratory species, because its value also depends on the intactness of other locations used by the species. In the extreme, if all individuals of a species regularly move between two areas, as is the case with many classic migrants, the conservation status of the area in more critical condition (i.e. characterized by a lower carrying capacity or where reductions in birth rate or survivorship are greater) will dictate the overall status of the species (e.g. Figure 5.1). Conservation measures taken in the less impacted area could be redundant, wasting resources and missing the opportunity to benefit the species. Therefore, for migratory species the consequences of conservation actions (such as the designation of a protected area) taken in one place depend on the magnitude of threats and the success of actions taken elsewhere (Martin et al. 2007; Iwamura et al. 2013). For example, shorebird numbers in the East Asian-Australasian Flyway (EAAF) have dropped dramatically in the past few decades and evidence is pointing to habitat loss from tidal wetland loss at key stopover sites in the Yellow Sea (MacKinnon et al. 2012). If this hypothesis is correct then action to manage shorebird habitat elsewhere in the flyway will fail to halt the decline of these birds without corresponding management of habitat at stopover sites in East Asia. Conservation of migratory species thus remains a complex multi-jurisdictional problem, requiring collaborative solutions (Bull et al. 2013; Levin et al. 2013).

Large scale conservation initiatives struggle to address migratory connectivity, despite considerable focus on the specific conservation needs of migratory species in the literature. For instance the US National Fish, Wildlife and Plants Climate Adaptation Strategy (Small-Lorenz et al. 2013) does not address the needs of migratory species in climate change vulnerability assessments. Similarly, despite being responsible for managing a large number of charismatic migratory species, the US National Parks Service has yet to develop a comprehensive plan to deal with migratory species (Berger et al. 2014).

Choosing areas to protect or manage for migratory species requires an approach that accounts for movement processes rather than a simple focus on distribution patterns based on static maps of species occurrences or models of their realised distribution (Rondinini et al. 2006). Choosing conservation areas for sedentary species commonly involves identifying the locations that collectively, at least cost, contain the greatest number of species or largest amount of suitable habitat (Moilanen et al. 2009). Site selection for migratory species is necessarily more complex.

First, calculating the spatial configuration of sites may involve not just one type of habitat or resource, but several, which need to yield suitable resources at the appropriate time and to have the right spatial configuration. In Chapter 2 I examine how well these different parts of the annual cycle are represented in the global protected area estate, using migratory birds as a case study.

1.1.2 Effectiveness of protected areas as a tool for conservation of migratory species

While we still have no empirical analysis of whether migratory species are more or less threatened than non-migrants, given the evidence for decline in migratory species across the globe, conservation approaches will need to adequately meet the needs of migrants. One of the best tools we have for conservation is protected areas (McNeely 1994; Chape et al. 2005; Greve et al. 2011; Joppa & Pfaff 2011).

Despite increasing human pressures on the landscape, nations across the globe have committed to an expansion of the protected area network (Jenkins & Joppa 2009), with the target for coverage by the world’s protected areas recently being raised by the Convention on Biological Diversity (CBD) to 17% by 2020 (CBD 2011). Historically, concerns such as biodiversity representation have played a limited role in protected area designation, though this is now changing and biodiversity targets are well embedded in protected area network design (Margules & Pressey 2000; CBD 2011). However, protected areas tend to be placed in areas without competing land uses (Pressey 1994; Joppa & Pfaff 2011) and do not yet provide significant protection for many species and ecosystems (Rodrigues et al. 2004a; Joppa & Pfaff 2011; Venter et al. 2014). Until recently, systematic conservation planning has typically assumed that conservation features are static entities, ignoring the dynamics of species and habitat distributions (Rodrigues et al. 2004a; Watson et al. 2011; Small-Lorenz et al. 2013; Berger et al. 2014; Venter et al. 2014).

In Chapter 2 I investigate how well the current protected area estate represents all global species of migratory birds, taking into account their need for protection across their full annual cycle. I discover that less than 9% of migratory birds meet standard representation targets in the current global protected area network. This contrasts dramatically with the 45% of non-migratory species that meet a similar representation target, and highlights an urgent need for spatially targeted investment to ensure the long term persistence of migratory species.

1.1.3 Protection of resources and connectivity is essential

Understanding patterns of resource use and the connectivity between sites is important for effective conservation planning for migratory species (Webster et al. 2002; Martin et al. 2007; Bolger et al. 2008; Fynn & Bonyongo 2011). For example, Martin et al. (2007) discovered that a strategy that aimed to maximise the number of migratory American redstarts (Setophaga ruticilla) protected in the nonbreeding range resulted in protection gaps for certain sub-populations in the breeding range. In contrast, when the aim was altered to take into account migratory connectivity by adding the constraint of retaining a prescribed minimum number of birds within each breeding area, the species was protected more equitably across its entire range.

Important sites for migratory species can be easily overlooked because they are not continuously occupied, especially sites that may be occupied for only a few days a year or on rare occasions of resource shortage elsewhere in the species’ distribution. Some nomadic species use individual sites as rarely as once a decade, although the site may remain critical during severe drought and ultimately essential for the long-term persistence of a species (Fynn & Bonyongo 2011). For many parts of the world, including Australia, climate variability and severity of drought are predicted to increase in the coming decades (Cleugh et al. 2011). As rainfall patterns shift with climate change, even protected areas that currently perform well such as the Serengeti ecosystem may fail as resource and migration patterns will undergo spatial and temporal shifts (Fynn & Bonyongo 2011; Singh & Milner-Gulland 2011). Conservation strategies that fail to take these rarely-used and shifting sites into account risk failure. In Chapters 3 and 4 I evaluate the impact of transiently- occupied sites on conservation practice and outcomes in Australian nomadic birds, and develop approaches to incorporate the impacts of these dynamics into conservation planning.

Migratory species can collapse to extinction surprisingly quickly. Conversion of key refugia for the once abundant Rocky Mountain grasshopper (Melanoplus spretus) resulted in the extinction of a species that once existed in the USA in its trillions, and was thought to be an ineradicable pest (Lockwood & DeBrey 1990). Similarly, the passenger pigeon (Ectopistes migratorius) was once the most abundant bird in the world, with flocks of birds covering the sky for miles, yet recent research suggests that human exploitation coincided with natural population fluctuations to drive the species to extinction in just 50 years (Hung et al. 2014).

The converse of this vulnerability is that the dependence by migratory species on relatively small spatial areas means that remedial conservation action to restore connectivity can rapidly yield positive results. For example, migrations of the Damara zebra (Equus burchelli antiquorum) in Botswana along traditional pathways spontaneously recommenced a short time after fences were removed to restore connectivity (Bartlam-Brooks et al. 2011).

1.2 How well are migrants faring?

The general prediction that migratory species are acutely vulnerable to habitat loss is borne out at least by anecdotal observation, with many migrants in sharp population decline around the world (Butchart et al. 2004; Sanderson et al. 2006; Szabo et al. 2012). Climate change, habitat loss, human encroachment and over-exploitation are impacting migratory and nomadic species across the globe (Sanderson et al. 2006). These threats are by no means unique to migratory species, but their movements add additional vulnerability through both temporal and spatial disruption of access to resources (Robinson et al. 2009). The sharpest rates of increase in observed extinction risk in Australian birds (measured using the Red List Index) were recently found to occur among two orders dominated by migratory species, the seabirds and shorebirds (Procellariiformes and Charadriiformes; Szabo et al. 2012).

Overharvesting, habitat loss and human encroachment have disrupted traditional migration patterns with subsequent population crashes (Berger et al. 2008; Bolger et al. 2008; Harris et al. 2009). Large declines have been seen in populations of migratory ungulates worldwide, even species that appear to be well represented in reserves and protected areas (Berger et al. 2008; Bolger et al. 2008; McGowan et al. 2011). Analyses of these declines has revealed that protected areas supporting migratory ungulates often fail to represent crucial resources, such as breeding or wintering grounds, and traditional migration routes needed by these animals (Bolger et al. 2008; Fynn & Bonyongo 2011). For example, agricultural expansion around the Tarangire National Park in northern Tanzania has restricted access to traditional migration routes for wildebeest (Connochaetes taurinus), hartebeest (Alcelaphus buselaphus) and oryx (Oryx gazelle) in the region (Bolger et al. 2008). These three species have declined by up to 95% between 1988 and 2001. Even relatively intact migratory routes face imminent disruption from continued human-induced changes to landscape and climate (Fynn & Bonyongo 2011; Singh & Milner-Gulland 2011).

Mobile species, by virtue of their ability to move around the landscape, might be considered less affected by global change than sedentary species, with the ability to simply shift migration routes or timing to avoid resource bottlenecks. Certainly, migratory species are acutely responsive to small changes in conditions. Amongst some of the best known migrants, the birds and mammals, changes in climate have already led to changes in direction, timing and degree of movement (Lehikoinen et al. 2004; Beaumont et al. 2006; Chambers 2008) and distributional shifts (Griffioen 2001; Chambers et al. 2005; Robinson et al. 2005; Maclean et al. 2008; Rakhimberdiev et al. 2011). Such changes can also lead to phenological mismatch between predator and prey (Coppack & Pulido 2004; Lehikoinen 2011; Saino et al. 2011). In an example of a timing mismatch, populations of a long-distant migrant, the pied flycatcher (Ficedula hypoleuca) have declined by 90% in the past two decades (Both et al. 2006). These birds overwinter in sub-Saharan Africa, returning to Europe to breed in spring. One of the main sources of food for their nestlings are caterpillars, which have been quick to respond to increases in temperature by pupating earlier in the season. While the flycatchers have begun arriving sooner, perhaps in response to this shifting cue, in many regions they are still breeding too late to match the peak emergence of caterpillars, with consequent declines in nesting success (Both et al. 2006). The ability of migratory species to adapt to global change will depend on degree of coupling in migratory signals between different parts of their range, rapidity of change, physiological capacity for change and genetic scope for evolution. While some species may have the capacity to respond to global change, many others will require our help to do so.

1.3 Conserving the phenomenon of migration

Disruption to migration affects not just the migrants themselves but the ecosystems they support (Bauer & Hoye 2014). Migratory species have a large impact in many ecosystems, whether on nutrient cycling, bio-control or vegetation structure across the globe (Freese et al. 2007; Whelan et al. 2008; Wilcove & Wikelski 2008; Maas et al. 2013; Green & Elmberg 2014). Declines in migratory species can result in disruption to these ecosystem services with impacts on both the natural and agricultural environment in Australia and elsewhere. For example, the crash in North American migratory salmon has been estimated to have reduced the marine-derived nitrogen and phosphorus biomass entering the forests of the Pacific Northwest by 93-95%, resulting in a nutrient deficit and associated ecological changes (Gresh et al. 2000). Migrating bogong moths (Agrotis infusa) annually transport massive amounts of nutrients and energy (almost 5000GJ) into the Snowy Mountains of Australia, and are a major source of food for bats, birds, mammals and fish (Green 2011). Birds play a major role in pollination in Australia and declines in birds in the Mt Lofty

8 region of South Australia are already limiting seed production of the bird-pollinated shrub Astroloma conostephioides (Paton 2000). The importance of migratory species in regulating ecosystems makes conserving migration as a phenomenon as important as conserving the species themselves.

1.4 The ecology of migration

Migration is a widespread strategy, and a significant proportion of the world’s taxa undergo large- scale movements at some stage in their lifecycle (Robinson et al. 2005). Migratory species encompass divergent taxa including birds, fish, mammals, insects and crustaceans. It is estimated that over 25% (approximately 2600 species) of the world’s bird species undergo some form of seasonal movement, spanning every continent and most landscapes (Cox 2010). Migratory species comprise an estimated 1% of terrestrial mammals, 30% of bats, 36% of marine mammals and all 7 species of sea turtles (Robinson et al. 2009). However, movements can often be cryptic, particularly in the less well known species and these numbers probably underestimate the extent of movement as a life history strategy.

Within Australia, it has been estimated that some form of migration occurs in almost 40% of landbird species (Chan 2001). It is likely that many more patterns of movement remain undocumented due to the prevalence of cryptic movements, partial migration and small scale movements within the Australian continent (Chan 2001; Griffioen 2001; Green 2006).

Migration and nomadism may function as strategies to avoid predation, track food resources, avoid environmental extremes and minimise competition for limited resources, both intra- and inter- specific (Berthold 2001; Cox 2010). Movement can range from local scale altitudinal movements up and down a mountainside with changing seasons, to epic ten thousand kilometre feats of endurance (Figure 1.1). In addition, movements can range from predictable to-and-fro movements, to nomadic patterns that are hard to understand or predict. Commonly labelled by terms such as migration, nomadism, irruption and dispersal, movements span a wide spectrum of scales and behaviours.

Classic migration Altitudinal migration Partial migration

Popn A

Popn A’

Movement from breeding to non-breeding Movement from breeding to non-breeding The situation where one sub-population grounds. May be long or short distance and ground. In eastern Australia often associated migrates (A’) and another is sedentary (A). may involve single journey or a number of with movement from the Great Dividing Range Suspected to be a common strategy in short hops. Examples include orange-bellied to coastal areas. Shown by rose robin and Australian species, e.g. grey fantail and parrot and eastern koel eastern spinebill. silvereye

Differential migration Leap-frog migration Dispersal

♂ Juv ♀

A poorly-known strategy where adult males, Movement where sub-populations at higher Movement often undirected, associated with females and juveniles migrate to different latitudes migrate to lower latitudes than juveniles leaving birthplace. Juveniles can later places or at different times. Several shorebirds populations at intermediate latitudes. Probably be sedentary or migratory. Probably a common show this pattern, e.g. grey plover and lesser occurs in Australia but no clear examples yet. tactic for most species at a range of distances. sand plover.

Nomadism Loop migration Irruption

The most difficult movement pattern to define Migration follows a looping rather than direct Irregular movements from breeding ground, and map. Often defined as irregular movement route from breeding to non-breeding grounds. usually in response to low food supplies and in response to shifting resources. Often no Found in yellow-faced honeyeater movements. high population densities. Examples include defined breeding area or season. Common barn owl, letter-winged kite and black falcon. tactic in arid Australia, e.g budgerigar* and pied honeyeater. *may display seasonal component to movements

Figure 1.1 Types of migration and Australian examples

Global bird biodiversity is centered on the tropics (Orme et al. 2005; Somveille et al. 2013; Figure 1.2). Contrastingly, classic (or full) migration, the seasonal to-and-fro movement most common in the northern hemisphere, is strongly associated with temperature regions (Figure 1.3a). Altitudinal migratory species occur along the world’s large mountain ranges (Figure 1.3b). Nomadism is almost exclusively a southern hemisphere phenomenon, where complex interacting weather patterns drive dramatic inter-annual fluctuations in resource availability (Figure 1.3c).

Figure 1.2 Global bird species richness, as measured by the number of species occurring with each ~23,332 km2 hexagon. Reproduced from (Somveille et al. 2013)

1.5 Nomads: Extreme migrants

Nomadism is one of the least known forms of migration, and most difficult from a conservation planning perspective. Nomadic species move around the landscape in complex and irregular (though not necessarily unpredictable) patterns, commonly associated with highly fluctuating resources (Dean 2004).

The movement patterns of nomadic birds, their responses to changes in environmental conditions and even the definition of nomadism are still not well understood and are much debated. There are two conflicting hypotheses concerning the patterns and drivers of nomadism. Firstly, that nomadic movements track resource hotspots in response to lack of resources (Wyndham 1982; Cheke et al. 2007; Perfito et al. 2007). There is some empirical support for this hypothesis. Movements in red- billed quelea (Quelea quelea) are rainfall driven, coinciding with peaks in green and ripening grass seeds (Ward 1971; Cheke et al. 2007). Similarly, letter-winged kite (Elanus scriptus), of the Australian outback, track rainfall-driven irruptions of native rodents (Pavey & Nano 2013). However, this may reflect a simplistic view of a complex phenomenon and in a study in the arid Southern Karoo region of Africa, Dean and Milton (2001) observed that the presence or absence of nomadic birds was not significantly correlated with seed or with rainfall at a site and occurrence in any one location may often be driven in a large part by changes in resources elsewhere.

A second hypothesis is that nomads undertake random movements in good conditions and contract to refugia when conditions degrade (Bennetts & Kitchens 2000). In extreme conditions such as severe drought, previous knowledge about the location of resources and refugia would confer significant advantage over continuing to undertake random movements, and gaining this knowledge in times of high landscape-wide food availability will have lowered cost to the individual. Tracking experiments on snail kites (Rostrhamus sociabilis) did find that movement probabilities were higher in times of high food availability, consistent with elevated exploratory behaviour (Bennetts & Kitchens 2000). In a widely cited theoretical analysis of the profitability of site tenacity versus nomadism, it was predicted that nomadic behaviour was favoured as good years occur less frequently, and when resource fluctuations are cyclic rather than random (Andersson 1980), indicating that each of the two hypotheses may have validity under different environmental conditions.

Nomadism is predominantly a southern hemisphere phenomenon (Figure 1.3c), and particularly common among Australian birds, where boom-bust cycles drive resource fluctuations. Rather than displaying random opportunistic movements in response to resource fluctuations, Australian nomads such as finches and honeyeaters may display seasonal directionality and endogenously controlled behaviour consistent with that of the long-distant migrants of the northern hemisphere (Wyndham 1980; Wyndham 1982; Davies 1984; Munro et al. 1993; Dallimer & Jones 2002; Cheke et al. 2007). Previously thought to be resource-tracking nomads, budgerigar (Melopsittacus undulatus) have been found to display a partial north-south movement pattern, loosely coinciding with rainfall (Wyndham 1982) as have emus (Dromaius novaehollandiae) in Western Australia (Davies 1984). While arid environments are variable in short-scale locations and amounts of rainfall, weather in the different regions in Australia is driven by larger climate patterns such as the El Niño-Southern Oscillation (ENSO) (Meyers et al. 2007; Risbey et al. 2009). Successful exploitation of arid regions conceivably favours populations able to recognise and respond to these patterns.

Conserving nomadic species presents an extreme challenge. Unlike conventional migration, the sites and linkages of nomads are dynamic, changing from year to year and season to season. There are two main issues faced in conservation planning for nomadic species in Australia. Firstly, the extreme variability in climate from year to year combined with a relatively small human population, means that we know very little about the movements of most of our nomadic species (Chan 2001; Tulloch et al. 2013a). Many Australian avian migrants show some form of nomadism where movements track complex patterns of changing resources (Chan 2001; Griffioen 2001). This complexity makes predicting those movements extremely difficult, and most movements are currently only understood from patchy, anecdotal observation rather than synthetic analyses. Secondly, it is unknown to what extent a conservation strategy that takes account of nomadism differs from one simply assuming the species are sedentary, with only a single study globally addressing conservation planning for nomadic species (Fahse et al. 1998). Fahse et al (1998) examined configurations of theoretical protected areas for a nomadic lark in the Nama-Karoo, South Africa by using a spatio-temporal model to estimate the survival of flocks given known ecological relationships to seasonal rainfall patterns. Their study sought to inform the single-large-or-several- small (SLOSS) debate on optimal protected area configuration rather than a systematic conservation plan, and therefore did not incorporate cost or other feasibility metric.

A number of previous studies have examined the literature and used modelling approaches to identify broad migration and movement patterns in Australian birds (Taplin 1991; Chan 2001; Griffioen & Clarke 2002; Reside et al. 2010). These have improved our understanding of the migration strategies of a number of species; however specific information on movement patterns for many species remains unknown. The most comprehensive of these studies was undertaken almost a decade ago (Griffioen 2001) and a considerable amount of data has been collected since then. There have been major advances in species distribution modelling during this period, and in Chapters 3 and 4 I use these new techniques, combined with an extra decade of data to develop the first temporally dynamic distribution maps for Australia’s nomadic birds. I then use these models to assess geographic range dynamics in nomads and explore strategies for their effective conservation.

1.6 Conservation planning for nomadic species

Given financial and practical constraints, we are often not able to take action at all the sites that are used by a migratory species, so we must prioritise carefully. How do we choose where and on which species to focus our conservation investments for nomadic species?

1.6.1 Species prioritisation in nomadic species

Extinction risk estimates, such as the IUCN Red List (IUCN Standards and Petitions Subcomittee 2014), provide one of the foundations for prioritizing conservation actions (Joseph et al. 2009). Estimates can be based directly on population metrics (population decline and small population size) or on risk surrogates such as geographic range size. Lack of accurate abundance metrics for many species means geographic range size remains the most common way to diagnose extinction risk, with almost 50% (2,072 of 4,440 species) of threatened mammals, birds, amphibians and gymnosperms qualifying on the basis of geographic range size criteria (Gaston & Fuller 2009).

Measures of geographic range size are typically based on a conceptualization of geographic range size as a fixed attribute of a species (Gaston & Fuller 2009). In metrics such as extent of occurrence (EOO) or area of occupancy (AOO), species with smaller extents or areas are assumed to be at greater risk of extinction (Gaston & Fuller 2009; IUCN Standards and Petitions Subcomittee 2014). These approaches generally function well for sedentary species but for migratory species, whose distributions fluctuate across both time and space, the assumptions behind these metrics rarely hold as the quantity being measured (geographic range size) is itself changing through time. Measures of such mobile species’ geographic range size based on collations of data from different time periods

14 will generally be larger than the geographic range size at any one point in time potentially leading risk assessors to believe a species is secure from threat (unless alternative population trend data are available). However, the distributions of migrants and nomads can be substantially constricted at certain points in time, impacting persistence (Pimm et al. 1988). Current IUCN guidelines allow for these bottlenecks to be considered in extinction risk evaluation through both the inclusion of ‘extreme fluctuations’ in criteria B2 and the definition of AOO as ‘the smallest area essential at any stage to the survival of existing populations of a taxon’ (IUCN Standards and Petitions Subcomittee 2014). Application of these criteria is limited by gaps in our knowledge of population fluxes and temporal distributions, particularly in nomadic species where complex movements make monitoring and mapping difficult.

In Chapter 3 I present a method for mapping the dynamic distributions of data-poor nomadic species. I examine the impacts of dynamics on extinction risk in Australian nomadic birds, discovering the presence of bottlenecks in their distribution, putting them at higher risk of extinction than we currently realise. I integrate these findings into guidelines for better extinction risk evaluation in nomadic and other highly mobile species. In Chapter 4, I measure the impact of dynamic distributions on protected area network design, comparing an objective of protecting the species’ overall distribution across time to an objective of protecting a series of distributions at discrete points in time. I outline an approach that can be used to spatially prioritise nomadic species, even in data-poor species and regions, in the absence of information on migratory physiological capabilities.

1.7 Tools for conservation planning of migratory species

Understanding how populations are genetically and geographically linked between periods of the annual cycle (i.e. migratory connectivity), or throughout their lifetime for nomadic and irruptive species, will be critical to successful conservation planning approaches for migratory species (Webster et al. 2002; Martin et al. 2007; Bolger et al. 2008; Buler & Moore 2011; Fynn & Bonyongo 2011). Effective conservation planning for migratory species therefore requires information on the location of sites and their functional significance, as well as tools to incorporate that knowledge into prioritisation approaches. Information on migratory connectivity has been incorporated into conservation planning in both the marine (Moilanen et al. 2008; Linke et al. 2011) and terrestrial realm (Martin et al. 2007; Klaassen et al. 2008), though good worked examples remain few.

Much of the focus of research effort on migratory species has been on intensive collection of information on connectivity, habitat suitability and demographic data, with many authors calling for more information (Webster et al. 2002; Faaborg et al. 2010; Robinson et al. 2010). However, with time running out for many migratory species, and with so many species affected by human impact, there is an urgent need to develop conservation planning approaches that can be applied cheaply and rapidly across large numbers of species using readily available data.

In Chapter 5 I discover that we know more than we think when it comes to conservation planning for migratory species. By drawing on ideas from diverse fields including artificial intelligence, network theory and decision theory, I outline approaches that already allow us to make good conservation planning decisions for migratory species. I discover that while we are yet to apply them, we already have many of the tools and approaches we need to incorporate migratory movements into conservation planning practice.

1.8 Thesis overview

This thesis explores the impacts of migratory movements on conservation planning. In Chapter 2 I evaluate the impacts of those movements on the effectiveness of the current protected area estate for migratory birds. In Chapters 3 and 4 I determine the impact that movements have on our current approach to two aspects of conservation planning: the way we prioritise actions across species (extinction risk evaluation, Chapter 3) and the design of conservation networks (spatial prioritisation, Chapter 4). In Chapter 5 I examine the theory behind conservation planning for migratory and nomadic species, and discover tools and approaches to help conservation practitioners better incorporate migratory movements into conservation planning practice.

Chapter 2

Protected areas and the world’s migratory birds

2 Protected areas and the world’s migratory birds

2.1 Abstract

Migratory species depend on a suite of interconnected sites throughout their life cycle. Threats to unprotected links in these chains of sites are driving rapid population declines of migratory species around the world (Harris et al. 2009; MacKinnon et al. 2012; Sanderson et al. 2006), yet the extent to which different parts of the annual cycle are covered by protected areas remains unknown. Here we show that barely 9% of 1451 migratory birds meet standard species-specific protection targets across all phases of their annual cycle (i.e. the proportion of their distribution covered by protected areas in breeding areas, non-breeding grounds and while on migration). In contrast, 45% of non- migratory birds meet equivalent targets. Substantial variation in protected area coverage means that migratory birds that are well protected in one nation might be more poorly covered as they traverse other nations. Furthermore, we found that just 22% of Important Bird and Biodiversity Areas identified for migratory bird species were completely covered by protected areas, with those sites important for birds while on migratory journeys between breeding and non-breeding grounds being least well covered. If migratory species are to survive in a rapidly changing world, they must be protected throughout their migratory cycle and their requirements must be integrated into ongoing efforts to meet internationally agreed targets for protected area expansion. This will necessitate better-targeted investment and enhanced coordination among countries.

2.2 Introduction

From the writings of Aristotle (Historia animalium), to the musings of Gilbert White in Georgian England (White 1789), migratory birds have fascinated and inspired people for generations. Migrants undertake remarkable journeys, from single endurance flights of more than 10,000 km in the bar-tailed godwit Limosa lapponica (Battley et al. 2012) to the lifetime relay of arctic terns Sterna paradisaea, which fly the equivalent distance to the moon and back three times during their lives (Egevang et al. 2010). Migratory species make major contributions to resource fluxes, biomass, nutrient transport, predator-prey interactions and food-web structure within and among ecosystems (Bauer & Hoye 2014), and play a substantive role in human culture. It is therefore deeply concerning that more than half of migratory birds across all major flyways have declined significantly over the last 30 years (Kirby et al. 2008).

Protected areas remain one of the most effective conservation approaches in the conservation manager’s toolkit. They are effective at minimizing population decline and averting species loss; by reducing the threats of habitat loss, and to a lesser degree habitat degradation, hunting pressure, and disturbance (Bruner et al. 2001; Andam et al. 2008; Joppa & Pfaff 2011). As well as allowing for the conservation of wildernesses (Watson et al 2009), the IUCN protected area framework allows for sustainable land use to occur in combination with nature conservation (Dudley et al. 2010).

Threats in any one part of a annual cycle can impact the entire population of a migratory species (Martin et al. 2007; Iwamura et al. 2013, Runge et al. 2014), and so environmental management actions for migratory species need to coordinated across habitat types, seasons and jurisdictions (Bull et al. 2013; Runge et al. 2014). Yet the extent to which the distributions of migratory species are covered by protected areas globally is poorly understood. Many previous global and regional species conservation assessments and prioritization analyses either omit parts of the annual cycle or treat all species’ distributions as static (Rodrigues et al. 2004a; Watson et al. 2011; Le Saout et al. 2013; Small-Lorenz et al. 2013; Berger et al. 2014).

Here we explore how protected area coverage of migratory birds varies across the annual cycle and across countries, and compare their current levels of protection against standard protection targets. Overlaying maps of protected areas onto distribution maps of the world’s birds, we assess whether the proportion of each species’ distribution covered by protected areas met a target threshold (Beger et al. 2010; Rodrigues et al. 2004; Venter et al. 2014). For migratory species, we set targets for each stage of the life-cycle separately, including only the 1451 species with mapped distributions throughout their annual cycle.

Widespread migrants may benefit more from broader scale policy responses (e.g. targeting forestry and agriculture planning and practices) than individual site-based interventions (Boyd et al. 2008). However, for nearly all bird species worldwide for which site-based conservation is appropriate and needed, key sites—Important Bird and Biodiversity Areas (IBAs)—have been identified so it is useful to assess protection levels for those sites (Birdlife International 2014b). IBAs are sites that are significant for the global persistence of biodiversity, identified using data on birds. Four criteria relating to threatened, restricted-range, biome-restricted and congregatory species are used to identify IBAs, with over 12,000 sites identified to date. We evaluated protected area coverage of IBAs identified for the same set of species included in our analyses based on broad-scale distribution maps.

2.3 Methods and data

2.3.1 Birds

Digitised bird distribution maps (9891 extant species) were obtained from BirdLife International and NatureServe (2012). The seasonal parts of the annual cycle (resident, breeding, non-breeding and passage) were mapped separately and the analysis was limited to polygons where the pattern of seasonal occurrence is known, where the species is extant or probably extant, and with a native or reintroduced distribution. Species with IUCN Red List categories of Extinct, Extinct in the Wild, Critically Endangered (Possibly Extinct or Possibly Extinct in the Wild) or Data Deficient were excluded from analysis, as were migratory species with only one mapped part of the annual cycle (one polygon). Following BirdLife International (2012), we classified the world’s birds into non- migrant (7457 species), full migrant (1451 species), altitudinal migrant (341 species), or nomad (182 species). We excluded altitudinal migrants and nomads for the purpose of this analysis because their movements are unclearly mapped. BirdLife International describe a species as a full migrant where ‘a substantial proportion of the global or regional population makes regular or seasonal cyclical movements beyond the breeding range, with predictable timing and destinations’. This includes species that may be migratory only in part of their range and we follow BirdLife International and NatureServe (2012) classification of seasonal distribution into resident, breeding, non-breeding and passage. We also followed BirdLife International (2012) to classify species into seabird (both pelagic and coastal; 318 species) and non-seabird (8590 species).

2.3.2 Protected areas

Protected area boundaries were obtained from the World Database of Protected Areas (IUCN & UNEP-WCMC 2013) in February 2013. Due to omissions in this dataset, we augmented it with 12754 and 10913 protected areas from Estonia and Russia (using data available from Protected Planet during 2013; www.protectedplanet.net) and 49 marine protected areas in Australia (available at http://www.environment.gov.au/metadataexplorer). We excluded UNESCO Biosphere Reserves as they may contain large areas not considered protected (Coetzer et al. 2013), proposed sites and sites with unknown designation status but included all other nationally and internationally designated sites. Protected areas with a point locality and reported extent were converted to circular buffers of the appropriate area, assuming the point represented the centroid of the protected area (13453 sites). We excluded protected areas that lacked both polygons and reported extent (6959

20 sites). Any overlapping polygons in the protected areas dataset were spatially dissolved. National boundaries were obtained from Global Administrative Areas V2.0 (www.gadm.org) and Exclusive Economic Zones obtained from Marine Boundaries Geodatabase version 6 (www.marineregions.org).

2.3.3 IBAs

Digital boundaries and tabular data on IBAs were obtained from BirdLife International (2014b). We included both confirmed and proposed IBAs (the large majority of the latter will be confirmed once regional reviews are complete). IBAs without spatial information (polygon or point locality) were excluded from the analysis (817 IBAs). IBAs for species with IUCN Red List categories of Extinct, Extinct in the Wild, Critically Endangered (Possibly Extinct or Possibly Extinct in the Wild) or Data Deficient were excluded from analysis as were IBAs for species without mapped distributions (39 species). Where unspecified, the seasonal occurrence of species triggering IBA identification was determined by intersecting the IBA boundaries with a 200-km buffered polygon of the species distribution, and expert review; 344 IBAs for 131 species that could not be assigned to a seasonal range were excluded from the analysis. In total, of the 4743 species in the tabular data, 99 were excluded from this analysis.

2.3.4 Analysis

We projected all spatial data into a Behrmann equal area projection, and processed them using Python v2.6.5 (www.python.org), ESRI ArcGIS v10.0 (ESRI 2013), and R v3.1.0 (www.r- project.org). Species with a small geographic range size are expected to be at greater risk of extinction than those with large geographic distributions (Gaston & Fuller 2009; Harris & PIMM 2007). We therefore measured the proportion of each species’ geographic distribution that overlapped protected areas and compared this against a representation target that required 100% of a distribution to be covered by protected areas where the geographic range was <1,000km. The representation target was reduced to 10% where the range size was >250,000km and log-linearly interpolated between these two thresholds (Rodrigues et al. 2004; Watson et al. 2011; Venter et al. 2014). Targets were evaluated (i) individually for each part of the annual cycle shown by each species, and (ii) across the entire distribution of each species, dissolving all parts of its range into one. The first approach allowed us to investigate how protected area coverage varied across the

21 annual cycle, and to measure how many parts of the annual cycle met the target. We measured the proportion of each IBA that overlapped protected areas.

2.4 Results

2.4.1 Protected area coverage of migratory species

We found that 91% of migratory bird species have inadequate protected area coverage for at least one part of their annual cycle, despite individual elements of the annual cycle being well protected for some species (Table 2.1). This is in stark contrast to the 55% of non-migratory species with inadequate protected area coverage across their global distribution. Twenty-eight migratory bird species have no coverage in at least one part of their annual cycle, and 18 of these have no protected area coverage of their breeding range. Two species lack any protected area coverage across their entire distribution. The number and proportion of species meeting targets both within and outside national borders are listed for each country in Appendix 1. Not every part of the annual cycle (resident, breeding, non-breeding and passage) has been mapped for every migratory species, and the numbers in the final column indicate the number of species for which that part of the cycle has been mapped.

Table 2.1 Mean rates of protection for migratory and non-migratory bird species, number of gap species and proportion of species meeting targets for protected area coverage. Rates of protection are calculated as the proportion of the species distribution contained within protected areas. Gap species are those where none of their distribution is held within protected areas, with ‘any part of cycle’ being the number of species which lack protection in any one of their resident, breeding, non- breeding or passage distributions. The rows below ‘Part of annual cycle’ refer to full migrants.

Number of Percentage of Mean % of range gap species species meeting Total number of covered (nil coverage) coverage targets species

Non migrants 18.9 243 44.8 7457

Full migrants 10.2 2 8.8 1451

Part of annual cycle:

Resident 11.3 3 43.7 898

Breeding 14.1 18 34.4 1260

Non-breeding 10.9 8 39.8 1267

Passage 13.4 2 26.2 530

Any part of cycle 28

2.4.2 Protected area coverage of Important Bird and Biodiversity Areas (IBAs)

IBAs show higher levels of protected area coverage than species distributions on average (Table 2.2). 22% of IBAs for migratory birds are completely covered by protected areas and an additional 41% are partially covered. IBAs are most often identified in the breeding grounds of species (77% of species with an IBA), yet for 40% of species the breeding range is the least well protected stage of the migratory cycle. IBAs along the migratory route from breeding to non-breeding areas are most likely to be incompletely protected, with only 16% being completely covered.

Table 2.2 Global protected area coverage of Important Bird and Biodiversity Areas (IBAs), shown for those migrants and non-migrants for which IBAs have been identified

Number of migratory species for % of IBAs Number (%) of which this is the Mean % of IBA % of IBAs Mean (median) species with at with any least protected area covered per completely number of IBAs least one IBA species (>=98%) covered coverage per species identified part of the range

Non migrants 55.8 24.0 41.7 16.0 (9) 3488

Full migrants (total distribution) 51.3 21.9 41.3 37.4 (14) 885

Full migrants (least protected part of range) 36.3 12.9

Part of annual cycle:

Resident 54.7 22.9 10.8 (5) 472 (53) 230

Breeding 51.4 24.2 22.4 (8) 684 (77) 358

Non-breeding 45.4 19.8 17.2 (6) 473 (53) 183

Passage 46.3 16.2 14.3 (5) 317 (36) 114

2.4.3 Protected area coverage of threatened species

We evaluated protected area coverage for species classified as threatened(BirdLife International 2012) (Critically Endangered, Endangered or Vulnerable on the IUCN Red List; Table 2.3) We find that threatened species are underprotected, with just 2.6% of threatened migrants having target levels of protected area coverage adequately across their full range. Threatened migrants have substantially lower levels of protected area coverage than threatened non-migrants (7.7% vs

24.1%). IBAs identified for threatened species have lower levels of protected area coverage than non-threatened species (Table 2.4).

Table 2.3 Global protected area coverage of threatened species

Number of gap species (nil protected area Percentage of species Mean % of range covered coverage) meeting targets Total number of species

All Threatened All Threatened All Threatened All Threatened

Non migrants 18.8 24.1 265 155 44.8 12.3 7457 1055

Full migrants (total distribution) 10.2 7.7 2 2 43.8 18.0 1451 156

Part of annual cycle:

Resident 11.23 5.8 3 2 43.7 5.6 898 72

Breeding 14.1 28.9 18 13 34.4 12.2 1260 139

Non-breeding 10.9 10.1 8 5 39.8 16.4 1267 122

Passage 13.3 10.1 2 0 26.2 22.2 530 27

Every part of cycle 8.8 2.6

Any part of cycle 28 17

Table 2.4 Global protected area coverage of Important Bird and Biodiversity Areas (IBAs), shown for those migrants and non-migrants for which IBAs have been identified; threatened species

Number of migratory species for which % of IBAs Number (%) of this is the least Mean % of IBA % of IBAs Mean (median) species with at with any protected part of area covered per completely number of IBAs least one IBA species (>=98%) covered coverage per species identified the range

Non migrants 46.0 18.0 41.7 7.9 (4) 858

Full migrants (total distribution) 43.7 19.3 35.2 35.2 (18) 161

Full migrants (least protected part of range) 28.6 10.3

Part of annual cycle:

Resident 54.7 25.6 15.6 (7) 75 (47) 32

Breeding 45.2 20.3 15.4 (5) 127 (79) 60

Non-breeding 41.6 21.7 15.1 (7) 117 (73) 48

Passage 40.3 12.6 14.1 (6) 55 (34) 21

2.4.4 Evaluation of representation targets for protected area coverage

Protected area coverage targets are negatively scaled with range size, and setting targets independently for each part of the annual cycle will increase the overall proportion of a species’ range required to meet its targets. To check that the low level of protected area coverage of migrants in comparison with non-migrants is not driven by the way we set targets, we explored the difference between targets based on the size of each part of the annual cycle with those based on summed total size of each species’ distribution (Table 2.5). The proportion of species meeting targets increases from 8.8% to 12.8% when the target is based on the total distribution size for that species (i.e. the same target is set across all parts of a species seasonal distribution combined). This difference is small by comparison with the difference between migrants and non-migrants (12.8 vs 44.8%), indicating that while the way we set targets for migratory species has a small effect on the proportion of species meeting those targets, it is the lack of coordination in protected area coverage across the annual cycle that drives the result.

Percentage of species Percentage of species meeting meeting targets (target targets (target designated by designated by total size Total number size of part of annual cycle) of distribution) of species Full migrants (total 43.8 1451 distribution) Every part of range 8.8 12.8 Part of annual cycle: Resident 43.6 50.5 898 Breeding 34.4 45.9 1260 Non-breeding 39.8 44.2 1267 Passage 26.2 38.3 530

2.4.5 Sensitivity analysis excluding seabirds

Globally, designation of protected areas in the marine realm lags behind coverage on land (15.4% terrestrial vs 3.4% marine area protected (Game et al. 2009), and as a result it is likely that fewer seabirds meet protection targets than non-seabirds. We evaluated protected area coverage for species classified as pelagic or coastal seabirds (BirdLife International 2012; Juffe-Bignoli et al. 2014) to explore whether the low levels of protection in migratory birds are driven purely by higher proportions of seabirds within the migratory birds category. The results of the analysis are shown in Table 2.6. Protection targets are met for just 3.3% of migratory seabirds. When we exclude the 244 migratory seabirds from the analysis, the proportion of migratory birds meeting targets rises slightly from 8.8% to 9.9%, indicating that there is a major shortfall in the protection of terrestrial migrants as well as pelagic species.

Table 2.6 Global protected area coverage and number of species meeting targets; seabirds

Seabirds Non-seabirds

Full migrants Non migrants Full migrants Non migrants

Total number of species 244 74 1207 7383

Mean % of range covered 5.9 16.7 11.1 18.9

Median % of range covered 3.5 5.6 10.0 13.5

Number of species meeting targets (%) 8 (3.3) 14 (18.9) 120 (9.9) 3246 (43.8) (designated by each part of annual cycle)

Number of species meeting targets (%) 46 (18.9) 20 (27.0) 587 (48.6) 3318 (44.9) (designated by total distribution)

2.5 Discussion

Our results highlight an urgent need to coordinate protected area designation across the annual cycle (Figure 1). For example, habitat loss is one of the key threats to the Vulnerable Red-spectacled amazon (Amazona pretrei), a migratory parrot of Brazil (BirdLife International 2014) yet less than 4% of this species’ distribution occurs within protected areas, with negligible coverage of its seasonal breeding and wintering areas (Marini et al. 2010). Similarly, the Endangered spotted ground-thrush (Zoothera guttata) of southern Africa is threatened by deforestation and human encroachment, yet just 1.5% of its breeding habitat is currently protected. Key migration routes for this species have yet to be identified, and such sites may soon be lost though ongoing fragmentation (BirdLife International 2014). Poor coverage is not limited to species with low abundance or narrow distributions. The great knot (Calidris tenuirostris) is a once abundant migratory shorebird now globally classified as Vulnerable (BirdLife International 2014), whose migration spans continents from Siberia to Australia. Just 6.8% of the area used while on migration, where the species congregates in high numbers, is covered by protected areas. Filling the protection gaps for these species throughout their annual cycle will be key to their recovery.

Because migratory birds move across international borders, achieving their protection is a shared responsibility. Some countries (such as France and Venezuela) meet targets for protected area coverage for more than 80% of their migratory bird species, while others (such as Libya and Madagascar) meet targets for none (Figure 2.1a). Nations across North Africa and central Asia stand out as having low protected area coverage of migratory bird distributions, whereas southern African nations have high protected area coverage. We also discovered wide variation in the proportion of migratory bird species occurring in each country that meet their protection targets overseas, a consequence of the migratory connections linking jurisdictions and continents (Figure 2.1b). For instance, Germany meets targets for protected area coverage for over 98% of migratory bird species occurring within its borders, but less than 13% of Germany’s migratory birds are adequately protected across their global range. This is not simply a case of wealthy nations losing natural heritage to poor nations. Many Central American countries (with low GDP) meet targets for more than 75% of their migratory species, but these species have low levels of protected area coverage in the more prosperous Canada and USA (Figure 2.1c). Within regions such as the European-African flyway, mechanisms such as those seen in climate negotiations (i.e. REDD+;

Venter et al. 2013) where funding flows to countries with the greatest conservation need may be one way to incentivize better global cooperation (Sultanian & van Beukering 2008).

28 bird species within each country meeting targets for each part of their migratory range globally and (c) the percentage area covered by protected areas in that country. Targets are scaled by the size of each part of the seasonal distribution. Note the difference in the range of the color ramp between the three maps.

Protected areas are usually designated at the national scale, but collaborative international partnerships and concerted inter-governmental coordination and action are crucial to safeguard migratory species (Kirby et al. 2008). A number of international agreements (e.g. Convention on the Conservation of Migratory Species of Wild Animals (CMS), Ramsar Convention on Wetlands) recognise the specific challenges associated with migratory species, and attempt to deliver special protection to migratory species. Migratory landbirds in particular lack coverage under flyway-based bird conservation instruments (Jones & Mundkur 2010), although this is now being addressed through initiatives such as the development of the African-Eurasian Migratory Landbird Action Plan under the CMS27. However, only 120 nations are Parties to CMS, and there is an urgent need to strengthen other agreements such as those between range states in specific migratory flyways. Internationally coordinated action (particularly within migratory flyways) through these and other mechanisms will require substantially greater international leadership and resourcing.

There are several caveats to this work. Firstly, for many migratory species with large geographic range size, even a modest target of 10% could entail protection of large areas, perhaps limiting the feasibility of achieving protection targets. That said, a large part of this target may be met if the Aichi target of protecting 17% of all ecological regions is achieved (Venter et al. 2014). Many of the large-ranging migrants occupy regions where both human impact and avian species richness is low, such as the extensive boreal forests and tundra of the northern hemisphere (Sanderson et al. 2002), and such habitats might not be the highest priorities for protected area expansion (Venter et al. 2014).

Secondly, we make the assumption that protected areas are a useful surrogate for conservation action. Whether or not protected area expansion is an appropriate action will relate to the conservation objective – whether we are trying to minimize species loss or, for instance, increase population size. Protected area expansion is extremely powerful tool for minimizing species loss, however it may need to be teamed with management actions where we have other conservation objectives such as increasing species population size. Protected areas are effective at managing many threats (Bruner et al. 2001; Andam et al. 2008; Joppa & Pfaff 2011), and where threats are not being adequately addressed the situation could in many cases be alleviated by improved

29 management (Le Saout et al. 2013). For some large-ranging migratory species conservation actions are perhaps best directed outside formal protected areas. For instance, intensification and mechanization of grassland management is a key threat to the migratory corncrake (Crex crex) which breeds in agricultural meadows across Europe, and conservation solutions will involve working with farmers to maintain habitat for the species (BirdLife International 2014). However, even in such cases, protected area designation can provide a useful legal and social framework for environmentally sustainable management of land (McNeely 1994).

Parts of species ranges can have differing ecological significance, and the population consequences and time spent in these different areas can vary (Hostetler et al. 2015). Knowledge on the spatial distribution of threats, and population dynamics across the full annual cycle can allow conservation to be prioritized in the region currently most affected by threats. However, our present state of knowledge makes this impractical for all but a tiny minority of species.

Claims of inaccuracy and commission errors are often raised in relation to these distribution maps, which represent extent of occurrence (EOO) rather than extent of suitable habitat (ESH). Recent work by Venter et al (2014) explored the sensitivity of protected area planning to map-based commission errors and found that while ESH maps cover on average 53% of the extent of the EOO maps (as used here) their results were robust to these differences, translating to just 6-8% difference in the final solution, driven in a large part by the higher targets for the smaller ESH maps. Similarly, Beresford et al. (2011) compared ESH and EOO maps for African birds and found that while ESH maps average 28% of the area of EOO maps, ESH maps did not affect accuracy (number of commission and omission errors) in half of species, and reduced accuracy in 16% of species. We welcome future improvements to the accuracy of these distributional maps, despite commission errors they present a valuable tool for eliciting global trends in conservation and that the findings of this chapter – that current and projected expansions to the global protected area estate will not be sufficient to protect migratory birds without targeted protection of sites and resources linked by migration – are sufficiently robust to guide shifts in protected area policy.

Conservation metrics based purely on protected area coverage may present an overoptimistic measure of conservation status in regions where migratory birds are subject to high threat levels once they move outside protected areas. Seasonal hunting of migrating birds (both legal and illegal) places populations passing through many European countries under intense pressure (BirdLife 2011; BIO Intelligence Service 2011), and this not reflected by the high level of protected area

30 coverage in these countries. For instance, 86% of migratory birds meet protection targets within both Italy and Malta despite high levels of illegal hunting in these countries (BirdLife 2011). Contrastingly, strong management of hunting within countries with otherwise poor levels of protection (such as Canada) can mean certain species are well conserved despite low representation within protected areas.

2.6 Conclusions

Our results have dramatic consequences for the future conservation of migratory birds. While global focus since the development on the new Convention on Biological Diversity Strategic Plan in 2010 (CBD 2010) has been on increasing both the size and representation of the global protected area estate, with some success (Tittensor et al. 2014), our results highlight a failure to adequately consider the linkages between protected areas. Our data shows that migratory species remain very poorly represented in the global protected area system despite ongoing focus in international agreements. The current 2020 CBD targets (CBD 2011) will drive the greatest expansion of protected areas in history and is a key opportunity to start to save migratory species (Radeloff et al. 2012). But we need to shift to transparent and biologically sensible targets that consider ecological phenomena such as migration and set up innovative bilateral and multilateral conservation arrangements between nations if migratory species are going to be saved.

Chapter 3 Geographic range size and extinction risk assessment in nomadic species

This chapter is in press:

Runge, CA, AI Tulloch, E Hammill, HP Possingham, and RA Fuller. 2015. Geographic range size and extinction risk assessment in nomadic species. Conservation Biology. doi: 10.1111/cobi.12440

3 Geographic range size and extinction risk assessment in nomadic species

3.1 Abstract

Geographic range size is often conceptualized as a fixed attribute of a species, and treated as such for the purposes of quantification of extinction risk, where species occupying smaller geographic ranges are assumed to have a higher risk of extinction. However many species are mobile, with movements ranging from relatively predictable classic migrations to complex irregular movements shown by nomadic species. These movements can lead to substantial temporary expansion and contraction of geographic ranges, potentially to levels which may pose an extinction risk. By linking occurrence data with environmental conditions at the time of observations of nomadic species, we modelled the dynamic distributions of 43 arid-zone nomadic bird species across the Australian continent for each month over an eleven year period. We discovered enormous variability in predicted spatial distribution over time, with ten species varying in estimated geographic range size by more than an order of magnitude, and two species varying by more than two orders of magnitude. Several species not currently classified as globally threatened contracted to very small areas during times of poor environmental conditions despite their normally large geographic range size, raising questions about the adequacy of conventional assessments of extinction risk based on geographic range size (e.g. for IUCN Red Listing). We explore estimation of extinction risk in nomadic species using minimum range size and extent of fluctuation in geographic range size, calculated from dynamic distribution models. Climate change is predicted to affect the pattern of resource fluctuations across much of the southern hemisphere, where nomadism is the dominant form of animal movement, so it is critical we begin to understand the consequences of this for accurate threat assessment of nomadic species. Our approach provides a tool for discovering spatial dynamics in highly mobile species, unlocking valuable information for improved extinction risk assessment and conservation planning.

3.2 Introduction

Extinction risk estimates provide one of the foundations for prioritizing conservation actions (Joseph et al. 2009), but are hindered by a lack of accurate distribution and abundance metrics for many species. Measures of geographic range size can be used as surrogates for population decline and extinction risk (Purvis et al. 2000), with geographic range size consistently emerging as a key correlate of extinction risk in mammals, amphibians and birds (Cardillo et al. 2008; Sodhi et al. 2008; Lee & Jetz 2011). Several different measures of geographic range size exist (Gaston & Fuller 2009). Estimations of extinction risk typically use metrics such as extent of occurrence (EOO) or area of occupancy (AOO), based on a conceptualization of geographic range size as a fixed attribute of a species; with EOO being a measure of the degree to which a species’ distribution, and hence vulnerability to threats, is spread across geographic space, while AOO is a measure of the area actually occupied by the species. Using these metrics, species with smaller extents or areas are assumed to be more threatened (Gaston and Fuller 2009; IUCN 2014). However, when a species is nomadic within its overall distribution, estimates of EOO or AOO based on pooling observations across time will often be larger than the geographic range size at any one point in time. This could lead to an erroneous conclusion that a nomadic species is safe from extinction when it is not. Here we examine the temporal variability in the AOO of nomadic species, and explore the consequences of such dynamism for extinction risk assessments.

Across much of the southern hemisphere, animal movement patterns are dynamic and irregular, with many bird species displaying some form of irregular movement such as nomadism (Chan 2001; Dean 2004). Nomads move in complex patterns, often associated with highly fluctuating resources, for example seasonal fruiting or resource booms associated with irregular desert rainfall (Berthold 2001; Dean 2004; Cox 2010). Movement strategies may be adjusted dynamically according to the prevailing conditions at each time and place (Andersson 1980; Woinarski et al. 2000; Webb et al. 2014). Much of the evidence for nomadic movements in individual species is anecdotal or qualitative, likely as a result of the difficulties in monitoring and tracking such highly dynamic species (Marchant & Higgins 1990). As a consequence, the responses by nomads to fluctuations in environmental conditions remain poorly understood (Bennetts & Kitchens 2000; Dean & Milton 2001). Without this information, it is challenging to estimate their extinction risk.

Almost 50% (2,072 of 4,440 species) of threatened mammals, birds, amphibians and gymnosperms are listed as threatened on the basis of geographic range size criteria, in conjunction with meeting

35 subcriteria on trends, fragmentation and fluctuations (Gaston and Fuller 2009). However, any measure of geographic range size for nomadic species that pools distributional data across time will represent a maximum that represents an upper bound on a distribution. At certain points in time a species’ distribution might contract to localized resource patches, occupying only a very small part of its maximum distribution. Moreover, many nomadic species move large distances across inaccessible environments that are poorly surveyed, leading to large gaps in our knowledge of their distributions (Szabo et al. 2007; Tulloch et al. 2013b). These gaps make it difficult to determine from distributional data alone whether a species is in a true contraction and missing from much of the landscape, or whether surveys have not adequately covered its whole distribution.

The consequences of distributional fluctuations on species’ persistence are partially captured by existing extinction risk assessment frameworks, with extreme fluctuation being an assessable subcriterion under criteria B and C2 of the IUCN Red List (IUCN 2014). However, IUCN Red Listing under extreme fluctuation requires that population size or geographic range size targets have already been triggered. Lack of theoretical and empirical testing leaves the relationship between fluctuating range size and extinction risk unclear, though there is evidence for higher extinction risk with fluctuating population size (Pimm et al. 1988; Hung et al. 2014), and temporary range contraction (Newton 2004), forming the basis for IUCN Red List criteria B (IUCN 2014). Actual relationships are likely to be species- and threat-specific; dependent on the nature of threats, and the impact those threats have on density-occupancy relationships in the target species (Gaston 2003).

Several previous studies have used modelling approaches to identify fluctuating species distributions (Taplin 1991; Chan 2001; Griffioen & Clarke 2002; Reside et al. 2010), though the extent of geographic range size fluctuations in vertebrates remains poorly known. Here we determine temporal variability in the geographic range size (AOO) and therefore extinction risk for a suite of Australian nomadic birds. We compare modelled ‘time-sliced’ distribution maps against more traditional estimates of AOO based on occurrences that are pooled across time. We use the results of our models to provide guidelines for incorporating range size variability into existing extinction risk assessments.

3.3 Methods

3.3.1 Case study area and species

We used a suite of Australian arid-zone nomadic birds as a case study. Occupying over 6.2 million km2, the Australian arid and semi-arid zones are associated with irregular fluctuations in resources predominantly driven by rainfall. Complex patterns of rainfall drive movement in many species of birds, mammals and invertebrates (Keast 1959; Dean 2004; Letnic & Dickman 2006). Resource fluctuations comprise annual seasonality overlain onto longer and less predictable “boom and bust” cycles in resources (Meyers et al. 2007; Risbey et al. 2009). Nomadic species in Australia face a suite of threats including habitat loss through degradation and human encroachment, climate change and pressure from introduced species (Reid & Fleming 1992; Cleugh et al. 2011; Ford 2011; Garnett et al. 2011).

We selected 43 arid-zone bird species thought to be nomadic or possibly nomadic (Marchant & Higgins 1990; Ziembicki & Woinarski 2007; BirdLife 2012). Nomadic species range over large areas, and may show different movement patterns under different environmental conditions (Dean 2004) limiting the ability of field experts to reliably classify species as nomadic. For this reason, we include any species where nomadism is indicated in part or all of their range. Bird occurrences were collated from 20 minute area searches of 2 hectare plots conducted between June 2000 and March 2011 as part of the New Atlas of Australian Birds (for details see website: http://www.birdlife.org.au/projects/atlas-and-birdata). We excluded occurrences with no recorded coordinate system or where the spatial accuracy of the coordinate location was coarser than 500m. The number of occurrences for each species ranged from 29 to 21,634 over the 11 year period. We excluded occurrences outside Interim Biogeographic Regionalisation Areas (AGDoE 2004) that intersected Australian rangelands (ACRIS 2005) to limit model fitting to just the arid and semi-arid subpopulations of modeled species. We also excluded occurrences with missing environmental data (e.g. where cloud cover consistently disrupted satellite data). The study area was divided into grid cells of 0.05° for analysis.

3.3.2 Species distribution models

We used the software Maxent v3.3.3 (Phillips et al. 2006) to predict the distribution of each species from the occurrence data sets. Maxent was run on an Ubuntu platform with samples-with-data inputs (Phillips et al. 2009). The Birdlife Atlas dataset is temporally and spatially biased, with relatively few surveys of arid regions during summer months (Tulloch et al. 2013; Szabo et al. 2007; Figure 3.1). We accounted for this coastal and spring bias in survey effort by drawing 10 000 background data points from a random sample of Atlas surveys (Phillips et al. 2009). Put simply, this approach compares the distribution of environmental variables across species presences with the distribution of environmental variables across surveyed locations (rather than across the whole landscape).

Figure 3.1 Spatial and temporal bias in survey effort within the study region

We included nineteen predictor variables in the models; twelve static variables (vegetation types) and seven time-dependent variables calculated over the 3 months prior to the date of each record (maximum temperature; minimum temperature; maximum and normalized fractional photosynthetic vegetation; maximum and normalized fractional non-photosynthetic vegetation; Foley’s drought index). For example, species occurrences for June 2000 were associated with environmental records aggregated over the months March 2000, April 2000 and May 2000. Short term averages of weather data have been shown to predict nomadic species’ distributions more accurately than long-term climate averages (Reside et al. 2010) and climate extremes have been found to more strongly define species distribution boundaries than means (Lynch et al. 2013). Time lags of 1, 2, 3, 4, 5, 6 and 12

38 months were tested, and 3 months emerged as the best predictor across the modelled species (but see Reside 2010). All variables showed pairwise Pearson correlation coefficients below 0.7.

We calculated static vegetation variables by reclassifying the 31 National Vegetation Information System (NVIS) - Major Vegetation Groups Version 3.0 (AGDoE 2005) into 12 groups and calculating the proportion of each pixel covered by each vegetation group (Table 3.1). Fractional photosynthetic vegetation (PV, vegetation greenness) and non-photosynthetic vegetation (NPV, vegetation dryness) were calculated from the Guerschman Fractional Photosynthetic Vegetation (FPV) dataset (Guerschman et al. 2009), which uses remote-sensing data from the EO-1 Hyperion and MODIS satellite-based instruments. We calculated maximum PV and NPV as the absolute maximum value over the 3 month window and normalized PV and NPV as that maximum divided by the long-term average for 2000 to 2011. We calculated 3-monthly maximum and minimum temperature from interpolated daily temperatures accessed through SILO (Jeffrey et al. 2001). Foley’s drought index was used to reflect rainfall scarcity, as rainfall is interpolated across large distances in the study regions (Fensham et al. 2009).

Table 3.1: Reclassification of NVIS Major Vegetation Groups into 12 vegetation variables for species distribution models

MVG category Description 6,13,16 Acacia woodlands and shrublands 7 Callitris forest and woodland 8 Casurina forest and woodland 22 Chenopod, samphire shrublands and forblands 5,11,12,29 Eucalypt woodlands 20 Hummock grasslands 9,24 Inland wetland 14 Mallee woodland and shrublands 21 Other grasslands 17 Other shrublands 19 Tussock grasslands

We created one species distribution model for each species from all records spanning June 2000 to March 2011. We make the assumption that the modelled species respond consistently to environmental drivers across their range. For each species, we projected the model onto the environmental variables corresponding to each month from that time period, to create monthly ‘time-sliced’ distributions (130 projections per species). We validated models by a combination of

39 null model testing, comparison with published distributions, and expert evaluation based on known ecology. Null models were created by selecting 100 random subsets from all survey data, with the number of records corresponding to the number of records used to model each species. All species models were found to have greater predictive power than null models run with the same parameters (z-test; probability that observed model AUC falls within the range expected from the null model p < 0.00001 for all species; Raes & ter Steege 2007). We rejected one species on the basis of a visual assessment of the resulting distribution maps (the cryptic Chestnut-backed Quail-thrush - Cinclosoma castanotum), which showed low suitability in some areas of known occurrence. This species is a cryptic ground-dwelling bird with a call above the hearing range of many observers, likely making this species’ observations heavily affected by detectability bias. A second species was rejected on the basis of insufficient data – Princess Parrot Polytelis alexandrae with only 10 records.

One potential consideration in species distribution modelling is prediction outside environmental conditions in the training dataset. We addressed this in two ways. Firstly, projected species responses to environmental variables were clamped to the range of environmental conditions within the dataset. Secondly, an important consideration in modelling of climatic niche is that the data adequately covers the range of environmental variables (rather than spatial and temporal coverage per se). We assessed the range of environmental conditions within the training dataset and found it contained data from -6°C to 48°C and FPV from 0 to 100%, thus covering the range of conditions encountered within the study region. The only species with predictions significantly outside the training range was Polytelis alexandrae. The maximum temperature in the available occurrence data for this species was limited to 40°C, which we assessed as likely to be exceeded within this species range, and this model was discarded as mentioned above.

We reclassified the Maxent logistic probability into predictions of absence and probability of presence using equal sensitivity and specificity threshold values (Liu et al. 2005). Each pixel above the threshold retained its logistic probability value of habitat suitability, while every pixel below the threshold was reclassified as zero habitat suitability. We then clipped the time-sliced maps to exclude IBRA bioregions (AGDoE 2004) where the target species had not been detected in the 11 year period. Due to the coarse spatial resolution of our distribution models, one cell may contain multiple vegetation types, not all of which will be suitable for all species - although a single cell could be predicted as suitable, the entire area of that cell (~25km2) is unlikely to be occupied. We

40 therefore estimated geographic range size (AOO) at each point in time by multiplying the habitat suitability for each pixel by the area (km2) of that pixel, then summing the values across all pixels in the time-sliced map. To derive an estimate of the pooled geographic distribution for each species, based on aggregated distribution across time (the kind of quantity typically used to estimate extinction risk for nomadic species), we calculated the maximum of the habitat suitabilities for each pixel across all time periods, multiplied the habitat suitability for each pixel by its area (km2) and then summed the values across all pixels in the map.

3.3.3 Extinction risk

Minimum, maximum and mean geographic range size calculated from the time-sliced range sizes are essentially akin to estimates of AOO (Gaston and Fuller 2009). We used linear models to analyze the relationship between the pooled geographic range size and the response variables of minimum, maximum, and mean range sizes estimated from our models. We calculated magnitude of fluctuation as the ratio of maximum to minimum geographic range size, classified as extreme fluctuation where that value exceeds 10 (IUCN 2014). Analyses were conducted using R version 2.15.1 (www.r-project.org).

3.4 Results

Tables containing information on range size metrics and model statistics (Appendix 2) and plots of temporal dynamics in range size (Appendix 3) can be found in the Appendices for all 43 modelled nomadic species discussed here and an additional 31 species classified as non-migratory and migratory and modelled according to the methods outlined in section 3.3. The total area predicted as suitable for each species fluctuated across seasons and years with distinctly different patterns between species. The enormous variation in dynamics across species suggests that the models reflected the differing relationship between each species and the environmental variables rather than simply reflecting variation in particular environmental variables. Plots of temporal range size dynamics are provided in Appendix 3. By way of example, the modelled range size for the Scarlet- chested Parrot (Neophema splendida) showed a strong degree of seasonal fluctuation with repeated seasonal minima in March (Figure 3.2a). This seasonal fluctuation was overlain with longer-term fluctuation in both minima and maxima. Sixteen species in total (35%) showed such seasonal fluctuations (Appendix 3).

Not all species showed extreme seasonal effects, with 27 species (63%) showing some seasonal variation superimposed onto more complex dynamics. For instance, the Black Honeyeater (Sugomel nigrum) displayed slight seasonal variation but much stronger and more complex long term effects (Figure 3.2b). At the beginning of the time period, which corresponded to high rainfall across interior Australia (2000 to late 2002), the species was predicted to occupy a large area. Notably, the minima in these years exceeded the maxima of later years, and the distribution contracted to a low in January 2010.

The relationship to landscape-wide dynamics in rainfall and drought was mixed. The Letter-winged Kite (Elanus scriptus) contracted dramatically after 2003 with a recovery at the end of the time series corresponding to an increase in rainfall across the landscape (c). These nocturnal raptors feed on rodents that irrupt after high rainfall events such as that in 2000 to 2002 (Pavey et al. 2008), and recently there has been a spike in records corresponding with the latest rainfall event in 2009-2011 (Pavey & Nano 2013; Figure 3.2c & f). Six other species showed a similar pattern (Black- shouldered Kite - Elanus axillaris, Spotted Harrier - Circus assimilis, Stubble Quail - Coturnix pectoralis, Mistletoebird - Dicaeum hirundinaceum, Black Falcon - Falco subniger, Budgerigar - Melopsittacus undulatus). An additional seven species showed a weaker time-lagged contraction after 2003 with no recovery after 2009 (Grey Honeyeater - Conopophila whitei, Ground Cuckooshrike - Coracina maxima, Grey-headed Honeyeater - Ptilotula keartlandi, Grey-fronted Honeyeater - Ptilotula plumula, White-fronted Honeyeater - Purnella albifrons, Black Honeyeater).

Conversely, the suitable habitat for three species expanded as the landscape dried out after 2003 (Figure 3.2d; Yellow Chat - Epthianura crocea, Orange Chat - Epthianura aurifrons and Chestnut- breasted Whiteface - Aphelocephala pectoralis).

Interestingly, one species, the Gibberbird (Ashbyia lovensis), a species usually described in the literature as nomadic or locally nomadic (Marchant & Higgins 1990), displayed an approximately constant range size although the location of these areas was dynamic (Figure 3.2g & e).

Some species showed extreme fluctuations between the maximum and minimum range size (Table 3.2; Figure 3.3), and the magnitude of these fluctuations increased as mean range size decreased. In part this is inevitable, because fluctuation of the wider ranging species is limited by the size of the Australian continent. Of the 43 species, 11 showed extreme fluctuation (> 1 order of magnitude) as defined by IUCN Red List criterion B2cii (IUCN 2014; Table 3.2).

Table 3.2: Range size and extinction risk metrics for 43 nomadic bird species

Overall Minimum Magnitude Satisfies Satisfies range size range size of criterion subcriterion (km2) (km2) fluctuation B2 (range B2cii in range size < (extreme Species size 2000km2) fluctuation) Stubble Quail Coturnix pectoralis 1,819,376 169,017 7 Black-shouldered Kite Elanus axillaris 2,645,411 113,305 15 Yes Letter-winged Kite Elanus scriptus 719,691 60,454 10 Yes Spotted Harrier Circus assimilis 3,559,606 583,026 4 Australian Bustard Ardeotis australis 3,135,949 1,123,919 2 Common Bronzewing Phaps chalcoptera 1,097,672 86,879 6 Flock Bronzewing Phaps histrionica 916,107 84,554 8 Diamond Dove Geopelia cuneata 2,731,995 220,878 9 Grey Falcon Falco hypoleucos 2,572,585 882,558 2 Black Falcon Falco subniger 2,675,534 537,230 3 Major Mitchell's Cockatoo Lophochroa leadbeateri 2,404,222 560,730 3 Cockatiel Nymphicus hollandicus 3,270,352 106,111 18 Yes Bourke's Parrot Neopsephotus bourkii 1,657,523 746,496 2 Scarlet-chested Parrot Neophema splendida 496,793 776 502 Yes Yes Budgerigar Melopsittacus undulatus 2,789,945 186,998 11 Yes Black Honeyeater Sugomel nigrum 2,206,769 237,940 7 Pied Honeyeater Certhionyx variegatus 2,538,637 630,913 3 Brown Honeyeater Lichmera indistincta 2,571,125 138,958 12 Yes Painted Honeyeater Grantiella picta 780,039 92,922 4 Striped Honeyeater Plectorhyncha lanceolata 659,307 82,817 5 Gibberbird Ashbyia lovensis 327,149 151,157 1 Crimson Chat Epthianura tricolor 2,611,986 157,107 13 Yes Orange Chat Epthianura aurifrons 2,138,565 493,032 3 Yellow Chat Epthianura crocea 257,089 26,570 5 White-fronted Chat Epthianura albifrons 625,249 64,954 6 Grey Honeyeater Conopophila whitei 1,297,181 108,314 10 Yes Spiny-cheeked Honeyeater Acanthagenys rufogularis 2,063,826 448,022 4 White-fronted Honeyeater Purnella albifrons 1,669,300 103,538 11 Yes Grey-headed Honeyeater Ptilotula keartlandi 1,814,667 185,357 6 Grey-fronted Honeyeater Ptilotula plumula 2,210,412 255,598 5 Striated Pardalote Pardalotus striatus 1,161,005 219,578 3 Western Gerygone Gerygone fusca 2,271,607 384,749 4 Chestnut-breasted Whiteface Aphelocephala pectoralis 71,193 37 1720 Yes Yes Banded Whiteface Aphelocephala nigricincta 1,446,464 336,688 3 Ground Cuckooshrike Coracina maxima 3,155,208 448,945 5 Grey Fantail Rhipidura albiscapa 436,107 86,055 3 Little Crow Corvus bennetti 2,508,774 1,111,711 2

Overall Minimum Magnitude Satisfies Satisfies range size range size of criterion subcriterion (km2) (km2) fluctuation B2 (range B2cii in range size < (extreme Species size 2000km2) fluctuation) Jacky Winter Microeca fascinans 1,564,910 326,901 3 Red-capped Robin Petroica goodenovii 2,829,147 562,818 4 Mistletoebird Dicaeum hirundinaceum 2,873,533 336,324 5 Painted Finch Emblema pictum 1,494,337 350,768 3 Plum-headed Finch Neochmia modesta 955,399 89,282 7 Pictorella Mannikin Heteromunia pectoralis 1,284,739 33,227 26 Yes

The slopes of linear models showed that pooled geographic range size exceeded the minimum geographic range size by 82.6% ± 7.6%, (estimate ± 95% CI), mean geographic range size by 58.5% ± 6.6% and maximum geographic range size by 30.4% ± 5.3% (Figure 3.4).

Figure 3.4 The relationship between pooled geographic range size and the time-sliced estimates of maximum (y~0.70x-2.6x104, p<0.001) ,mean,(y~0.40x-4.4x104, p<0.001) and minimum.(y~0.17x- 1.6x104, p<0.001) Bounding lines indicate 95% confidence intervals

3.5 Discussion

We have provided an empirical analysis of nomadic species dynamics using time-sliced species distribution models linked to time-delayed local weather patterns. As expected, the area occupied was highly variable across time, with the extent and pattern of fluctuation differing markedly among species. All species exhibited significant bottlenecks; points in time where the area of occupancy of the species was very low. By exploring these bottlenecks using our estimates of minimum range size, we determined how many species meet the classification thresholds for threat under IUCN guidelines. This approach can be applied with fewer data than quantitative population trend estimates, is more appropriate for nomads than static geographic range size estimation based on pooled occurrences across time, and can be used for classification of extinction risk for nomadic species anywhere that sufficient occurrence data have been collected to derive species distribution models.

Extinction risk in a nomadic species as measured by minimum AOO is not necessarily the same as that of an otherwise identical sedentary species. While a nomad and an equivalent sedentary species could be at equally high risk from threats whilst occupying a bottleneck or refugial site, the ability of nomads to expand in distribution (and population) when environmental conditions improve may buffer them from stochastic threats over the long term as they can move on and take advantage of good conditions elsewhere (Dean 2004). However, recent work has found that the buffering effect of movements is obviated in the face of widespread habitat loss, with equal declines among migrants and non-migrants (Albright et al. 2010; Bennett et al. 2014). Movement itself could be risky in the sense that locations and timings of suitable resources are unpredictable and irregular (MacNally 2009). Additionally, in some cases, threats can be concentrated into precisely the areas to which nomadic species contract (Stojanovic et al. 2014); for example, both invasive predators and livestock grazing follow rainfall patterns during prolonged drought (Reid & Fleming 1992; Grenville et al. 2014).

Although nomads are often wide-ranging, they are rarely habitat generalists. Nomads instead can be highly habitat specific, keying into specific environmental conditions such as vegetation seeding and flowering events (Ziembicki & Woinarski 2007; Pavey & Nano 2013; Tischler et al. 2013; Webb et al. 2014), making them less resilient to environmental change than generalist sedentary species. There has been widespread modification and transformation of vegetation across inland

Australia, with 46% of the continent subject to grazing of native vegetation (SoE 2011), and this is likely to have impacted nomadic birds (Reid & Fleming 1992).

Our data suggest that threat assessments (e.g. IUCN Red Listing) based on geographic range size may underestimate extinction risk in nomadic species, if such assessments are based on pooled occurrences across time. Populations of nomadic species might rarely cover the pooled geographic range, instead frequently contracting to areas significantly smaller than their maximal distribution. For instance, the Scarlet-chested Parrot (Neophema splendida) is currently listed as least concern as the population is thought to be stable and occupy a large area (EOO 262,000 km2; BirdLife 2013)

48 though the accuracy of population estimates is acknowledged to be poor. However, given the evidence of extreme fluctuations in geographic range size presented here (Figure 3.3) and the repeated occurrence of minimum AOO below the 2000 km2 IUCN vulnerable threshold (Figure 3.5a; IUCN 2014), there is perhaps a case to uplist this species.

Similarly our models hint at strong fluctuations in geographic distribution for the Chestnut-breasted Whiteface (Aphelocephala pectoralis; Figure 3.3), and that the area of occupancy may drop to just 37 km2 at certain points in time, well below the IUCN endangered threshold (Figure 3.5b; IUCN Red List criteria B2: AOO<500 km2). These examples suggest that species may be at greater risk of extinction than suggested by their current IUCN status, and we urge field studies to look for empirical evidence of distributional fluctuations.

Which measure of geographic range size best reflects an appropriate measure of extinction risk for nomadic species? Fluctuation in population size is already captured under criterion B2cii (IUCN Red List), but only applies if absolute area thresholds in EOO or AOO have been breached (IUCN 2014) and there are no guidelines around fluctuating range size. Guidelines indicate that for migratory species, the geographic range size metric for Red Listing should be based on the smaller of either the breeding or non-breeding distributions (IUCN 2014). While recognising it is not a direct analogy, we suggest assessing extinction risk for nomads on the basis of minimum range size, either observed or estimated, in situations where a species cannot be assessed using alternative methods such as fluctuations in population size. Our approach assumes that summed habitat suitability in occupied areas represents a species’ geographic range size, which although parsimonious in the absence of data to the contrary, would benefit from detailed investigation. While the true relationship between fluctuating distributions and extinction risk is unresolved for nomads, we make the assumption that the relationship between habitat suitability, population density and extinction risk is linear.

Nomadic movements across space and time limit our ability to determine population dynamics, and consequently our ability to estimate risk on that basis. Many migratory species can be surveyed annually because of predictable movements to and from breeding grounds, which allows reasonably accurate measurement of population change and extinction risk (Wilson et al. 2011; Clemens et al. 2012). However, for nomadic species when and where we monitor may dramatically influence our estimates of both population abundance and trend. Figure 3.6 illustrates a possible outcome of monitoring at different locations across a nomadic species’ distribution, assuming for the purpose of

49 this example a linear relationship between habitat suitability and population size (Lawton 1993). Extrapolating trends measured at the center of a distribution could lead to an overestimate of total population size and an underestimate of population fluctuations. Conversely, monitoring at the edge of the range could indicate a dramatically fluctuating population, with low to medium probability of presence, depending on the location monitored. The overall trend (Figure 3.6a) shows population size and dynamics may be somewhere between those estimated by monitoring at the core (Figure 3.6b) and edges (Figure 3.6c-d), consistent with the general pattern that populations are more abundant at the center of their ranges and variable towards the range edges (Brown 1984; Gaston 2003). It would be very difficult to identify any underlying population trend in the presence of such complex spatial and temporal fluctuations. Geographic range size determination thus seems the most tractable way to assess extinction risk in nomadic species, despite its reliance on (as yet untested) theoretical relationships between habitat suitability and population size.

While our models enhance the capability to estimate extinction metrics, it is unclear how distribution fluctuations impact long-term persistence. The impact of fluctuations on population persistence is a function of the number of subpopulations and the synchronicity of fluctuation across those populations (Lawton et al. 1994). Both theory and empirical evidence predict that extinction risk is higher in species with highly fluctuating populations (Pimm et al. 1988; Hung et al. 2014) yet such fluctuations could also indicate an ability to cope with changing patterns of resources in a landscape. While many nomadic species are hypothesized to have an inherent capacity to bounce back from spatial and numerical bottlenecks (Dean 2004; Jonzén et al. 2011), we know little of their vulnerability to environmental change. The response to bottlenecks may be related to the length and amplitude of the bottleneck, and the presence and condition of refugia (Mangel & Tier 1994). For instance, an extreme drought in eastern Australia in 1902 led to mass mortality among birds in central Queensland that persisted for many years, and was a major contributor to the extinction of the once common Paradise Parrot (Psephotus pulcherrimus; Keast 1959), whose refugial grounds had been lost to newly expanding agriculture. Similarly, short term heat waves can cause huge mortalities in arid-zone birds. One such event occurred in January 2009, when temperatures rose above 45°C for several consecutive days killing thousands of birds (McKechnie et al. 2012). Predicted increases in heat wave frequency might exacerbate the impact of such mortality events (McKechnie & Wolf 2010). Cooler microclimates can mediate these mortalities and conservation actions for susceptible species may include provision of shaded bird-accessible water points (McKechnie et al. 2012). These species evolved in a landscape where environmental conditions are dynamic, and strategies such as opportunistic breeding and diet switching may facilitate the ability of arid-zone birds to recover from bottlenecks (Dean 2004). However, rapid environmental change such as climate change has the potential to outpace species’ abilities to respond to temporally and spatially variable environmental conditions. Further research is required to determine the thresholds beyond which the ability of these species to recover from temporal, spatial and evolutionary bottlenecks is impaired.

3.6 Conclusions

By generating estimates of both mean and minimum range size across time, our study shows how to derive more accurate empirical estimates of fluctuations in dynamic species than those currently available. Truly accurate estimation of long-term persistence in nomads such as arid-zone birds is limited by our lack of knowledge of the impact of human land use change and their ability to overcome environmental fluctuations. In the absence of such information, our approach provides a valuable starting point for conservation planning for dynamic species.

Chapter 4 Incorporating dynamic distributions into spatial priority setting for conservation

4 Incorporating dynamic distributions into spatial priority setting for conservation

4.1 Abstract

Species’ distributions are generally treated as static for the purposes of prioritization, but many species such as migrants and nomads have distributions that shift over time. Determining priority actions for such species must take account of this temporal variation making planning their conservation a complex problem. Here we explore how dynamic distributions can be incorporated into a spatial prioritization, and suggest approaches for prioritizing conservation action when knowledge of species’ movements is uncertain, for a suite of nomadic species across arid and semi- arid Australia. We find that incorporating the temporal dynamics of species into spatial prioritization substantially changes the spatial pattern of conservation investment, spreading the priority more widely across the landscape, and increasing the overall area needed to be placed under conservation measures to achieve a specific target level of species protection. Prioritizing bottlenecks, locations critical to each species when its distribution is at a minimum, prioritizes a very different suite of sites to those chosen using the traditional approach of static distribution maps based on occurrences pooled across time. Our results highlight the need to consider dynamic movements in the conservation planning process to ensure that mobile species are adequately protected. A static approach to conservation planning may misdirect resources and lead to inadequate conservation for mobile species.

4.2 Introduction

For species that move between multiple sites, such as migrants and nomads, the success of spatial conservation planning will be dependent on identifying actions that maintain a viable population across both space and time. Nomads move in complex, irregular patterns, often associated with opportunistic breeding or feeding (Dean 2004) and although nomadic species often have a large geographic range when their seasonal distributions are pooled across time, at any given point in time just a small part of that range may contain suitable resources for the species. The distribution of some species fluctuates by an order of magnitude or more, and may become highly spatially constricted at particular points in time (Runge et al. 2015). Such spatial dynamics can be significant for any attempts to conserve mobile species including migrants and nomads (Runge et al. 2014).

Failing to protect critical sites, including bottlenecks, may result in irreparable population declines or extinction (Reid & Fleming 1992; Woinarski et al. 1992).

Spatial prioritization is one of the backbones of systematic conservation planning, and is a process where a set of planning units within a region are recommended for action based on their availability, conservation benefit and cost. Conservation planners have rarely included animal movements when prioritizing actions, instead focusing on how to incorporate costs (Naidoo et al. 2006), feasibility (Knight et al. 2011; Tulloch et al. 2014), uncertainty in data (Carvalho et al. 2011; Tulloch et al. 2013), future threats (Game et al. 2008), and multiple options for conservation action (Reyers et al. 2012). Recent emphasis on the importance of incorporating spatial and temporal dynamics into the planning process (Grantham et al. 2008; Lourival et al. 2011) has led to some attention being focused on this issue (e.g. Runge et al. 2014), with several recent studies incorporating the movements of migratory species into conservation planning (Martin et al. 2007; Iwamura et al. 2014).

Classic migrants have relatively predictable patterns of movement, and there has been much recent progress in understanding how to incorporate such movements into spatial conservation planning (Klaassen et al. 2008; Moilanen et al. 2008; Beger et al. 2010; Linke et al. 2011; Kool et al. 2013; Iwamura et al. 2014). However, many species show much less predictable patterns of movement. For example, the irregular movements of nomadic species make their conservation a particular challenge. As a consequence, their distributions are generally treated as static, with little or no reference to their need for protection in particular parts of their lifecycle or across resource hotspots (Gilmore 2007; Watson et al. 2011, Venter et al. 2014). We are only aware of one attempt where the dynamics of nomadic species have been considered in a prioritization scheme. Fahse et al. (1998) examined alternative configurations of theoretical protected areas for a nomadic lark in the Nama- Karoo, South Africa by using a spatio-temporal model to estimate the survival of flocks given known ecological relationships with seasonal rainfall patterns (Fahse et al. 1998). They found that these nomadic birds were best protected by a series of sites spread across the study region, spatially focused on areas of high resource availability. Their study sought to inform the single-large-or- several-small (SLOSS) debate on optimal protected area configuration rather than a systematic conservation plan, and therefore did not incorporate cost or other feasibility metric. However, this remains the only example where distributional dynamics have been incorporated into a prioritization for a nomadic species.

Without guidelines for best practice approaches for incorporating spatial and temporal dynamics of species with variable ranges into systematic conservation planning, decision-makers run the risk of prioritizing the wrong areas, wasting funding, and losing donor confidence. Conservation planning problems are typically formulated as either a ‘minimum set’ (meet conservation targets using minimal resources i.e. area, money) or ‘maximum coverage’ (maximize conservation benefits given fixed amount of resources) problem (Possingham et al. 2006; Moilanen et al. 2009a). Regardless of the approach, using a static distribution map that overestimates the habitat needs for a nomadic species would most likely focus prioritization on the cheapest places regardless of whether or not those places are used more often than others, and risks overlooking key sites used by nomads if they are relatively expensive. A dynamic approach allows planners to discover and account for the places that are important at only certain points in time (e.g. when ephemeral resources are available in that area), with less risk of protecting areas of permanently low value for nomads.

The distribution of nomads is dynamic across space and time and there are a number of possible approaches to prioritizing conservation actions in light of those dynamics. Currently, the nature of movements of nomadic birds is poorly known (Chan 2001; Dean 2004; Burbidge & Fuller 2007) and can differ across regions (Wyndham 1982; Ziembicki & Woinarski 2007). As a consequence, it remains difficult to determine the most effective conservation strategy for such highly dynamic species. One approach may be to prioritize refugia or bottlenecks, places to which the species contracts during times of limited habitat suitability across the wider landscape. If such refugia exist, they may be crucial to long-term persistence (Reid & Fleming 1992; Morton et al. 1995). However knowledge on the long-term consistency in locations of refugial sites is limited (Manning et al. 2007), as is understanding of the role they play in population dynamics (Bennetts & Kitchens 2000). In some species it may be more important to protect a good sample of suitable habitat across space and time (Dickman et al. 1995). It is unclear how those differing approaches will affect population persistence, given the lack of ecological knowledge on nomadic species. A bet-hedging approach may be to undertake conservation actions in refugia (where known) in combination with broader landscape management.

Here we present an approach for incorporating temporal dynamics into a static spatial prioritization for data-poor nomadic species. Our objective was to compare the minimum set of areas identified for conservation action (ecologically favorable management involving loss of agricultural profitability including but not limited to protected area designation) under five scenarios of species

56 representation that varied according to plausible beliefs of where and when the most important places for maintaining nomadic populations occur. This approach could inform conservation planning for any suite of species with dynamic distributions across time, from short-term migratory movements to long-term distributional changes driven by climate change. We then examine the impact of different temporal choices of distributional information on the configuration and cost of spatial prioritizations and suggest how that information might be used to guide both conservation and ecological research priorities. We then use a case study of nomadic birds in arid and semi-arid Australia to evaluate how well the current Australian protected area estate captures the dynamic distributions of a suite of nomadic birds.

4.3 Methods and data

4.3.1 The study region

The arid and semi-arid zones of Australia occupy an area of over 6.2 million km2. Large areas of the region are grazed, predominantly for cattle and sheep production, with smaller regions of cropping and irrigated agriculture (SoE 2011). While most of the arid inland retains remnant vegetation, 46% of this vegetation has been modified by human activity, mainly grazing (SoE 2011). Changes in grazing intensity have the potential to negatively impact native fauna (Reid & Fleming 1992; Frank et al. 2014), particularly ground-dwelling species such as Australian Bustard (Ardeotis australis; Ziembicki & Woinarski 2007), granivorous birds such as Flock Bronzewing (Phaps histrionica; Dostine et al. 2014) or those where prey abundance is related to grass-seed cycles, such as Letter- winged Kite (Elanus scriptus; Pavey & Nano 2013). Changes in vegetation community brought about by direct grazing, clearing for fodder or vegetation thinning may have indirect impacts on species such as whiteface (Acanthizidae), chats and honeyeaters (Meliphagidae) that are dependent on particular communities within the shrub layer (Tischler et al. 2013).

The study region was divided into grid cells of 10x10km for analysis, resulting in 66179 planning units. We estimated conservation feasibility using data on agricultural profit at full equity for the period 2005-2006 (Marinoni et al. 2012). These data were calculated in a period of widespread drought, and to avoid underestimating landholder values negative profitability values were set to zero. We adjusted for inflation to December 2013 (ABS 2013) and multiplied the average profitability per hectare in each planning unit by the area of that planning unit. We determined the net present value of foregone annual profitability as per Carwardine et al. (2008) in Australian

57 dollars. A transaction cost of $10 000 was applied to each planning unit where agricultural profitability was non-zero (indicating agricultural activity occurs within planning unit). Planning units where no agricultural activity currently occurs were assigned a transaction cost of $5000 so they would not be automatically protected. Choice of transaction costs are best guess, and will be dependent on the type of conservation project undertaken. The conservation feasibility metric used here, agricultural profitability, provides a surrogate estimate for a diversity of actions; from adopting lower stocking rates to setting aside wetlands and remnant woodlands; facilitated either by landholders or by designation of protected areas. The prioritization method used is sensitive to the relative values of planning units, rather than their absolute values.

4.3.2 Conservation features and targets

We obtained distribution maps for 42 bird species thought to be nomadic or possibly nomadic occurring the in the arid and semi-arid zones of Australia from Runge et al. (2015). Nomadic species range over large areas, and may show different movement patterns under different environmental conditions (Dean 2004) limiting the ability of field experts to reliably classify species as nomadic. For this reason, we include any species where nomadism is indicated in part or all of their range. Grey Fantail (Rhipadura albiscapa), a predominantly coastal species, was excluded from the analysis. We use IOC nomenclature for all species.

The distribution maps took the form of monthly time-sliced bird distribution maps for the period June 2000 to March 2011, constructed by matching occurrence data with environmental conditions at the time of the observations (130 maps per species). The time period was limited by the availability of MODIS-derived environmental layers underlying these modelled distribution maps, which only came online in early 2000. These maps provide quantitative estimates of monthly habitat suitability for each species, at 0.05° resolution, clipped to exclude regions where the species is unlikely to occur. The conservation value of each feature in each planning unit was calculated as the product of the area of the planning unit in km2 and the area-weighted mean habitat suitability of the distribution map cells (0.05°) that overlapped the planning unit (10x10km).

Initially we trialled representation targets scaled according to the size of species’ geographic range across time following Rodrigues et al. (2004), but these resulted in very large parts of the landscape being prioritised, driven by the requirement to protect 100% of the range of certain small-ranging species. We therefore chose to set the fixed representation target of 30% for each conservation

58 feature, meaning that we were aiming to protect 30% of each species’ distribution according to the scenarios below.

1. Static scenario: Find the minimum set of reserves that maintains complementary and representative coverage of the distribution averaged across time for all species. This scenario was based on species distributions averaged across all 130 time-slices yielding one conservation feature per species (42 conservation features in total)

2. Time-sliced scenario: Find the minimum set of reserves that protects habitat use across time. This was based on species estimated distributions in January, April, July and October of each year in the study period with the distribution for each species during each of those month-year combinations being input as a separate conservation feature (43 conservation features per species, 1806 conservation features in total). We excluded the other months from this analysis to ensure the prioritization problem remained computationally tractable, while still representing seasonal habitat use.

3. Annual scenario: Find the minimum set of reserves that accounts for inter-annual variability in target species distributions. This was based on the average habitat suitabilities for each of the 12 years in the study period (12 conservation features per species, 504 conservation features in total).

4. Monthly scenario: Find the minimum set of reserves that accounts for monthly variability in target species distributions. The monthly scenario was based on estimated species distributions which had been averaged across all occurrences of each month in the study period (12 conservation features per species, 504 conservation features in total).

5. Bottleneck scenario: Find the minimum set of reserves that protects each species’ distribution when its geographic range is at its minimum. This was based on the mapped species distributions in the month of the minimum geographic range extent across the time series for each species, and yielded one conservation feature per species, ignoring their distributions at other times (42 conservation features in total).

4.3.3 Prioritizing habitats for nomads

We identified priority regions for conservation action using the conservation planning software Marxan version 2.43 (Ball et al. 2009). Marxan uses a simulated annealing algorithm to prioritize

59 areas that minimize the cost of the final set of planning units while meeting representation targets for conservation features such as species distributions (the objective function). We performed 100 runs for each scenario and set the boundary length modifier (BLM) to zero. By eliminating the boundary length term from the objective function we ignore spatial connectivity, thus assuming all species can reach available habitat and that management of the resulting protected areas is not significantly hampered in isolated planning units. We believe this is reasonable across the arid and semi-arid zones, where the primary form of management for bird conservation is adjusting grazing pressure (Reid & Fleming 1992). We ran the prioritization under five scenarios, and identified the optimal spatial distribution of the protected area designation for each scenario i.e. the set of planning units that met the representation target whilst minimizing cost. We also calculated selection frequency, the number of times a planning unit was selected across the 100 runs, a measure of the importance or irreplaceability of each planning unit. We compared the spatial concordance of the resulting prioritizations by calculating Bray-Curtis dissimilarity in R version 3.0.0 (http://www.r-project.org/).

4.3.4 Coverage of current protected area estate

Planning units were defined as protected where at least 50% of their area was covered by a protected area listed in the Collaborative Australian Protected Areas Database as IUCN management category I-IV (CAPAD; AGDEWR 2012). The generally large size of most arid zone protected areas meant that there was almost no difference between the overall amount of land under protection in the CAPAD database (14.8%) and the proportion of the planning units designated as protected (14.6%), suggesting omission errors where a small protected area occurred within a much large planning unit that was then classified as unprotected, were only a very small source of error in the dataset. The proportion of each species’ geographic distribution currently represented in protected areas was estimated by summing the conservation value within planning units designated as protected and dividing this by the sum of conservation value in all planning units, for each of the 130 time periods. Mean protection for each species is the average of those values across time. Geospatial analyses were conducted in Python 2.6.5 (https://www.python.org/) and ArcGIS 10.0 (http://www.esri.com/).

4.4 Results

Taking into account the movements of nomadic species when planning for their conservation entailed protecting more area and locating those protected areas in different places in comparison with a simple static approach that ignores species’ changing distributions over time. The ‘time- sliced’ scenario required the greatest area for conservation action, with higher cost than other scenarios ($177 million, 2.02 million km2; Table 4.1). It also showed only limited spatial congruence with the static scenario, with a Bray–Curtis dissimilarity of 21.7% (Table 4.2). Intuitively, total cost and reserve area were lowest under the bottleneck prioritization, which attempts to represent species distributions when they are at their minimum ($104 million, 1.59 million km2).

Table 4.1 Cost and area prioritized for each of the five scenarios.

Number of Total cost conservation Area selected Scenario features (million km2) ($ million) 1 Static 42 1.89 130 2 Time-sliced 1806 2.02 177 3 Annual 504 1.96 142 4 Monthly 504 1.94 138 3 Bottleneck 42 1.59 104

Table 4.2 Comparison of spatial dissimilarity of the five scenarios, Bray–Curtis dissimilarity 0 = identical, 100 % = completely dissimilar.

Static Time-sliced Annual Monthly Time-sliced 21.7 % Annual 15.0 % 16.5 % Monthly 10.2 % 18.0 % 12.6 % Bottlenecks 30.9 % 34.7 % 31.7 % 31.3 %

Changing the conservation goal (i.e. scenario) often resulted in dramatically differing patterns of the locations of priority conservation actions. The most similar solutions were the monthly and static (Bray–Curtis dissimilarity 12.6%) and the annual and monthly scenarios (Bray–Curtis dissimilarity 10.5%; Table 4.2). The spatial pattern of the bottleneck scenario was the most divergent from other

61 scenarios (Bray–Curtis dissimilarities ranging from 30.9% to 34.7%). The time-sliced solution was more divergent from the static and bottleneck solutions (Bray–Curtis dissimilarity 21.7% and 34.7% respectively) than from the monthly and annual scenarios (Bray–Curtis dissimilarity 18.0% and 16.5% respectively), suggesting that temporal variation in the distribution of nomads is relatively well represented by annual distributions.

Individual planning units differed in their level of contribution to alternative scenarios (as measured by selection frequency). There were many irreplaceable sites in eastern Australia that were selected in each of the 100 runs under all scenarios (Figure 4.1). The static scenario also showed high irreplaceability in south-western Australia, the Nullarbor and the deserts of northern Australia, in contrast to the time-sliced scenario where these regions show greater spread and lower irreplaceability of sites. Prioritization under the time-sliced scenario put higher emphasis on eastern Australia than in the static scenario. The conservation priority of any given planning unit across much of central and western Australia was lower (as suggested by low selection frequencies) than that of the static scenario, though the region was still important to meeting conservation targets. Prioritization of features during bottlenecks resulted in selection of fewer planning units than the other scenarios, with lower emphasis on eastern Australia.

On average, 14.5% of the geographic distribution of all species is currently represented in the Australian protected area estate. However, while some species such as the Scarlet-chested Parrot (Neophema splendida) are well protected on average (39.8% mean protection across all 130 time- slices), there were many species only poorly represented in current protected areas (Figure 4.2). Painted Honeyeater (Grantiella picta), and Plum-headed Finch (Neochmia modesta), both eastern Australian species, are very poorly protected (mean protection 2.39% and 1.35% respectively;Table 4.3). The range of Yellow Chat (Epthianura crocea) is also poorly represented in the current protected area estate (5.22%). Additionally, species move in and out of protected areas across time (Figure 4.2) and some species, such as Chestnut-breasted Whiteface (Aphelocephala pectoralis) and Letter-winged Kite (Elanus scriptus) showed very high variability in the amount of their area of occupancy that is protected over time. During some time periods they are almost completely unprotected, while at other times a high proportion of their distributions occur within protected areas.

Figure 4.1 Selection frequencies and difference maps of spatial prioritization under five scenarios. Selection frequency (how often a planning unit (PU) is chosen across 100 runs) under scenario: a) static b) time-sliced c) annual d) monthly e) bottleneck; Dark blue = PU chosen in 100 runs, white = PU never chosen; .and difference maps of static vs f) time-sliced g) annual h) monthly i) bottleneck. Colours indicate the difference in selection frequency between the static scenario and the current scenario. Blue = PU chosen in current scenario, but not in static scenario, red = PU chosen more often in the static scenario. White = PU selected (or not selected) equally in both.

Table 4.3 Ten most poorly protected species. Species in bold are the least protected both averaged across time and during bottlenecks.

Mean % range % range Least protected on average protected Least protected during bottlenecks protected Plum-headed Finch Neochmia modesta 1.35 Chestnut-breasted Whiteface Aphelocephala pectoralis 0.00 Painted Honeyeater Grantiella picta 2.39 Plum-headed Finch Neochmia modesta 0.55 Crimson Chat Epthianura crocea 5.22 Painted Honeyeater Grantiella picta 1.51 Letter-winged Kite Elanus scriptus 6.48 Letter-winged Kite Elanus scriptus 3.12 Flock Bronzewing Phaps histrionica 6.81 Black-winged Kite Elanus axillaris 5.19 Striped Honeyeater Plectorhyncha lanceolata 7.25 Cockatiel Nymphicus hollandicus 5.56 Cockatiel Nymphicus hollandicus 8.17 Flock Bronzewing Phaps histrionica 5.96 Gibberbird Ashbyia lovensis 8.44 Black Falcon Falco subniger 6.40 Black Falcon Falco subniger 8.62 Crimson Chat Epthianura crocea 6.44 Pictorella Mannikin Heteromunia pectoralis 9.46 Gibberbird Ashbyia lovensis 6.99

4.5 Discussion

Protecting mobile species entails incorporating their movement dynamics into systematic conservation tools (Runge et al. 2014). Despite increasing awareness of this in the literature, and some attempts to incorporate the dynamics of predictable migrations (Martin et al. 2007; Iwamura et al. 2014) nomads have thus far been overlooked. We explored the spatial prioritization of a suite of highly mobile nomadic species in a static conservation network across a range of conservation objectives, both incorporating and ignoring their spatial dynamics across time. Regardless of the objective, incorporating the temporal dynamics of species into spatial prioritization increases the area of land prioritized for conservation action and reduces the importance of any one site in the conservation plan. The patterns of expansion and contraction in these dynamic species vary among years. As a consequence, in a static conservation scheme a wide range of sites needs to be prioritised even though the species won’t be present in those sites at all times. The spatial structure of the prioritisation scheme changes with the underlying objective, and planning to maintain habitat across time, or maintain bottleneck refugia, or maximize coverage of overall range, all result in spatially divergent prioritization schemes. Prioritizing bottlenecks, sites critical to the species when its distribution is at a minimum, is the most cost-effective and spatially constrained solution, and it could be argued that protection is most important when nomadic species’ distributions are at their minimum.

The currently accepted approach for incorporating the distributions of most species into systematic conservation planning uses the range of these species pooled across time without considering the dynamics within that range (Gilmore 2007; Watson et al. 2011). Our results show that spatial prioritizations under such an approach will differ significantly from those that incorporate movement dynamics, and risks leaving the species unprotected at certain times. In our case study, assuming that the birds’ distributions are static underemphasizes the importance of high-value eastern regions, overprotects central and western Australia and is likely to lead to loss or decline of eastern inland species.

When spatial dynamics were incorporated into planning, the conservation planning objective had a large impact on both the cost and spatial pattern of the resulting prioritization. For example, sites prioritized across eastern Australia show high irreplaceability under every scenario, where high agricultural profitability across the region limits conservation actions to the few remaining intact patches. However the bottleneck scenario, which emphasizes very different spatial priorities

65 compared to the other scenarios, places less emphasis on conservation actions in eastern Australia. Indeed, species richness during bottleneck times is concentrated in a band across central northern Australia, with low richness across the eastern third of the continent (Figure 4.3a). In contrast, overall species richness (as represented by aggregated distributions across time) is spread widely across the central inland (Figure 4.3b), though it is more consistently focused on sites in the Great Sandy Desert, Tanami and Great Victoria deserts across time (Figure 4.3c).

Figure 4.3 Maps of nomadic bird species diversity a) during bottlenecks b) total aggregated across time c) average diversity at any point in time.

While protected areas are one of the best tools currently available to conservation managers, our results also indicate that protected areas alone may be insufficient for conserving these birds in perpetuity. On average, Australia’s protected area estate covers 14.5% of the distribution of these species across time, and up to 80% of the distribution of some species at certain points in time. However, because the distribution of nomadic species shifts around the landscape, certain species remain poorly protected during parts of their lifetime (Figure 4.2), especially those whose distributions fluctuate widely (Runge et al. 2015). For instance, the internationally Vulnerable Grey Falcon (Falco hypoleucos) has a mean protection of 13%, with a minimum of 10.5%. Plum-headed Finch and Painted Honeyeater, both found in the agricultural zones of the eastern third of the continent, have a mean protection of just 1.4% and 2.4% respectively. Investment in areas outside the current protected area estate for such species is clearly necessary (Figure 4.4). Three key areas for urgent investment are the Mitchell grass plains of the Gulf country, the Mulga country of western Queensland and the Cobar Peneplain of central New South Wales, which show up as high priority investments under all five scenarios.

The large area and isolation of the sites required for protection under each scenario (one third of the landscape under the time-sliced scenario to one quarter under the bottleneck scenario) means

66 cumulative management costs may limit the feasibility of a conservation approach based solely on protected areas, despite the relatively low agricultural value of much of the region consistent with previous studies on conservation of dynamic systems (Lourival et al. 2011). More economic conservation actions outside of protected areas may involve working with landholders to limit overgrazing of shrubs and native grasses, maintain vegetation along waterways or in ephemeral swamplands, or manage feral predators, although the different costs associated with these actions may change the location of some priority areas. A move away from reliance on static protected areas into large-scale integrated land management, where conservation actions and human land use are intertwined will be crucial for the majority of nomadic species and new developments are constantly emerging that allow the co-existence of human resource use (e.g. cropping and grazing) and biodiversity conservation.

Lack of ecological knowledge might often limit our understanding of whether managing species across time or during bottlenecks is the more suitable approach. However, a suite of ‘no-regrets’ sites in eastern Australia are consistently prioritized across all planning goals (Figure 4.4a; Carvalho et al. 2011). Conservation investment at these sites is resilient to differing hypotheses or approaches to dealing with nomadic behavior and so may prove a good starting place for investment. Other sites are robust to uncertainty in some but not all scenarios and planners will need to decide which of the scenarios are more likely to represent the resource needs of species. The mechanisms driving the response of nomads to resource availability are most likely species- and threat-dependent –

67 some species might best be represented by a bottlenecks planning approach, but other species with high inter-annual variability in distribution might be best represented by an inter-annual approach. Still other options include more complex decision-support applications that explicitly account for uncertainty, such as Marxan with Probability (Tulloch et al. 2013c) or Zonation (Moilanen et al. 2009b).

Generating time-sliced distributions is a time-consuming exercise, and relies on specialist skills and adequate data with which to build models. Our study benefited from eleven years of citizen science surveys across a generally data-poor region. Lack of inter-annual surveys would limit applicability of this approach for many non-avian species. Where budget, data or time constraints limit the generation and use of time-sliced distributions, estimates of spatial distribution can be generated through expert elicitation approaches such as Bayesian Belief Networks (Smith et al. 2007; Murray et al. 2009). Our results indicate that a prioritization based on the annual distribution of these birds is a good surrogate for one incorporating shorter time-scale dynamics.

While here we consider only a static conservation network, a dynamic conservation scheme, where the sites for conservation action track the movements and population dynamics of species across time, could limit the area under conservation action at any one time and may be appropriate where threats are also dynamic (Bengtsson et al. 2003; Costello & Polasky 2004; Grantham et al. 2008). Several options for dynamic implementation of conservation action have been proposed (Golovin et al. 2011; Grantham et al. 2011; Levin et al. 2013b), and advances in computational methods in other fields may provide further options (Jafari & Hearne 2013; Minas et al. 2014; Mortazavi-Naeini et al. 2014). Success of such an approach will rely on a management framework which allows for timely identification of sites and rapid implementation of conservation action at those sites.

The prioritisation approach illustrate here is based entirely on distributional information, and neglects the interactions of resource and site use across time, which may often have unexpected population consequences (Studds et al. 2008). One further limitation of this study is that our prioritizations ignore connectivity between sites, making the assumption that species can move freely throughout their distribution as it shifts across time periods. While this is probably a fair assumption for the species considered, the approaches to prioritization taken here may fail to maintain adequate connectivity across time in species with limited dispersal capabilities. While there has been much recent research on connectivity in conservation planning (Laitila & Moilanen

2013; Pouzols & Moilanen 2014) it would be fruitful to explore the capabilities of systematic conservation planning software to incorporate both connectivity and spatio-temporal dynamics.

4.6 Conclusion

The success of conservation planning for nomadic species will be dependent on identifying actions that maintain viable populations across both space and time and discovering ways to integrate those actions into human land use (Bolger et al. 2008; Martin et al. 2007). By incorporating time-sliced distributions as multiple conservation features we present a simple approach for accounting for temporal and spatial dynamics in spatial prioritization schemes for highly dynamic species. We have shown that dynamic distributions strongly influence the optimal spatial configuration of conservation actions. Our results highlight that movements of species are often far from simple, and conservation of dynamic species depends on accounting for these complex patterns. While Australia’s current protected area estate performs well for some species, targeted conservation investment is necessary to maintain viable populations across all nomadic birds into the future.

Chapter 5

Conserving mobile species

This chapter has been published:

Runge, CA, TG Martin, HP Possingham, SG Willis, and RA Fuller. 2014. Conserving mobile species. Frontiers in Ecology and the Environment. 12: 395-402. doi:10.1890/130237

5 Conserving mobile species

5.1 Abstract

The distributions of many species are dynamic in space and time, and movements made by individuals range from regular and predictable migrations to erratic, resource-driven nomadism. Conserving such mobile species is challenging; the effectiveness of a conservation action taken at one site depends on the condition of other sites that may be geographically and politically distant (thousands of kilometres away or in another jurisdiction, for example). Recent work has shown that even simple and predictable linkages among sites caused by classic migration can make migratory species especially vulnerable to habitat loss, and substantially affect the results of conservation prioritizations. Species characterized by more erratic or nomadic movements are very difficult to protect through current conservation planning techniques, which typically view species distributions as static. However, collaborations between migration ecologists, conservation planners, and mathematical ecologists are paving the way for improvements in conservation planning for mobile species.

In a nutshell:

• Mobile species require new approaches in conservation planning • Accounting for the dependencies among sites and populations is vital for successful conservation of mobile species • Decision-theoretic approaches allow robust conservation decisions to be made, even in cases where migrations are poorly understood

5.2 Introduction

Conservation planning has tended to assume that the targets of management, such as species or ecosystems, are static in space and time (Pressey et al. 2007). However, more than 12% of the world’s vertebrates make long-distance movements, whether migratory or nomadic, and mobile species occur on every continent and in every ocean (Robinson et al. 2009). Theory for conserving mobile species is in its infancy, and there are only a few examples of conservation planning for migratory or nomadic species (Martin et al. 2007; Grantham et al. 2008; Klaassen et al. 2008; Sawyer et al. 2009; Sheehy et al. 2011; Singh and Milner-Gulland 2011; Iwamura et al. 2014).

Here, we address some of the issues specific to conservation planning for mobile species, review progress so far in solving those issues, and present an associated research agenda.

Table 5.1 Descriptions of large-scale animal movements

Migration A cyclic and predictable movement beyond a home range. From altitudinal migration up and down a mountainside or stream, to partial migration where certain populations migrate and others remain sedentary, and differential migration where certain groups within a population such as females, males, or juveniles migrate. May entail a single direct trip or a gradual journey using stopover locations. Breeding and non-breeding grounds can be spatially distinct or overlapping. Nomadism Wandering movements without fixed breeding grounds, though often some seasonal directionality (Dean 2004). Breeding occurs when and where conditions permit, rather than in fixed times and places. Nomadic species may become sedentary at certain times in their life cycle, or under particular climatic conditions, reverting to nomadic movements as resource distributions change. Often associated with arid regions, nomads commonly occur where there is high inter-annual variability in resource availability, such as pelagic species reliant on moving fish stocks and tropical forest animals that depend on flowering or fruiting events. Irruption In some species, normally sedentary individuals occasionally undertake long-distance movements, often in response to unusual spikes or troughs in resource availability. Examples include boreal forest birds such as pine grosbeaks (Pinicola enucleator) and spotted nutcracker (Nucifraga caryocatactes). These expansions may occur as a shift in breeding distribution to take advantage of a resource boom (irruption coincides with boom), to avoid a resource failure such as food shortage, or as a competition-driven dispersal event of unusually high numbers of juveniles (irruption post-boom). Intergenerational Several insects, such as the monarch butterfly (Danaus plexippus) and North American relays green darner dragonfly (Anax junius), undergo regular migrations over multiple generations. Monarchs undergo a multi-generational migration from their non-breeding grounds in Mexico to their most northern breeding sites in Canada, breeding up to four times during the annual cycle (Flockhart et al. 2013). In the case of the green darner, once the adults complete the southward migration, they die and the next generation begins the northward movement during the following spring (Russell et al. 1998).

Movements by mobile species vary from regular “to-and-fro” migrations to less predictable, resource-driven nomadic wanderings. Some species exhibit irregular long-distance irruptions, driven by peaks or troughs in resource availability, while others perform complex intergenerational relays (Table 5.1). Mobile species can perform important ecosystem functions (e.g. regulating prey abundance or delivering nutrient inputs) and conserving movement as a process may be just as important as conserving the species themselves (Shuter et al. 2011).

Figure 5.1 In this theoretical example, habitat loss has affected one-eighth of the total habitat available to a species that occurs in two patches. If habitat quality and population abundance are evenly distributed within and among patches, we might predict that a sedentary species (a) will decline in total population size by one-eighth as a result of the habitat loss. Where the two patches are linked by migration (b), we might predict a population decline of one-quarter because the entire population passes through the affected patch at some point during its life cycle. If one habitat patch is lost altogether, extinction of the migratory species will result.

5.3 Accounting for dependencies among sites

The benefits of conservation actions for mobile species taken in one place (e.g. the designation of a protected area) depend on the magnitude of threats and the success of actions taken elsewhere, making it difficult to evaluate the conservation value of any particular location in isolation (Martin et al. 2007; Iwamura et al. 2013). In the extreme, if all individuals of a species regularly move between two areas, the area in more critical condition (i.e. characterized by a lower carrying capacity or where reductions in birth rate or survivorship are greater) will dictate the overall status of the species (Figure 5.1; see Sutherland 1996), and conservation measures taken in the less critical

74 area could be redundant. Although possibly occupied only for a short period of time, stopover sites or drought refuges could also be crucial to a large proportion of the population; thus, a relatively small amount of habitat loss could, in theory, lead to rapid extinction (Figure 5.2; Weber et al. 1999). For example, the number of migratory shorebirds using the East Asian–Australasian Flyway (EAAF) has declined dramatically in the past few decades, and evidence implicates habitat loss at important stopover sites in the Yellow Sea (Murray et al. 2014). If this hypothesis is correct, then action to manage shorebird habitat elsewhere in the Flyway might fail to halt the decline of these birds without corresponding management at stopover sites in eastern Asia (Figure 5.4). Similarly, the migratory leatherback sea turtle (Dermochelys coriacea) is declining as a result of a combination of egg-poaching at its nesting sites and mortality from both inshore fisheries and pelagic long-line fishing. International restrictions on pelagic long-line fishing will not halt the decline of this species without corresponding effort at inshore locations and nesting sites (James et al. 2005).

Despite these dependencies among sites, mobile species may be able to avoid degraded sites as well as some of the impacts of habitat loss by virtue of their ability to travel long distances. Indeed, an assessment of species included on the International Union for Conservation of Nature (IUCN) Red List suggests that mobile species are not more likely to be classified as globally threatened and are not being added to the IUCN Red List at a faster rate than sedentary species (Kirby et al. 2008). However, this finding might simply be a function of the comparatively large geographic range size of migratory species, and further theoretical and empirical investigation is required to understand whether mobile species are, as a general rule, more or less vulnerable to threats than their sedentary counterparts. Moreover, alterations already observed in migratory timing and routes in response to habitat loss and climate change underscore the urgent need for conservation practitioners to understand the extent to which mobile species can dynamically respond to these threats (Kirby et al. 2008; Cox 2010).

Choosing conservation areas for sedentary species commonly involves identifying the locations that collectively, and for least cost, contain the greatest number of species or largest amount of suitable habitat (Moilanen et al. 2009a). Site selection for mobile species is necessarily more complex. First, calculating the spatial configuration of sites may involve not just one type of habitat or resource but several, all of which must yield suitable resources at the appropriate time and have the proper spatial configuration. For instance, many migratory ungulate populations have declined worldwide, even where species are well represented in protected areas (Craigie et al. 2010). Some protected areas have been shown to inadequately represent crucial resources, such as prerequisite conditions for breeding or non-breeding periods, or the full pathway of traditional migration routes required by the animals (Bolger et al. 2008). Second, priority areas for mobile species may not be the breeding or non-breeding grounds but rather the migratory corridors, bottlenecks, or refugia – regions that are crucial to a large proportion of a population at some comparatively brief point in their life cycle (Buler and Moore 2011); for example, recent tracking studies have revealed that Mongolian saiga (Saiga tatarica mongolica) are funneled through narrow corridors during migration as a result of steep topography (Figure 5.3)

Threats to these bottlenecks could cause major changes to metapopulation dynamics and survivorship for this critically endangered species. Similarly, human encroachment and changes in agricultural practices in southern Africa are restricting access to traditional migration routes, resulting in marked declines of ungulates and long-lasting impacts to ecosystems (e.g. changes in

76 nutrient cycling and predation pressure; Fynn and Bonyongo 2011). Even relatively intact migratory routes face imminent disruption from continued, human-induced disturbances to land- and seascapes (Singh and Milner-Gulland 2011).

Large-scale conservation initiatives struggle to address migratory connectivity, despite considerable focus on the specific conservation needs of migratory species in the literature. For instance, the US National Fish, Wildlife, and Plants Climate Adaptation Strategy (Small-Lorenz et al. 2013) does not address the needs of migratory species in climate-change vulnerability assessments; similarly, despite being responsible for managing a large number of charismatic migratory species, the US National Park Service has yet to develop a comprehensive plan to deal with migratory species (Berger et al. 2014).

5.4 Conservation objectives for mobile species

Here we present an overview of the tools and approaches that may prove useful in conservation planning for migratory species. While there have been few working examples of spatial prioritization for conserving migratory species, the needs of migratory species can, to a certain extent, be incorporated into existing frameworks. The approach taken will depend on objectives influenced by both the ecology of the species of interest and factors such as project timeframe, budget, and expertise.

Objectives in conservation planning for mobile species must explicitly account for the movement of individuals. Current approaches for sedentary species tend to treat the distribution of each species as a single conservation feature (Rondinini et al. 2006; Moilanen et al. 2009a). These approaches could be adapted to meet the needs of migratory species simply by treating different parts of the movement cycle (eg breeding grounds, non-breeding grounds, and stopover sites or migration corridors) as separate conservation features. Information on the locations of sites and resources used by mobile species is often readily available, and where it is not, species distribution modeling or consultation with experts (ie expert elicitation; Martin et al. 2012a) can help generate predictions of distributions from available data. However, such approaches may fail to protect subpopulations where there is strong population segregation between sites, and may fail to allocate conservation actions to bottlenecks that support a disproportionately large part of the population at certain times.

Objectives that go one step farther – by considering the connectivity between different parts of the movement cycle – can help to avoid functionally important areas being omitted from conservation plans. Martin et al. (2007), for instance, used a decision theory approach to model a conservation strategy for the American redstart (Setophaga ruticilla), a bird that migrates between breeding grounds in North America and non-breeding grounds in Central America (Figure 5.5). Protected area placement was compared under two conservation objectives: maximizing the population size across the non-breeding distributions without consideration of the connectivity between the breeding and non-breeding sites, and maximizing the population size across the entire range by adding the constraint that maintained a minimum of 30% of a population in each of five breeding regions. The resulting conservation strategies for each objective were highly divergent, with redstart

78 populations in one of the five breeding regions very poorly protected when connectivity was ignored..

Information on migratory connectivity has been incorporated into conservation planning in both the marine (Moilanen et al. 2008; Linke et al. 2011) and terrestrial (Martin et al. 2007; Klaassen et al. 2008) realms, although effective working examples are rare. Existing prioritization approaches can be adapted where connectivity is both spatially continuous (Kool et al. 2013) and geographically discrete (Beger et al. 2010), as are the migrations of many bird species. Advances in tracking technologies, genetic approaches, and stable isotope analysis are proving to be useful tools for identifying connectivity among sites (Webster et al. 2002), and consultation with experts can fill in gaps where such information is not available. For example, the synthesis of expert opinions on the structure of EAAF migration routes for shorebirds enabled the identification of locations that supported cost-effective habitat management in the face of sea-level rise (Iwamura et al. 2014).

79 governmental organizations working across the region. The Partnership has already listed a network of more than 100 important sites across the Flyway in 16 countries. Image credit: ©Dean Ingwersen

Threats from global change – particularly climate change – can have complex and unforeseen impacts on population dynamics in migratory species, and conservation success may be dependent on understanding and managing the impacts of these threats on factors such as fecundity and survival (Cox 2010; Webster 2002). Innovations in demographic modeling (Frederiksen et al. 2014), mechanistic modeling of migration (Bauer and Klaassen 2013), and spatial population models (Naujokaitis-Lewis et al. 2013) have led to improvements in how to map movements of mobile species and their population dynamics across the full life cycle. Understanding the links between environmental factors and species demography allows us to distinguish often unanticipated threats and identify conservation actions with the greatest population impact. Such modeling is particularly important in networks with complex population flow dynamics and low mixing of subpopulations between sites, and in species for which habitat degradation is more of a threat than habitat loss. Because of their current reliance on specialized analysis and intensive collection of demographic data, such approaches will likely only ever be applied in single-species management of highly threatened species. However, advances in the statistical tools available for the interpretation of extensive datasets (such as those generated by citizen science eg eBird; http://ebird.org/) may broaden the applicability of these intensive approaches (Zipkin et al. 2014). Nonetheless, despite major advances in the ability to model species’ responses to threats and environmental conditions, conservation ecologists are far from being able to incorporate such models within formal spatial prioritizations, given the enormous computational size of the problem.

The dual threats of habitat loss and climate change may require solutions that maximize future evolutionary potential and minimize risk from stochastic events (Hoffmann and Sgrò 2011; Hole et al. 2011). Such solutions would focus on the conservation of multiple subpopulations and dynamic migratory corridors. Conservation planning software such as MarProb allow information on the probability of species presence or threats to be incorporated into the prioritization algorithm (Carvalho et al. 2011).

Critically, existing prioritization approaches allow us to incorporate the costs of conservation actions with ecological information such as connectivity, habitat suitability, or population density (Moilanen et al. 2009a). A study in California used the conservation planning software Marxan to prioritize a multi-species conservation network for migratory shorebirds and waterfowl (Stralberg et

80 al. 2011), taking into account cost information.. Population densities at each site were estimated through a combination of survey data and expert judgment on habitat use, and were used in conjunction with cost information to prioritize sites for conservation action across the region. Conservation targets were set separately for each site (and season) to accommodate potentially distinct populations. While this study considered only the parts of the migrants’ life cycle spent within California, this approach could in principle be extended to design conservation networks across the full life cycle.

5.5 Conserving mobile species with incomplete and uncertain information

Given financial and time constraints, an intensive research-driven approach to conservation will not be feasible for the vast majority of migratory species, especially where little is known about migratory connectivity. Where information is limited, there are basically three choices for conservationists: investing in activities that improve current knowledge (i.e. “learning more”), using existing information to estimate the optimal conservation plan, or undertaking a combination of learning while taking action (i.e. adaptive management; Keith et al. 2011). Often, learning more is not the most effective way to achieve conservation outcomes, because of the delay in action, the risk of catastrophic population declines while new knowledge is acquired (Martin et al. 2012b), and the fact that resources might be diverted from on-the-ground management (McDonald-Madden et al. 2010a). The use of decision-theoretic approaches from applied mathematics and artificial intelligence can aid decision making where data are scarce (Martin et al. 2014). These techniques can also demonstrate how to optimally allocate time and resources between learning and taking action across space and time (Chadès et al. 2011). The application of decision science to solve migratory species conservation problems follows the same basic principles as any well-designed prioritization process: (1) define a clear objective (e.g. what to minimize or maximize); (2) specify a set of conservation actions from which a subset will be chosen as priorities; (3) build a model of how specific conservation actions will help meet the objective; (4) consider resource constraints (i.e. time and money); and (5) implement decisions in a way that promotes learning (Gregory et al. 2012; Game et al. 2013).

In practice, information on system behavior (such as migratory connectivity or survival across different parts of the migratory life cycle) is often lacking. In these cases, expert elicitation is proving useful (Martin et al. 2012a) and has been used to estimate population size of (Martin et al. 2007), habitat use by (Stralberg et al. 2011), and connectivity in (Iwamura et al. 2013) migratory species. Uncertainty in parameter estimates can be accounted for through the use of structured expert elicitation techniques. For instance, estimates of survival for the monarch butterfly (Danaus plexippus) during a portion of its migratory flyway were recently elicited from experts and used to parameterize the first year-round population model for a migratory insect (Flockhart et al 2014). To account for uncertainty, Flockhart et al. (2014) asked experts to estimate a range of survival values (upper bound, lower bound and best guess) and then to evaluate the probability that survival would fall within that range (Speirs-Bridge et al. 2010)..

Many of the more advanced techniques in decision science have yet to be applied formally to conservation problems associated with mobile species, suggesting possibilities for future applications. For example, it should be possible to design conservation plans that are robust to different plausible patterns of connectivity, or to cases where connectivity changes as a result of threats. Techniques based on decision theory can also highlight what new information would be most critical for improving conservation decision making in a particular situation, so that research effort can be focused on gaining new knowledge most likely to lead to a change in management (Grantham et al. 2009; Runge et al. 2011; Nicol and Chadès 2012).

5.6 Defining an appropriate suite of actions

Conservation planning is about choosing actions, not just choosing sites (Wilson et al. 2009; Game et al. 2013). For mobile species where movement patterns are unpredictable or changing in space and time, the suite of potential actions may be diverse and complex (Bull et al. 2013). In addition to fixed actions in fixed locations, resource managers may need to implement conservation actions that are ephemeral and depend on the state of the system. State-dependent actions have already been applied to conservation of static species (McCarthy et al. 2001; Johnson et al. 2011), and are particularly relevant to mobile species. Examples of state- or time-dependent actions might be to limit fisheries near sea turtle rookeries during the breeding season (James et al. 2005) or to halt wind-turbine activities during peak bird, bat, or insect migration periods (Drewitt and Langston 2006).

Dynamic alternatives to static protected areas, such as temporary stewardships or seasonally transient protected areas, may need to be considered (Bengtsson et al. 2003). These approaches are already used in marine conservation (Somers and Wang 1997; Horwood et al. 1998; Cinner et al. 2006). For instance, temporary closure of specific areas of South African long-line fisheries has been identified as an effective model for reducing bycatch of nomadic pelagic seabirds with least cost to the long-line fishing industry (Grantham et al. 2008). A key challenge for conservation biologists is to identify ways to implement dynamic protection on land where opportunities for dynamic landscape management are limited by static structures of land ownership.

Because of the extensive use of space by many mobile species, whole-landscape management will often be preferable to restricting conservation to the small zones within protected areas. An illustration of a successful whole-landscape management strategy is the conservation of pink-footed geese (Anser brachyrhynchus) in Europe (Klaassen et al. 2008). Pink-footed geese breed in Norway and winter in Denmark, the Netherlands, and Belgium, with stopover sites in Norway and Denmark. These stopover sites comprise agricultural land, causing conflict between landowners whose crops are damaged and conservation groups wanting to maintain the migration. Conservation of these birds may involve protecting key sites, compensation to farmers within a designated flyway where goose-related damage to crops is accepted, and bird-scaring techniques to limit use of non-target lands by birds. This kind of conservation initiative relies on cooperation among multiple stakeholders and is best suited to managed landscapes, where actions can be arranged dynamically across space and time. In more intact landscapes, or where resources are scarce and threats are more pervasive, more universal actions will likely be required.

5.7 Conclusions

Mobile species represent a major challenge for conservation planners. Traditional conservation planning approaches are inadequate for most situations in which species move from place to place, and we urge the development of research that (1) accounts for the dependencies among sites created by migratory connectivity, (2) determines explicitly when more knowledge about migratory connectivity will be useful for conservation, and (3) identifies actions that are dynamic in space and time. Observed rapid declines in mobile species around the world (Kirby et al. 2008) suggest that time is running out to achieve the large-scale conservation action necessary to mitigate the loss of these great wildlife spectacles.

Chapter 6

General Discussion

6 General Discussion

6.1 Thesis summary

This thesis represents a major advance in our understanding of the complexities of migratory species conservation and forms part of a growing body of work shifting the conversation from a focus on data-generation to development of pragmatic and global conservation solutions. My work highlights the interconnectedness of migratory species - that they connect us across nations, across landscapes and across time, and that our conservation solutions must recognise those connections. I discover that current conservation actions comprehensively fail to adequately protect migratory species when we consider those connections. In response, this thesis provides tools and approaches to enable conservation planners to recognise and incorporate those migratory linkages into conservation frameworks. I developed approaches for identifying movement patterns in data-poor species, improved guidelines for extinction risk evaluation of highly mobile species and discovered that accounting for movements changes the spatial pattern of conservation investment. Finally, I show how conservation planning can be adapted to better protect migratory species.

In this section I begin by briefly setting out the core achievement of each chapter. I then synthesise the thesis discussing it as a whole within the wider framework of our current understanding, before outlining key assumptions and limitations of the work not previously addressed within each chapter. Finally I present gaps and questions raised by this research, and suggest avenues for future research.

Protected areas are one of the most widely adopted conservation actions, and yet we know little of how adequately they represent migratory species. Previous summaries of global protection statistics across taxa and the recommendations for protected area expansion arising from these have completely ignored spatial dependencies across migratory species distributions (Rodrigues et al. 2004a; Beresford et al. 2011; Venter et al. 2014). In Chapter 2, I explored the impact of seasonal use of areas by migratory species on overall protection at a global scale, using migratory birds as a case study. I discovered that more than 90% of the world’s migratory birds lack protection which comprehensively represents each part of their annual cycle. My work highlights the importance of greater coherence and coordination between nations when it comes to protection of migratory species (Wilcove & Wikelski 2008; Trouwborst 2012; Lausche et al. 2013) by mapping the global disparity in conservation of migratory birds and identifying groups of nations where coordinated conservation efforts will have the greatest impact. Such coordination may be facilitated through the

86 use of international agreements, such as the CMS or bilateral agreements, or may take concepts and approaches from carbon-markets (ie REDD+; Venter et al. 2013) to develop trading schemes (Sultanian & van Beukering 2008), where countries fund protection of shared migratory species beyond their borders. This work comes at a critical time for conservation (Radeloff et al. 2012), as nations across the world are set to massively expand the protected area estate and the key message – that we need to consider the connections across the annual cycle - will guide and shape this expansion to benefit migratory species.

While there have been many calls for migratory connectivity to be considered in conservation of migratory species (Webster et al. 2002; Wilcove & Wikelski 2008; Robinson 2009) there has been limited progress in identifying and incorporating those movements into conservation planning frameworks, particularly in data-poor species. In Chapter 3 I developed a tool for discovering spatial dynamics in highly mobile and data-poor species, unlocking valuable information for improved conservation planning. I used existing citizen-science data to understand fluctuations in geographic range size and distribution, and explored the implications of those fluctuations for extinction risk evaluation and species monitoring. I discovered that species previously regarded as secure may be at a much higher risk of extinction than currently realised. While this chapter draws on as yet untested hypotheses around the relationship between nomadic species and extinction risk (Dean 2004), these findings represent the first time the needs of such highly dynamic species have been considered within a threatened species classification framework. The IUCN Red List is now a key conservation tool, guiding conservation priorities and informing policy and management (Rodrigues et al. 2006) and there are already indications that the conclusions of this chapter will influence amendments to IUCN Red List guidelines and species prioritisation.

Similarly, understanding of how and whether migratory movements alter conservation prioritisations is limited. A number of previous studies have incorporated spatial dynamics into prioritisation approaches for coral larvae (Beger et al. 2010), freshwater fish (Linke et al. 2011), and waterbirds (Stralberg et al. 2011), yet conservation action prioritisation across both time and space is a very new field. In one of the few worked examples, Grantham et al. used information on both spatial and temporal dynamics to prioritise action to reduce seabird mortalities from long-line fishing in South Africa in seasonally migratory species (Grantham et al. 2008). In Chapter 4 I add to this work, exploring how we might incorporate the spatial and temporal movements of highly mobile species into spatial prioritisation. I discovered that priority areas for conservation action

87 shift markedly when we incorporate the spatial and temporal dynamics of species. I examined how this approach might be applied to data-poor species, and provide a framework for a risk-adverse conservation approach for dynamic data-poor species. Chapter 4 advances on such existing literature by exploring the impact of dynamics, both temporal and spatial, on prioritisation for a suite of data-poor and highly dynamic species.

Successful conservation of migratory species into the future will require a shift in the way we plan and apply conservation actions to migratory species. This shift in thinking is encapsulated within Chapter 5, which draws together the concepts developed in the previous chapters to evaluate and rethink the current approach to conserving migratory species. In this chapter, I show that migratory connectivity is crucial to successful conservation of migratory species, and describe recent advances that now give us many of the tools we need to incorporate that connectivity into conservation planning. Migratory connectivity is more than just the spatial connections between sites, it also includes the events throughout the annual cycle that impact on population dynamics, which can often be complex (Marra et al. 2010). For instance, natal dispersal and the breeding location of American redstart (Setophaga ruticilla) is linked both to the quality of the site they occupy during their first tropical non-breeding season and to climatic conditions during their first spring migration (Studds et al. 2008). Most significantly, I show that lack of information need not be a barrier to conserving migratory species, and that we can make substantial progress based on what we already know by drawing on approaches such as adaptive management and using information generated through structured expert elicitation techniques (Martin et al. 2012a). Adaptive management involves, in part, predicting outcomes from conservation actions on the basis of a series of hypotheses about the study system, then observing the outcomes of those actions to strengthen the evidence for or against each hypothesis, adjusting conservation actions if needed (McDonald- Madden et al. 2010b). Adaptive management has already been applied to varying degrees in ecological problems (Keith et al. 2011). While detailed knowledge on the interaction between ecology and threats is lacking for many of the world’s migratory species, the approaches outlined in Chapter 5 allow conservation managers to address the urgent conservation needs of migratory species using existing knowledge within a systematic conservation planning framework.

While focused on short-term movements and changes in distributions, the approaches outlined here also have relevance across longer-term distributional shifts, as habitat degradation and climate change drive shifts in distribution and movement patterns among species. Already tools exist to

88 allow us to predict the outcomes of global change on the distribution and patterns of migratory species (Kearney et al. 2010; Singh & Milner-Gulland 2011; Gschweng et al. 2012) and the need to consider these shifts are recognised within international conservation initiatives (Trouwborst 2012). For some migratory species, existing protected areas will be adequate to allow them to adapt to global change, others will benefit from conservation actions to improve extent and quality of habitat (Hodgson et al 2009; Hole et al. 2011). Identifying locations where conservation investment will help maintain species across present and future distributions will be important for both migratory and sedentary species, and the approaches outlined in this thesis complement the existing body of work outlining approaches for incorporating shifting distributions into conservation planning (Carvalho et al. 2011; Carvalho et al. 2011b; Hole et al. 2011).

6.2 Synthesis

Seen as a whole, this thesis reveals that our current approaches to conservation do not adequately account for migratory species. When we couple the relative lack of conservation of migratory species with our underestimation of their extinction risk, it seems clear that we are underinvesting in conservation for migratory species. Research already shows that major bird extinctions, which to date have largely been island endemics, may soon shift to migrants (Szabo et al. 2012). Averting these losses will require coordinated and targeted global investment.

Conserving migratory species is a complex, many-faceted problem, and many of the solutions to help us do so are in their infancy. I show that accounting for migratory connections will be essential to successful conservation in these dynamic species. New solutions will be required - from improved ways of estimating extinction risk, to spatial prioritisation tools that incorporate their movements across time and space, and actions which can be applied dynamically and are adaptable to new threats faced by migratory species in a rapidly changing world.

6.3 Assumptions and model limitations

One of the basic assumptions of the work presented in Chapter 2 is that the distributions forming the basis of this chapter represent an approximation of the area occupied by the species. However, large parts of these distributions will be uninhabited or unsuitable for the species (Gaston & Fuller 2009). It is thus likely that our values underestimate the amount of occupied species habitat that is represented in protected areas (Rodrigues et al. 2004a; Somveille et al. 2013). It is worth

89 remembering within the context of our findings that such bias is likely to be consistent across both migrants and non-migrants and is unlikely to affect the substantial differences in their adequacy of protection, nor is it likely to change the result that some countries are doing better than others.

Both Chapter 3 and 4 draw heavily on distributions derived from species distribution models. Despite widespread use in extinction risk assessment and conservation prioritization (Syfert et al. 2014), aspects of SDM interpretation and application remain to be addressed (Guisan et al. 2013). These models often rely solely on environmental descriptors of species distributions, and ignore historical barriers, species interactions and evolutionary processes which also strongly influence distributions (Gaston 2003; Guisan & Thuiller 2005; Austin 2007; Jiménez-Valverde et al. 2008; Sinclair et al. 2010). Our interpretation of model outputs is constrained by incomplete knowledge on where their predictions fall on the spectrum between potential and realized niche (Jiménez- Valverde et al. 2008) and tools for assessment of model performance are limited (Araújo & Guisan 2006). Recent research indicates the distance in environmental space between presence and pseudo- absence data can affect model performance (Lobo et al. 2008; Hijmans 2012) and background sampling focused in environmental space might improve further the predictive power of the models in Chapter 3. The models were created using a subset of the Birdlife Australia Atlas data and generic variables across species; use of all the data and species-specific variables may further improve the reliability of these models.

6.4 Future directions

While this thesis takes a conservation planning philosophy, the results are not intended as prescriptions for on-ground implementation, but rather they are intended to provide insight into how to incorporate mobile species into conservation strategies. Many practical issues complicate the on- the-ground implementation of conservation planning, including institutional, cultural, and political dimensions. It would be fascinating to explore these human facets of conservation in the context of international agreements for conserving migratory species, building on the work reported in Chapter 2. In this chapter I raised the possibility of nations improving the fate of their migratory species through conservation effort in other jurisdictions. This work might be extended by exploring which countries would most benefit from collaboration towards the aim of maximizing the proportion of shared migrants meeting conservation targets. Social information can be incorporated into conservation planning through use of approaches from game theory and network theory (Bode et al. 2011; Guerrero et al. 2014), and such techniques can improve cost-effectiveness and

90 likelihood of uptake of conservation actions (Levin et al. 2013; Mazor et al. 2013). Conservation spending could be directed not just to countries where an increase in protected areas or other effective conservation actions would fill in the gaps for the greatest number of shared species, but to those countries with highest likelihood of conservation success based on the human dimension. Likelihood of conservation success could be measured by the strength of existing linkages between countries (such as trading or cultural connections; Levin et al. 2013), or by governance structures most likely to result in spending translating into permanent on-the-ground protection for migratory species. Potential future analyses could also include consideration of conservation actions for migratory species other than protected area designation.

One of the fundamental limitations to the work in Chapters 3 and 4 is our lack of understanding on the relationship between range size fluctuations, population dynamics and extinction risk. Such questions will be difficult, if not impossible, to answer empirically, but simulation experiments may provide some insights (Keith et al. 2008). Recent work on the demise of the passenger pigeon (Ectopistes migratorius; Hung et al. 2014) highlights that even hugely abundant species can decline rapidly if fluctuating environmental conditions coincide with increased threats. The lack of ecological knowledge in most existing nomadic species limits our understanding of the thresholds beyond which nomads are unlikely to bounce back from range declines, and we lack understanding on the extent to which the ability to recover depends on the timing and frequency of fluctuations.

Understanding of the fundamental drivers of nomadism is limited, and it remains to be seen whether any theoretical rules can be derived about this behavior. Many interesting questions remain to be answered. Are there spatial patterns to nomadic behavior across a species distribution? Do species shift between nomadic and sedentary, and how does that relate to resource availability and predator dynamics? Is nomadism a local phenomenon or do nomadic movements occur on a continental scale, and how much seasonality is there to those movements? What is the distinction between nomadism and migration? Conservation of these species is not necessarily reliant on answers to these questions, but they remain interesting from an ecological theory perspective nonetheless.

Two avenues for future research arise from Chapter 4. In this chapter we suggest expert elicitation might be a useful way to generate distributional information. It would be worth exploring whether accurate estimates of distribution can be derived from expert elicitation, and whether priority sites for conservation are consistent across both modelled and expert-elicited distributions. Secondly, the approach taken in Chapter 4 ignores dispersal capability of species, assuming they can reach all

91 suitable habitat. Advances in conservation planning software (Pouzols & Moilanen 2014) could be drawn upon to design a prioritization which incorporates the dispersal capability of species, with the possibility of using expert elicitation where physiological limits to dispersal are unknown (Smith et al. 2007; Murray et al. 2009).

Finally, much work remains to be done to enable migratory movements to be fully incorporated into conservation planning frameworks. Many of these ideas are outlined in Chapter 5, and those most likely to draw immediate benefits are those exploring conservation planning where information on connections, distributions and population dynamics are limited.

6.5 Final words

This thesis represents an advance in our understanding of both the issues and solutions surrounding conservation planning for migratory species. There is still much work to be done, and a long path to tread before the tools and recommendations put forward in this work become part of mainstream conservation practice. But with a little care, and some well-designed investment, future generations will be able to experience the amazing and inspiring phenomenon that is migration.

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8 Appendices

8.1 Appendix 1: Proportion of species meeting targets, by country.

Proportion of species meeting targets, by country. Targets are area-based and species-specific, set for each part of the seasonal range.

Number of Number of Percent of Percent of Number of species meeting species meeting species meeting species meeting migratory targets within targets across targets within targets across Country species borders entire range borders entire range

fghanistan 272 32 20 11.8% 7.4% Albania 218 64 26 29.4% 11.9% Algeria 232 17 22 7.3% 9.5% American Samoa 32 4 2 12.5% 6.3% Andorra 100 2 16 2.0% 16.0% Angola 157 112 38 71.3% 24.2% Anguilla 89 6 9 6.7% 10.1% Antarctica 40 9 0 22.5% 0.0% Antigua and Barbuda 99 17 9 17.2% 9.1% Argentina 263 16 19 6.1% 7.2% Armenia 230 175 21 76.1% 9.1% Aruba 67 3 7 4.5% 10.5% Australia 195 140 29 71.8% 14.9% Austria 190 21 23 11.1% 12.1% Azerbaijan 255 181 24 71.0% 9.4% Bahamas 203 24 10 11.8% 4.9% Bahrain 58 8 0 13.8% 0.0% Bangladesh 268 51 19 19.0% 7.1% Barbados 125 8 9 6.4% 7.2% Belarus 188 14 23 7.5% 12.2% Belgium 192 182 24 94.8% 12.5% Belize 211 195 16 92.4% 7.6% Benin 169 148 36 87.6% 21.3% Bermuda 18 3 0 16.7% 0.0% Bhutan 219 206 15 94.1% 6.9% Bolivia 171 140 20 81.9% 11.7% Bonaire, Saint Eustatius and Saba 90 20 9 22.2% 10.0% Bosnia and Herzegovina 210 11 26 5.2% 12.4% Botswana 124 115 29 92.7% 23.4% Bouvet Island 32 16 0 50.0% 0.0%

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Brazil 251 135 25 53.8% 10.0% British Indian Ocean Territory 18 18 2 100.0% 11.1% British Virgin Islands 118 7 8 5.9% 6.8% Brunei 125 90 17 72.0% 13.6% Bulgaria 247 242 27 98.0% 10.9% Burkina Faso 163 139 31 85.3% 19.0% Burundi 129 3 37 2.3% 28.7% Cambodia 195 177 14 90.8% 7.2% Cameroon 189 20 40 10.6% 21.2% Canada 385 53 26 13.8% 6.8% Cape Verde 45 3 4 6.7% 8.9% Cayman Islands 148 127 9 85.8% 6.1% Central African Republic 142 111 35 78.2% 24.7% Chad 179 63 35 35.2% 19.6% Chile 212 64 11 30.2% 5.2% China 520 50 35 9.6% 6.7% Christmas Island 27 15 3 55.6% 11.1% Clipperton Island 23 23 0 100.0% 0.0% Cocos Islands 11 1 0 9.1% 0.0% Colombia 243 190 27 78.2% 11.1% Comoros 33 1 5 3.0% 15.2% Cook Islands 34 4 0 11.8% 0.0% Costa Rica 237 203 22 85.7% 9.3% Croatia 227 184 27 81.1% 11.9% Cuba 195 170 10 87.2% 5.1% Curacao 71 4 7 5.6% 9.9% Cyprus 149 122 11 81.9% 7.4% Czech Republic 176 168 23 95.5% 13.1% Democratic Republic of the Congo 197 117 45 59.4% 22.8% Denmark 198 187 25 94.4% 12.6% Djibouti 169 38 19 22.5% 11.2% Dominica 100 81 9 81.0% 9.0% Dominican Republic 152 139 9 91.5% 5.9% East Timor 85 3 18 3.5% 21.2% Ecuador 210 157 25 74.8% 11.9% Egypt 251 63 17 25.1% 6.8% El Salvador 217 153 20 70.5% 9.2% Equatorial Guinea 95 78 23 82.1% 24.2% Eritrea 220 35 30 15.9% 13.6% Estonia 191 187 21 97.9% 11.0% Ethiopia 229 197 35 86.0% 15.3%

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Falkland Islands 78 8 2 10.3% 2.6% Faroe Islands 50 2 3 4.0% 6.0% Fiji 48 6 8 12.5% 16.7% Finland 197 92 24 46.7% 12.2% France 271 260 28 95.9% 10.3% French Guiana 123 111 18 90.2% 14.6% French Polynesia 39 4 1 10.3% 2.6% French Southern Territories 66 29 2 43.9% 3.0% Gabon 105 87 28 82.9% 26.7% Gambia 183 5 33 2.7% 18.0% Georgia 226 16 23 7.1% 10.2% Germany 229 225 29 98.3% 12.7% Ghana 168 127 37 75.6% 22.0% Gibraltar 135 134 17 99.3% 12.6% Greece 244 227 27 93.0% 11.1% Greenland 52 14 8 26.9% 15.4% Grenada 80 5 7 6.3% 8.8% Guadeloupe 104 90 9 86.5% 8.7% Guatemala 267 208 22 77.9% 8.2% Guernsey 111 92 17 82.9% 15.3% Guinea 178 16 37 9.0% 20.8% Guinea-Bissau 161 129 31 80.1% 19.3% Guyana 137 22 20 16.1% 14.6% Haiti 144 2 9 1.4% 6.3% Heard Island and McDonald Islands 41 41 0 100.0% 0.0% High Seas 844 749 82 88.7% 9.7% Honduras 243 153 21 63.0% 8.6% Hungary 195 193 23 99.0% 11.8% Iceland 73 30 8 41.1% 11.0% India 422 35 25 8.3% 5.9% Indonesia 212 146 26 68.9% 12.3% Iran 335 54 28 16.1% 8.4% Iraq 253 54 22 21.3% 8.7% Ireland 155 102 22 65.8% 14.2% Israel 231 69 19 29.9% 8.2% Italy 251 223 26 88.8% 10.4% Ivory Coast 159 137 38 86.2% 23.9% Jamaica 152 128 7 84.2% 4.6% Japan 263 193 23 73.4% 8.8% Jersey 116 88 17 75.9% 14.7% Jordan 203 25 15 12.3% 7.4% Kazakhstan 321 11 25 3.4% 7.8%

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Kenya 216 138 40 63.9% 18.5% Kiribati 51 11 5 21.6% 9.8% Kosovo 167 28 21 16.8% 12.6% Kuwait 153 127 10 83.0% 6.5% Kyrgyzstan 236 25 15 10.6% 6.4% Laos 231 211 15 91.3% 6.5% Latvia 193 176 22 91.2% 11.4% Lebanon 194 7 14 3.6% 7.2% Lesotho 87 7 16 8.1% 18.4% Liberia 139 23 30 16.6% 21.6% Libya 174 27 14 15.5% 8.1% Liechtenstein 105 94 16 89.5% 15.2% Lithuania 188 182 25 96.8% 13.3% Luxembourg 132 131 20 99.2% 15.2% Macedonia 204 28 24 13.7% 11.8% Madagascar 75 11 6 14.7% 8.0% Malawi 135 127 33 94.1% 24.4% Malaysia 189 8 21 4.2% 11.1% Maldives 44 34 3 77.3% 6.8% Mali 186 17 34 9.1% 18.3% Malta 58 50 6 86.2% 10.3% Marshall Islands 47 22 6 46.8% 12.8% Martinique 93 74 9 79.6% 9.7% Mauritania 217 45 30 20.7% 13.8% Mauritius 31 5 3 16.1% 9.7% Mayotte 31 1 4 3.2% 12.9% Mexico 451 148 30 32.8% 6.7% Micronesia 63 31 8 49.2% 12.7% Moldova 204 3 22 1.5% 10.8% Monaco 13 13 0 100.0% 0.0% Mongolia 271 225 20 83.0% 7.4% Montenegro 203 79 24 38.9% 11.8% Montserrat 74 1 8 1.4% 10.8% Morocco 247 3 24 1.2% 9.7% Mozambique 183 127 36 69.4% 19.7% Myanmar 316 50 23 15.8% 7.3% Namibia 166 136 33 81.9% 19.9% Nauru 32 32 4 100.0% 12.5% Nepal 290 120 18 41.4% 6.2% Netherlands 195 182 26 93.3% 13.3% New Caledonia 70 15 14 21.4% 20.0% New Zealand 121 73 15 60.3% 12.4% Nicaragua 235 217 22 92.3% 9.4%

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Niger 177 51 30 28.8% 17.0% Nigeria 202 164 40 81.2% 19.8% Niue 30 2 0 6.7% 0.0% Norfolk Island 49 15 4 30.6% 8.2% North Korea 234 7 20 3.0% 8.6% Northern Mariana Islands 59 26 7 44.1% 11.9% Norway 197 71 28 36.0% 14.2% Oman 137 82 12 59.9% 8.8% Pakistan 345 126 24 36.5% 7.0% Palau 56 3 9 5.4% 16.1% Palestina 211 10 17 4.7% 8.1% Panama 223 156 23 70.0% 10.3% Papua New Guinea 129 24 23 18.6% 17.8% Paraguay 144 9 15 6.3% 10.4% Peru 237 136 28 57.4% 11.8% Philippines 161 105 16 65.2% 9.9% Pitcairn Islands 27 7 0 25.9% 0.0% Poland 208 207 23 99.5% 11.1% Portugal 221 182 24 82.4% 10.9% Puerto Rico 145 8 9 5.5% 6.2% Qatar 93 9 5 9.7% 5.4% Republic of Congo 118 34 37 28.8% 31.4% Reunion 26 13 3 50.0% 11.5% Romania 237 214 24 90.3% 10.1% Russia 517 205 49 39.7% 9.5% Rwanda 131 98 37 74.8% 28.2% Saint Helena 53 8 1 15.1% 1.9% Saint Kitts and Nevis 81 9 8 11.1% 9.9% Saint Lucia 79 61 7 77.2% 8.9% Saint Pierre and Miquelon 92 2 9 2.2% 9.8% Saint Vincent and the Grenadines 78 4 7 5.1% 9.0% Saint-Barthelemy 90 0 9 0.0% 10.0% Saint-Martin 94 17 9 18.1% 9.6% Samoa 31 0 2 0.0% 6.5% San Marino 110 110 16 100.0% 14.6% Sao Tome and Principe 31 22 5 71.0% 16.1% Saudi Arabia 185 88 18 47.6% 9.7% Senegal 210 177 35 84.3% 16.7% Serbia 212 21 25 9.9% 11.8% Seychelles 38 16 4 42.1% 10.5% Sierra Leone 146 7 34 4.8% 23.3%

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Singapore 136 31 10 22.8% 7.4% Sint Maarten 78 61 8 78.2% 10.3% Slovakia 186 184 23 98.9% 12.4% Slovenia 202 202 24 100.0% 11.9% Solomon Islands 81 19 14 23.5% 17.3% Somalia 197 60 25 30.5% 12.7% South Africa 210 83 34 39.5% 16.2% South Georgia and the South Sandwich Islands 40 40 0 100.0% 0.0% South Korea 214 11 16 5.1% 7.5% South Sudan 199 19 37 9.6% 18.6% Spain 269 214 26 79.6% 9.7% Spratly islands 54 47 4 87.0% 7.4% Sri Lanka 162 142 13 87.7% 8.0% Sudan 238 18 32 7.6% 13.5% Suriname 125 104 18 83.2% 14.4% Svalbard and Jan Mayen 29 24 1 82.8% 3.5% Swaziland 117 10 27 8.6% 23.1% Sweden 209 70 24 33.5% 11.5% Switzerland 183 168 22 91.8% 12.0% Syria 225 59 14 26.2% 6.2% Taiwan 197 150 19 76.1% 9.6% Tajikistan 236 13 17 5.5% 7.2% Tanzania 197 173 42 87.8% 21.3% Thailand 275 234 18 85.1% 6.6% Togo 162 116 37 71.6% 22.8% Tokelau 26 5 1 19.2% 3.9% Tonga 34 4 1 11.8% 2.9% Trinidad and Tobago 137 119 15 86.9% 11.0% Tunisia 224 2 20 0.9% 8.9% Turkey 291 15 27 5.2% 9.3% Turkmenistan 251 13 19 5.2% 7.6% Turks and Caicos Islands 149 132 9 88.6% 6.0% Tuvalu 35 2 4 5.7% 11.4% Uganda 174 165 38 94.8% 21.8% Ukraine 245 8 25 3.3% 10.2% United Arab Emirates 124 67 10 54.0% 8.1% United Kingdom 198 171 27 86.4% 13.6% United States 545 123 42 22.6% 7.7% United States Minor Outlying Islands 90 73 8 81.1% 8.9% Uruguay 176 17 13 9.7% 7.4% Uzbekistan 253 53 17 21.0% 6.7%

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Vanuatu 50 4 11 8.0% 22.0% Venezuela 200 183 22 91.5% 11.0% Vietnam 274 12 17 4.4% 6.2% Virgin Islands, U.S. 124 100 8 80.7% 6.5% Wallis and Futuna 29 18 1 62.1% 3.5% Western Sahara 142 35 11 24.7% 7.8% Yemen 140 76 13 54.3% 9.3% Zambia 138 130 35 94.2% 25.4% Zimbabwe 130 125 32 96.2% 24.6%

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8.2 Appendix 2: Species movement classifications, modelled range size metrics and model validation statistics for 74 Australian inland birds.

Stubble Quail Coturnix pectoralis Nomadic 1,819,376 169,017 435,663 1,199,518 171,941 0.395 7 0.140 472 Black-shouldered Kite Elanus axillaris Nomadic 2,645,411 113,305 462,933 1,650,596 360,543 0.779 15 0.181 2845 Letter-winged Kite Elanus scriptus Nomadic 719,691 60,454 202,000 618,280 116,531 0.577 10 0.110 29 Spotted Harrier Circus assimilis Nomadic 3,559,606 583,026 1,316,650 2,554,575 391,594 0.297 4 0.182 1255 Australian Bustard Ardeotis australis Nomadic 3,135,949 1,123,919 1,626,294 2,349,761 293,412 0.180 2 0.214 1311 Common Bronzewing Phaps chalcoptera Possibly nomadic 1,097,672 86,879 310,034 531,379 108,800 0.351 6 0.136 5347 Flock Bronzewing Phaps histrionica Nomadic 916,107 84,554 302,802 689,112 134,677 0.445 8 0.194 165 Diamond Dove Geopelia cuneata Nomadic 2,731,995 220,878 1,134,687 2,028,898 416,395 0.367 9 0.236 4380 Grey Falcon Falco hypoleucos Nomadic 2,572,585 882,558 1,229,142 1,515,573 144,041 0.117 2 0.109 138 Black Falcon Falco subniger Nomadic 2,675,534 537,230 1,011,626 1,698,811 234,623 0.232 3 0.093 472 Major Mitchell's Cockatoo Lophochroa leadbeateri Possibly nomadic 2,404,222 560,730 942,133 1,519,569 189,782 0.201 3 0.244 1137 Cockatiel Nymphicus hollandicus Nomadic 3,270,352 106,111 987,539 1,858,621 406,414 0.412 18 0.164 4597 Bourke's Parrot Neopsephotus bourkii Nomadic 1,657,523 746,496 975,446 1,233,106 103,664 0.106 2 0.214 308 Scarlet-chested Parrot Neophema splendida Nomadic 496,793 776 128,339 389,329 100,571 0.784 502 0.195 69 Budgerigar Melopsittacus undulatus Nomadic 2,789,945 186,998 1,133,398 2,083,904 388,315 0.343 11 0.228 5492 Princess Parrot Polytelis alexandrae Nomadic 662,832 620 208,340 601,321 206,619 0.991 970 0.191 10a Black Honeyeater Sugomel nigrum Nomadic 2,206,769 237,940 890,360 1,628,696 263,342 0.296 7 0.264 917 Pied Honeyeater Certhionyx variegatus Nomadic 2,538,637 630,913 1,140,775 1,918,270 365,582 0.320 3 0.272 1202 Brown Honeyeater Lichmera indistincta Nomadic 2,571,125 138,958 871,046 1,602,178 385,477 0.443 12 0.189 8553

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Standard Magnitude deviation of Difference Number of Overall Minimum Mean Maximum of the Coefficient fluctuation between records range size range size range size range size mean of in range model and used in Species Movement status (km2) (km2) (km2) (km2) range size variation size null AUC models Painted Honeyeater Grantiella picta Nomadic 780,039 92,922 234,556 399,436 89,267 0.381 4 0.152 190 Striped Honeyeater Plectorhyncha lanceolata Possibly nomadic 659,307 82,817 252,542 414,736 81,228 0.322 5 0.239 3092 Gibberbird Ashbyia lovensis Nomadic 327,149 151,157 189,748 223,760 14,209 0.075 1 0.181 138 Crimson Chat Epthianura tricolor Nomadic 2,611,986 157,107 1,151,709 2,039,803 468,049 0.406 13 0.260 2517 Orange Chat Epthianura aurifrons Nomadic 2,138,565 493,032 1,059,164 1,546,689 225,849 0.213 3 0.264 873 Yellow Chat Epthianura crocea Nomadic 257,089 26,570 72,656 143,560 23,518 0.324 5 0.188 142 White-fronted Chat Epthianura albifrons Nomadic 625,249 64,954 207,426 383,826 63,656 0.307 6 0.255 1408 Grey Honeyeater Conopophila whitei Nomadic 1,297,181 108,314 573,783 1,076,109 190,734 0.332 10 0.160 47 Spiny-cheeked Honeyeater Acanthagenys rufogularis Possibly nomadic 2,063,826 448,022 932,766 1,573,907 224,940 0.241 4 0.120 14872 White-fronted Honeyeater Purnella albifrons Nomadic 1,669,300 103,538 375,174 1,087,753 235,236 0.627 11 0.278 4090 Grey-headed Honeyeater Ptilotula keartlandi Possibly nomadic 1,814,667 185,357 632,193 1,153,436 186,643 0.295 6 0.325 1960 Grey-fronted Honeyeater Ptilotula plumula Nomadic 2,210,412 255,598 708,074 1,192,172 250,579 0.354 5 0.287 1617 Striated Pardalote Pardalotus striatus Possibly nomadic 1,161,005 219,578 408,166 587,510 71,184 0.174 3 0.075 20315 Western Gerygone Gerygone fusca Nomadic 2,271,607 384,749 754,571 1,514,307 220,732 0.293 4 0.201 1747 Chestnut-breasted Whiteface Aphelocephala pectoralis Possibly nomadic 71,193 37 33,352 63,160 15,247 0.457 1720 0.227 68 Banded Whiteface Aphelocephala nigricincta Possibly nomadic 1,446,464 336,688 644,626 883,714 117,342 0.182 3 0.249 330 Ground Cuckooshrike Coracina maxima Nomadic 3,155,208 448,945 1,379,621 2,109,783 375,612 0.272 5 0.163 572 Grey Fantail Rhipidura albiscapa Nomadic 436,107 86,055 166,907 299,711 42,732 0.256 3 0.164 8239 Little Crow Corvus bennetti Nomadic 2,508,774 1,111,711 1,636,675 2,047,464 195,665 0.120 2 0.225 3395 Jacky Winter Microeca fascinans Possibly nomadic 1,564,910 326,901 580,638 933,210 127,589 0.220 3 0.167 6276 Red-capped Robin Petroica goodenovii Possibly nomadic 2,829,147 562,818 1,288,807 2,198,158 334,505 0.260 4 0.135 7089 Mistletoebird Dicaeum hirundinaceum Possibly nomadic 2,873,533 336,324 922,454 1,824,905 339,363 0.368 5 0.101 9021 Painted Finch Emblema pictum Possibly nomadic 1,494,337 350,768 686,168 1,144,220 174,715 0.255 3 0.265 521 Plum-headed Finch Neochmia modesta Nomadic 955,399 89,282 328,518 621,434 97,423 0.297 7 0.184 257

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Standard Magnitude deviation of Difference Number of Overall Minimum Mean Maximum of the Coefficient fluctuation between records range size range size range size range size mean of in range model and used in Species Movement status (km2) (km2) (km2) (km2) range size variation size null AUC models Pictorella Mannikin Heteromunia pectoralis Nomadic 1,284,739 33,227 302,550 870,705 205,929 0.681 26 0.235 249 Chestnut-backed Quail-thrush Cinclosoma castonatum Nomadic 128,186 18108 68415 90805 16,528 0.242 5 0.330 1166 White-breasted Woodswallow Artamus leucorynchus Migratory 2,131,113 292,433 946,369 1,693,001 369,328 0.390 6 0.192 2969 Masked Woodswallow Artamus personatus Migratory 3,626,301 282,829 1,300,215 2,238,026 432,318 0.332 8 0.206 2696 White-browed Woodswallow Artamus superciliosus Migratory 2,106,824 47,820 471,943 1,099,462 274,409 0.581 23 0.189 2364 Pallid cuckoo Cacomantis pallidus Migratory 3,666,892 146,535 1,317,160 2,750,745 534,228 0.406 19 0.169 2656 Black-eared Cuckoo Chrysococcyx osculans Migratory 3,299,070 342,410 1,074,182 2,064,798 347,870 0.324 6 0.175 621 Swamp Harrier Circus approximans Migratory 1,178,472 58,220 212,638 646,422 459,491 0.519 11 0.253 1312 Spotted Nightjar Eurostopodus argus Migratory 3,571,814 826,978 1,620,623 2,733,834 110,329 0.230 3 0.116 327 Nankeen Kestrel Falco cenchroides Migratory 2,869,973 938,171 1,718,588 2,471,800 373,199 0.196 3 0.087 8537 Brolga Grus rubicunda Migratory 1,591,539 258,036 731,825 1,263,151 336,017 0.323 5 0.252 2258 White-winged Triller Lalage tricolor Migratory 3,894,308 185,879 1,415,757 2,470,416 236,470 0.431 13 0.146 5181 Rufous Songlark Megalurus mathewsi Migratory 3,203,186 11,647 708,692 2,355,762 610,430 0.648 202 0.193 4566 Blue-winged Parrot Neophema chrysostoma Migratory 1,057,643 155,491 387,933 610,219 117,207 0.302 4 0.102 106 Inland Thornbill Acanthiza apicalis Sedentary 1,932,846 441,711 869,720 1,274,301 165,919 0.191 3 0.215 3321 Striated Grasswren Amytornis striatus Sedentary 284,974 16,573 57,050 128,734 26,173 0.459 8 0.302 547 Southern Whiteface Aphelocephala leucopsis Sedentary 1,865,509 387,710 983,116 1,540,833 220,951 0.225 4 0.205 4291 Singing Honeyeater Gavicalis virescens Sedentary 2,225,419 1,445,501 1,650,979 1,901,593 189,467 0.049 1 0.104 16386 Spinifex Pigeon Geophaps plumifera Sedentary 1,521,194 196,729 649,662 1,046,956 163,840 0.292 5 0.280 864 White-winged Fairywren Malurus leucopterus Sedentary 2,272,004 1,154,308 1,486,374 1,789,098 81,491 0.099 2 0.229 4694 Hooded Robin Melanodryas cucullata Sedentary 2,578,781 573,016 1,263,703 2,184,812 146,463 0.286 4 0.160 3563 Black-chinned Honeyeater Melithreptus gularis Sedentary 1,816,007 329,395 712,649 1,209,501 361,011 0.280 4 0.253 1359 Crested Pigeon Ocyphaps lophotes Sedentary 1,601,069 327,071 645,978 1,033,581 138,237 0.214 3 NAb 21634 Rufous Whistler Pachycephala rufiventris Sedentary 2,726,560 556,372 1,132,582 1,962,385 199,271 0.268 4 0.058 14186

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aThe predictions of this model should be regarded with caution due to the limited number of records available for this species. bNull models were not run for Ocyphaps lophotes as the size of the input files due to the large number of records in this species made the problem computationally intractable.

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8.3 Appendix 3: Range dynamics across time for 74 nomadic arid-zone birds

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8.4 Appendix 4: Mathematical formulation of Marxan with temporal dynamics

Marxan finds solutions to conservation problems using a simulated annealing algorithm. For this study, the traditional Marxan problem, ignoring connectivity, is to minimise the cost of taking a suite of spatially explicit conservation actions while ensuring all conservation features meet their targets (Ball et al. 2009). This can be expressed mathematically as:

Minimise 푁푖 ∑푖=1 푐푖푥푖 subject to f 푁푖 ∑푖=1 푎푖푖푥푖 ≥ 푇� ∀ and {0,1} i

푥푖 ∈ ∀ Where ci is the cost of planning unit i, is the number of planning units, is the number of features or species, and is a control variable푁푖 with value 0 for planning units푁 �not selected in the solution and value 1 for planning푥푖 units selected. Whilst boundary length modifiers were not used in this study, for problems aiming to design compact reserves the equation should be adjusted accordingly.

The objective function is subject to the constraint that all features ( ) meet areal representation targets ( ) for feature f in the reserve network. ∀ 푓 푇� The target was set to 30% of conservation features for each scenario, with representing the amount of conservation feature in planning unit . These input parameters vary푎푖푖 for each scenario and are described in more detail 푓below. 푖

1. Static scenario: aims to target 30% representative coverage of the overall range of all target species. To create conservation features, species distributions were averaged across all 130 time-slices yielding one conservation feature per species, as follows:

( ) = 퐷퐷퐷 1302011 ∑�=퐽퐽퐽 ∑�=2001 푝푖푖푖푖 푎푖푖푖

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where pijmy is the conservation value (habitat suitability multiplied by planning unit area) for species j ( 1, … , in planning unit i

( {1, … 푗 ∈ � 푛푛푛푛푛푛 표 � 푠}𝑠𝑠�), month푠 푁� �m and year y. The timestep t is not explicitly푖 ∈ 푛푛푛푛푛푛accounted표 �for푝푝 𝑝𝑝�in this푝 𝑢�scenario.푢� 푁푖 For the 42 species in this study, this equation produced a total of 42 conservation features (one per species representing average species distribution across time).

2. Time-sliced scenario: Targets 30% of seasonal habitat use. Species estimated distributions for 4 seasonal time periods (January, April, July and October) for each year in the study period were used as separate conservation features, as follows:

푎푖푖푖 푝푖푖푖푖 where t here is { , , , }, and {2000, … 2011}. This resulted in 43

conservation features� ∈ per퐽𝐽 species퐴𝐴 퐽, 𝐽with푂𝑂 1806 conserva푦 ∈ tion features in total. The modified Marxan problem now has features indexed by species and a time (year and month).

Minimise 푁푖 ∑푖=1 푐푖푥푖 subject to f, t 푁푖 ∑푖=1 푎푖푖푖푥푖 ≥ 푇𝑓 ∀ and {0,1} i

푥푖 ∈ ∀ where there is now a target for every species and time slice.

3. Annual scenario: Find the minimum set of reserves that accounts for inter-annual variability in target species distributions. This was based on the estimated species distributions which had been averaged across each of the years in the study period:

( ) = 퐷퐷퐷 12 ∑�=퐽퐽퐽 푝푖푖푖푖 푎푖푖푖 Where t is the year {2000, … 2011}, pijmy is the conservation value (habitat suitability

multiplied by planning푦 ∈ unit area) for species j in planning unit i, month m { , … }, and year y. This resulted in 12 conservation features per species (representing∈ 퐽𝐽 average퐷𝐷

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species distribution for each year, summed and averaged across months), 504 conservation features in total. The problem is the same as the time-sliced problem (scenario 2).

= 201112 ∑�=2000 푝푖푖푖푖 푎푖푖푖 where t is each month m { , … }. This resulted in 12 conservation features per

species (representing average∈ 퐽species𝐽 퐷 distribution𝐷 for each month, summed and averaged across the years), with 504 conservation features in total. The problem is the same as the time-sliced problem (scenario 2).

5. Bottleneck scenario: Find the minimum set of reserves that maintains bottleneck refugia (minimum range requirements). This was based on estimated species distributions at the time of minimum geographic range extent for each species with one conservation feature per species, ignoring their distributions at other times.

= ′ ′ 푎푖푖푖 푝푖푖� � where the month and year is chosen such that the total amount of the feature, summed across all planning units, is smallest for that species. This resulted in one conservation feature per species, 42 conservation features in total and we use the standard Marxan problem described in scenario 1.

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