domain: shs.eco sdv sde

text:
Sophie Panel
email: sophie.panel@iepg.fr

Antoine Pietri
email: antoine.pietri@umontpellier.fr

Tilman Brück

Quentin David

Cécile Fauconnet

Caroline Le Pennec-Caldichoury

Gwenolé Le Velly

Marion Mercier

Luigi Moretti

Angélique Palle

Alexandre Sauquet

God did not save the kings: Environmental consequences of the 1982 Falklands War *

Keywords: C22, F51, Q57 ecological warfare, Falklands War, king penguins, seabirds

Warfare has been found to have detrimental impacts on biodiversity due to its long-lasting economic and social consequences. Yet, much less is known about the amount of biodiversity loss directly resulting from the use of military technology. This paper analyzes the environmental consequences of one of the largest aerial and naval conflicts of the late 20st century, namely the 1982 Falklands War. As an indicator of the marine ecosystem status, we analyze population trends of king penguins breeding on the Falkland Islands over the period 1963-1997. Using interrupted time series analysis, we find that the war significantly slowed the growth rate of king penguins' population. To take better account of time-varying confounders, we complement this analysis using a synthetic control group based on data from other Sub-Antarctic colonies and find similar results.

Introduction

There is still considerable uncertainty about the effects of warfare on biodiversity. Current scholarship has come to the conclusion that most war-induced damages to biodiversity are indirect effects (such as poaching or deforestation) resulting from war-related economic deprivations [START_REF] Dudley | Effects of war and civil strife on wildlife and wildlife habitats[END_REF][START_REF] Gaynor | War and wildlife: linking armed conflict to conservation[END_REF]: to date, there is scant evidence that biodiversity loss can occur as a direct consequence of the use of explosives or chemicals. Yet, this conclusion might be premature, as much of the evidence comes either from protracted, low-intensity conflicts such as the FARC insurgency in Colombia, or from conflicts that resulted in profound institutional and economic disruptions, such as the DRC's civil war.

This paper focuses on the direct effects of warfare on the environment. In order to isolate the effects of war technology, we focus on the 1982 Falklands War between Argentina and the United Kingdom. Contrary to most contemporary conflicts, the Falklands War did not result in profound institutional changes; neither did it directly cause economic collapse [START_REF] Royle | Changes in the Falkland Islands since the conflict of 1982[END_REF] or large-scale labor redeployment -two consequences of wars that can indirectly affect wildlife [START_REF] Dudley | Effects of war and civil strife on wildlife and wildlife habitats[END_REF][START_REF] Gaynor | War and wildlife: linking armed conflict to conservation[END_REF]. Likewise, we found no report of deliberate tactics aiming at causing environmental damages, which are another oft-mentioned mechanism linking warfare and biodiversity loss [START_REF] Gurses | Environmental consequences of civil war: evidence from the Kurdish conflict in Turkey[END_REF]. Environmental consequences of the Falklands War can thus be directly traced back to the technology of the conflict.

The Falklands War has other interesting properties. One difficulty in analyzing the environmental consequences of warfare is that conflict is endogenous to environmental degradation. For example, there is mounting evidence that global warming increases the risk of civil war [START_REF] Hsiang | Civil conflicts are associated with the global climate[END_REF][START_REF] Hsiang | Quantifying the influence of climate on human conflict[END_REF][START_REF] Burke | Climate and conflict[END_REF]. The impact of biodiversity on warfare has not been systematically studied, but some anecdotal evidence suggests that wildlife resources (e.g. ivory, fur or rhino horns) may lead to similar security issues as high-value natural resources such as diamonds, while states' attempts to deter illegal wildlife trade have turned increasingly violent [START_REF] Douglas | High-value natural resources: Linking wildlife conservation to international conflict, insecurity, and development concerns[END_REF][START_REF] Barron | How the illegal wildlife trade is fueling armed conflict[END_REF]. At least one quantitative analysis finds that protected areas are used as shelters by insurgents and are associated with an increase in the intensity of violence in their vicinity [START_REF] Canavire-Bacarreza | Unintended consequences of conservation: Estimating the impact of protected areas on violence in Colombia[END_REF]. By contrast, the Falklands War broke out for reasons that were completely unrelated to environmental issues. Indeed, the conflict is often cited as the textbook example of a "diversionary" war [START_REF] Levy | Diversionary action by authoritarian regimes: Argentina in the Malvinas/Falklands case[END_REF][START_REF] Dassel | Civilians, soldiers, and strife: Domestic sources of international aggression[END_REF][START_REF] Oakes | Diversionary war and Argentina's invasion of the Falkland Islands[END_REF] and it is widely agreed that the intensity of the war was disproportionate to the economic or strategic value of the remote, sparsely populated Falkland Islands [START_REF] Freedman | The war of the Falkland Islands, 1982[END_REF][START_REF] Mueller | War has almost ceased to exist: An assessment[END_REF]: the conflict was famously described by Argentinian writer Jorge Luis Borges as "a fight between two bald men over a comb."

A second common identification issue lies in the fact that contemporary conflicts tend to cluster in space [START_REF] Ward | Location, location, location: An MCMC approach to modeling the spatial context of war and peace[END_REF][START_REF] Gleditsch | Transnational dimensions of civil war[END_REF][START_REF] Buhaug | Contagion or confusion? why conflicts cluster in space[END_REF] and in time [START_REF] Walter | Why bad governance leads to repeat civil war[END_REF]. This complicates longitudinal analyses-because studies analyzing the effects of recent outbreaks then run the risk of severely underestimating the real effect size-but also cross-country comparisons: indeed, many conflictaffected countries lack adequate control cases, that is, similar (often neighboring) countries that are also home to the species of interest but did not experience war during the investigation period. In contrast to most contemporary conflicts, the Falklands War is a unique event: the Falkland Islands did not experience large-scale military conflict prior to the 1982 war,1 and the conflict did not relapse afterwards. To summarize, the Falklands War can be considered representative of a broader class of events-namely conventional warfare without deliberate tactic to destroy the environment-but it presents unique advantages in terms of causal identification.

With regard to the outcome, we analyze population dynamics of king penguins (Aptenodytes Patagonicus) in the Falkland Islands before and after the war. King penguins possess many of the desirable properties of an indicator species-that is, a species used to monitor ecosystem changes that are unobservable or too costly to measure directly [START_REF] Hilty | Faunal indicator taxa selection for monitoring ecosystem health[END_REF][START_REF] Hazen | Marine top predators as climate and ecosystem sentinels[END_REF]. First, king penguins breed on land and are therefore conspicuous, contrary to most marine organisms that are found only underwater; furthermore, since they are colonial and breed at predictable times, their population size can be counted annually (contrast, for example, fish populations). Second, they occupy high trophic levels, and are therefore responsive to lower-level processes-such as ocean productivity or pollution-that occur throughout the entire food web [START_REF] Burger | Marine birds as sentinels of environmental pollution[END_REF][START_REF] Durant | Pros and cons of using seabirds as ecological indicators[END_REF][START_REF] Bohec | Polar monitoring: seabirds as sentinels of marine ecosystems[END_REF][START_REF] Hazen | Marine top predators as climate and ecosystem sentinels[END_REF]. Third, three characteristics make them particularly sensitive to ecosystem changes: (a) they are specialist feeders, and thus cannot switch diet in order to cope with lower prey availability [START_REF] Hilty | Faunal indicator taxa selection for monitoring ecosystem health[END_REF] Le [START_REF] Bohec | Polar monitoring: seabirds as sentinels of marine ecosystems[END_REF]; (b) contrary to many marine top predators, they are philopatric, and therefore unable to relocate to avoid disturbances [START_REF] Hilty | Faunal indicator taxa selection for monitoring ecosystem health[END_REF][START_REF] Burger | Marine birds as sentinels of environmental pollution[END_REF]; (c) they are slower than flying seabirds, which means that their foraging range is relatively restricted. Note also that because of their unusually long breeding cycle (the longest among all penguin species), they are bound to their breeding ground for long periods and thus especially dependent on the quality of their local environment.

Finally, in spite of their sensitivity to ecosystem changes, king penguins are a long-lived species with a low reproductive output and thus a relatively stable demography (contrast, again, fish populations): in other words, their response to environmental changes is likely to display a high signal-to-noise ratio [START_REF] Furness | Seabirds as monitors of the marine environment[END_REF].

Our method consists in two steps. First, we use a longitudinal dataset containing yearly estimates of king penguins' population size over the period . Using interrupted time series (ITS) analysis, we find that the 1982 Falklands War led to a significant trend break and slowed the post-treatment growth of the population, even when controlling for the effects of climate change: according to a conservative estimate (based on the lower limit of the 95% CI in our main estimate), the population in 1997 would have been about 1.5 as high in the absence of the war. Second, in order to alleviate potential biases due to unobserved confounders, we build a control unit based on a weighted combination of several other king penguins colonies unexposed to the war. This control unit is obtained using the Synthetic Control method [START_REF] Abadie | The economic costs of conflict: A case study of the Basque country[END_REF][START_REF] Abadie | Synthetic control methods for comparative case studies: Estimating the effect of California's tobacco control program[END_REF]. Following [START_REF] Linden | Combining synthetic controls and interrupted time series analysis to improve causal inference in program evaluation[END_REF], we use this synthetic unit in our regression model in order to evaluate the difference in the post-1982 trends between the king penguins' colony at the Falkland Islands and its control unit. We find that, while the two trends are not significantly different before the war, they significantly diverge after the war. The magnitude of the effect obtained is roughly similar with and without the control unit. In sum, we find robust evidence that the war led to a long-term slowing of the population growth rate.

Past research on warfare and biodiversity

There are very few comparative studies on the effect of warfare on biodiversity: most works focus on a single country, region or conflict episode. To the best of our knowledge, the single analysis using a large sample of countries is a very recent study by [START_REF] Daskin | Warfare and wildlife declines in Africa's protected areas[END_REF] which estimates the impact of armed conflict on large herbivore populations in 126 protected areas spread over 19 sub-Saharan African countries between 1946 and 2010. In their analysis, war-affected zones saw a systematic decrease of most populations (with an effect stronger than any other indicator of human activity), although complete population collapse was rare.

The impact of wars on biodiversity is not necessarily negative: armed conflict often encourages rural exodus [START_REF] Pech | The impact of armed conflict and forced migration on urban expansion in Goma: Introduction to a simple method of satellite-imagery analysis as a complement to field research[END_REF], farmland abandonment [START_REF] Witmer | Satellite data methods and application in the evaluation of war outcomes: abandoned agricultural land in Bosnia-Herzegovina after the 1992-1995 conflict[END_REF] or the decline of extractive industries through labor redeployment and the withdrawal of timber or mining companies from war zones [START_REF] Draulans | The impact of war on forest areas in the Democratic Republic of Congo[END_REF][START_REF] Mitchell | Resource curse in reverse: How civil wars influence natural resource production[END_REF][START_REF] Burgess | War and deforestation in Sierra Leone[END_REF][START_REF] Butsic | Conservation and conflict in the Democratic Republic of Congo: The impacts of warfare, mining, and protected areas on deforestation[END_REF]. Relatedly, conflicts sometimes create "no man's lands" that protect wildlife from human activities, like the oft-cited case of the buffer zone between North Korea and South Korea which has become a biodiversity hotspot [START_REF] Dudley | Effects of war and civil strife on wildlife and wildlife habitats[END_REF][START_REF] Gaynor | War and wildlife: linking armed conflict to conservation[END_REF]. Yet, on the whole, it does not seem that these positive externalities are sufficient to compensate the adverse impact of warfare. [START_REF] Hendrix | Civil conflict and world fisheries, 1952-2004[END_REF] indeed find that exposure to civil war decreases fish catches, yet, [START_REF] Mitchell | Resource curse in reverse: How civil wars influence natural resource production[END_REF] find the opposite effect.

With regard to the refuge-effect, the evidence remains largely anecdotal. Finally, farmland abandonment is only a local and short-term phenomenon, as refugees eventually resettle in other areas [START_REF] Baumann | Land-use change in the Caucasus during and after the Nagorno-Karabakh conflict[END_REF][START_REF] Eklund | How conflict affects land use: agricultural activity in areas seized by the Islamic state[END_REF]. An assessment of the early literature [START_REF] Dudley | Effects of war and civil strife on wildlife and wildlife habitats[END_REF] as well as two relatively recent reviews on wars and biodiversity [START_REF] Lawrence | The effects of modern war and military activities on biodiversity and the environment[END_REF][START_REF] Gaynor | War and wildlife: linking armed conflict to conservation[END_REF] conclude that deleterious effects are observed for all types of warfare (naval, aerial, terrestrial and nuclear), for all phases of the conflict (including production, training and war preparation), and across a wide range of locations and species.

The most obvious negative effect of warfare on wildlife populations is inadvertent killing though the use of explosives or chemicals [START_REF] Gaynor | War and wildlife: linking armed conflict to conservation[END_REF]). Landmines, for example, do not discriminate between humans and animals, and especially affect large mammals [START_REF] Berhe | The contribution of landmines to land degradation[END_REF]. However, the two main drivers of wildlife populations decline in wartime appear to be overexploitation due to the economic consequences of the war on the one hand, and habitat loss on the other hand. Overexploitation (e.g., in the form of hunting and wildlife trafficking) results from two interrelated factors. First, institutional collapse during large-scale civil wars destabilizes the conservation sector and greatly reduces the effectiveness of legal enforcement of environmental protection [START_REF] Baral | The Maoist people's war and conservation in Nepal[END_REF][START_REF] Glew | Gorillas in the midst: the impact of armed conflict on the conservation of protected areas in sub-Saharan Africa[END_REF]. Second, disruption of the economy in war-affected zones may force local populations or refugees to resort to poaching for subsistence and/or trafficking [START_REF] Draulans | The impact of war on forest areas in the Democratic Republic of Congo[END_REF][START_REF] De Merode | Species protection, the changing informal economy, and the politics of access to the bushmeat trade in the Democratic Republic of Congo[END_REF][START_REF] Beyers | Resource wars and conflict ivory: the impact of civil conflict on elephants in the Democratic Republic of Congo-the case of the Okapi Reserve[END_REF][START_REF] Lindsell | The impact of civil war on forest wildlife in West Africa: mammals in Gola Forest, Sierra Leone[END_REF][START_REF] Nackoney | Impacts of civil conflict on primary forest habitat in northern Democratic Republic of the Congo, 1990-2010[END_REF][START_REF] Waller | The effects of war on bonobos and other nonhuman primates in the Democratic Republic of the Congo[END_REF]; rebels and members of regular military forces also often engage in poaching to fund the insurgency or for personal gain [START_REF] Draulans | The impact of war on forest areas in the Democratic Republic of Congo[END_REF][START_REF] De Merode | Species protection, the changing informal economy, and the politics of access to the bushmeat trade in the Democratic Republic of Congo[END_REF][START_REF] Loucks | Wildlife decline in Cambodia, 1953-2005: exploring the legacy of armed conflict[END_REF][START_REF] Benz | Great apes and new wars[END_REF][START_REF] Lindsell | The impact of civil war on forest wildlife in West Africa: mammals in Gola Forest, Sierra Leone[END_REF][START_REF] Velho | Collateral damage: impacts of ethno-civil strife on biodiversity and natural resource use near Indian nature reserves[END_REF]. Poaching often continues in peacetime, as it is often facilitated by the proliferation of small arms in the aftermath of the conflict [START_REF] Loucks | Wildlife decline in Cambodia, 1953-2005: exploring the legacy of armed conflict[END_REF][START_REF] Beyers | Resource wars and conflict ivory: the impact of civil conflict on elephants in the Democratic Republic of Congo-the case of the Okapi Reserve[END_REF][START_REF] Nackoney | Impacts of civil conflict on primary forest habitat in northern Democratic Republic of the Congo, 1990-2010[END_REF].

Habitat loss can result from guerrilla or counter-insurgency tactics during asymmetric wars. For example, forests are often used as shelters by combatants: as a consequence, deforestation can result either from rebels' direct use of forest resources [START_REF] Fjeldså | Illicit crops and armed conflict as constraints on biodiversity conservation in the Andes region[END_REF] or from governments' attempt to cut off insurgents from their staging ground [START_REF] Van Etten | Environmental destruction as a counterinsurgency strategy in the Kurdistan region of Turkey[END_REF][START_REF] Gurses | Environmental consequences of civil war: evidence from the Kurdish conflict in Turkey[END_REF] or to destroy their sources of funding, e.g. illicit crop or timber trafficking [START_REF] Alvarez | Forests in the time of violence: conservation implications of the Colombian war[END_REF][START_REF] Fjeldså | Illicit crops and armed conflict as constraints on biodiversity conservation in the Andes region[END_REF][START_REF] Reuveny | The effect of warfare on the environment[END_REF]. Scorched earth tactics may also be employed to deter local populations from providing shelter, intelligence or monetary resources to combatants, or to deprive rebels' supporters from their livelihood [START_REF] Van Etten | Environmental destruction as a counterinsurgency strategy in the Kurdistan region of Turkey[END_REF][START_REF] Gurses | Environmental consequences of civil war: evidence from the Kurdish conflict in Turkey[END_REF]. The best-known example is the Vietnam War, during which the US military practiced systematic "carpet-bombing" and sprayed herbicides and napalm on Vietnamese forest, with far-ranging consequences for the ecosystem [START_REF] Hupy | The environmental footprint of war[END_REF][START_REF] Lacombe | Conflict, migration and land-cover changes in Indochina: a hydrological assessment[END_REF]. Habitat loss may also result from the political and economic impact of the conflict (e.g., post-conflict economic recovery or the resettlement of internally displaced persons), which sometimes last for decades [START_REF] Ordway | Political shifts and changing forests: effects of armed conflict on forest conservation in Rwanda[END_REF][START_REF] Nita | Widespread forest cutting in the aftermath of World War II captured by broad-scale historical Corona spy satellite photography[END_REF].

Generally, protracted civil wars in sub-Saharan Africa, Asia and Latin America are overrepresented in these studies [START_REF] Gaynor | War and wildlife: linking armed conflict to conservation[END_REF]. This imbalance with regard to location and conflict type somewhat limits the generality of the findings, and probably explains why most of these studies identify the human, economic and political consequences of the war as the main mechanisms driving biodiversity loss. As a consequence, little is known about the direct impact of the hostilities on biodiversity. Relatedly, few studies have investigated the effects of interstate wars, or, more generally, shorter conflicts that involve tactics more destructive to the environment. Some rare studies have analyzed the consequences of the 1991 Gulf War and its massive oil spills2 on seabird populations [START_REF] Evans | Short-term damage to coastal bird populations in Saudi Arabia and Kuwait following the 1991 Gulf War marine pollution[END_REF][START_REF] Price | Impact of the 1991 Gulf War on the coastal environment and ecosystems: current status and future prospects[END_REF]: they found heavy mortality rates among some species (>50 percent) but also evidence of post-conflict recovery.

Case description

The Falklands War

The Falklands War was a ten-week war between Argentina and the United Kingdom, taking place from April to June 1982. It found its roots in Argentina's sovereignty claim over the islands, which lie some 600 km east of the Argentine coast (and about 13,000 km from the UK). Numerous disputes occurred between the two countries since the UK established control over the islands in the early 19th century: the Argentine claimed that they were the legitimate owner of the territory and characterized British presence as forceful occupation, while the United Kingdom argued that Falkland Islanders-who were mostly descendants of British settlers-should have the right to self-determination [START_REF] Weisiger | Logics of war: Explanations for limited and unlimited conflicts[END_REF]). Argentina's sudden decision to seize the Falklands by force is often attributed to domestic factors: in 1982, the military junta led by Leopoldo Galtieri faced intense domestic turmoil and hoped (correctly) that the invasion of the islands would rally the public behind the regime [START_REF] Levy | Diversionary action by authoritarian regimes: Argentina in the Malvinas/Falklands case[END_REF][START_REF] Dassel | Civilians, soldiers, and strife: Domestic sources of international aggression[END_REF][START_REF] Oakes | Diversionary war and Argentina's invasion of the Falkland Islands[END_REF]. Another common explanation is that the outbreak of the conflict resulted from a series of escalatory steps and miscalculations from both governments following a diplomatic incident in the dependency of South Georgia3 [START_REF] Freedman | The war of the Falkland Islands, 1982[END_REF]. The Argentine military did not expect the UK to be willing and able to retake the islands by force, and believed the international community would condone the invasion since it occurred without British casualties [START_REF] Weisiger | Logics of war: Explanations for limited and unlimited conflicts[END_REF].

The war started on April 2 with the invasion of the Falkland Islands by the Argentine navy. There was no real deterrent force on the islands, so the UK had to rapidly dispatch a naval task force, which needed several weeks to reach the South Atlantic. Meanwhile, all diplomatic resolution attempts failed. The first effective encounters occurred by late April/early May, with a first British airstrike on the airfield near the capital Stanley (operation Black Buck 1, April 30th), and the sinking of the Argentine light cruiser ARA General Belgrano (2 May) which resulted in more than 300 casualties.

During the first weeks of the war, most of the combats took place at sea between the British navy and the Argentine air force. British troops landed in the area of San Carlos Water (northwestern coast of East Falkland) on May 21, and in Bluff Cove (eastern coast of East Falkland) in early June, after which they marched toward Stanley (the capital). After several weeks of land battles, the Argentine garrison in Stanley surrendered, which put an end to the hostilities. The junta briefly considered the option of carrying on attacks on the British task force from the air, hoping to make the costs of retaining the islands unbearable for the UK on the long run [START_REF] Weisiger | Logics of war: Explanations for limited and unlimited conflicts[END_REF]. However, at this point, the junta had lost the support of both the public and the armed forces. Within days, Galtieri was forced to step down, and the military defeat ultimately precipitated the breakdown of the military junta and Argentina's transition to democracy in 1983.

In spite of its shortness, the Falklands War is responsible for killing some 1,000 military personnel, and for the destruction of dozens of military warships (among others: the 12,000 tons cruiser ARA General Belgrano on the Argentine side; and the 5,500 tons landing ship RFA Sir Galahad as well as the 4,000 tons destroyers HMS Coventry and HMS Sheffield on the British side). The Argentine air forces alone lost more than 100 military aircraft [START_REF] Smith | Battle atlas of the Falklands War 1982[END_REF].

King penguins

There are five penguin species breeding on the Falkland Islands. We focus on king penguins because this is the only species for which we could gather sufficient data before and after 1982. King penguins progressively recolonized the islands after possible extirpation at the end of the 19th century, and the first documented successful breeding occurred in 1965 at Volunteer Point, at the northeast of East Falkland [START_REF] Pistorius | Population development and historical occurrence of king penguins at the Falkland Islands[END_REF].

King penguins have an unusually long breeding cycle, which lasts 14 to 15 months from moulting and courtship to chick fledging: this means that colonies are occupied continuously by chicks, and intermittently by adults [START_REF] Stonehouse | The king penguin of south georgia[END_REF][START_REF] Bost | King penguin[END_REF]. The typical breeding cycle starts with molt by the end of the austral winter. Egg-laying occurs usually in November or December, and hatching in January/February.

King penguins can lay one single egg per season, which they incubate on their feet without nest. During incubation and the early stages of the chick's growth (until mid-to late April), the male and the female exchange duties several times, one bird parenting while the other is foraging at sea. After this period in which they are fed regularly, chicks are left at the colony and fed only intermittently (or not at all) during the winter (May-August), while decreasing food availability forces adults to undertake long foraging trips that often extend over hundreds of kilometers. Parents then resume feeding in spring until fledging.

Because the breeding period last for more than one year, king penguins' breeding cycle is asynchronous: successful breeders from the previous year settle later in the colony and start molting and courting with up to two months delay. These breeding attempts often fail because the chick did not store enough food to survive the long winter fast [START_REF] Stonehouse | The king penguin of south georgia[END_REF][START_REF] Weimerskirch | The breeding biology and population dynamics of king penguins Aptenodytes patagonica on the Crozet Islands[END_REF]. The premature death of their offspring allows failed breeders to settle earlier in the colony in the subsequent year: in other words, successful breeders from year 0 become "late breeders" at year 1, and again "early breeders" at year 2 if their previous attempt failed, which is very likely (Le [START_REF] Maho | Undisturbed breeding penguins as indicators of changes in marine resources. Marine ecology progress series[END_REF][START_REF] Dobson | Comparison of color and body condition between early and late breeding king penguins[END_REF]. 4 In spite of the low chances of success of late breeders, the majority of the birds (over 80 percent) attempt to breed annually and only a small minority take a "sabbatical" after successful breeding [START_REF] Jiguet | Individual breeding decisions and long-term reproductive strategy in the King Penguin Aptenodytes patagonicus[END_REF][START_REF] Bohec | Population dynamics in a long-lived seabird: I. impact of breeding activity on survival and breeding probability in unbanded king penguins[END_REF].

Exposure

The war took place at a time where most adult king penguins had left the colony for foraging trips, with the possible exception of some late breeders (see Table 1). All chicks remained in the colony for the entire duration of the war. Yet, paradoxically, it is probable that breeding adults were more exposed to the war than chicks. The king penguin colony of the Falkland Islands is located at Volunteer Point, in the north-east of East Falkland. Although it is relatively close to the capital Stanley (about 20 kilometers as the crow flies), the colony was insulated from the main land, air, and sea battles. Battles were concentrated around key locations, e.g., Stanley and San Carlos Water (in the west of East Falkland). No landmines were laid by the Argentine at Volunteer Point. Meanwhile, most of the combats at air and sea took place within the Total Exclusion Zone-a circle of radius 370 km from the center of the Falkland Islands-declared by the United Kingdom in April 1982. 5 . This circle was within king penguins' typical foraging range at this time of the year [START_REF] Pütz | Spatial and temporal variability in the foraging areas of breeding king penguins[END_REF][START_REF] Baylis | Winter foraging site fidelity of king penguins breeding at the Falkland Islands[END_REF].

4 Note, however, that the timing and success of late breeding attempts depend on food availability, which varies across years and locations [START_REF] Olsson | Changes in king penguin breeding cycle in response to food availability[END_REF] 5 The British first declared a Maritime Exclusion Zone on April 7, prohibiting Argentine warships from approaching the islands. On April 30, the UK government declared a Total Exclusion Zone covering the same area: any aircraft or ship (military or civilian) from any country entering the zone was liable to be fired upon without further warning. Argentina responded by declaring the entire South Atlantic an Exclusion Zone (May 11). The British TEZ served several purposes, the most important of which were to deter neutral vessels from approaching the area (in order to simplify target identification problems) and to limit the conflict to a single area and to the forces dispatched in this zone [START_REF] Young | Maritime exclusion zones: A tool for the operational commander?[END_REF]. Obviously, it did not entail any commitment not to fight outside the zone: the most lethal episode of the Falklands War-the sinking of ARA General Belgrano by a British submarine on May 2-occurred some 55 km outside the TEZ. et al., 2008). 6 Another reason is the 1997-98 El Niño event, which triggered an exceptional increase of sea surface temperatures and probably affected population growth in the following years (Le [START_REF] Bohec | King penguin population threatened by Southern Ocean warming[END_REF].

King penguin population data

Yearly population estimates come from a variety of sources (see Appendices A and F for more details), including datasets made available by research institutes such as the Australian Antarctic Division [START_REF] Woehler | King penguin breeding population at heard island, ver. 1[END_REF], scholarly articles, and grey literature. A first difficulty is that estimates are given in different units (breeding pairs, adults or chicks) depending on the source. In order to obtain homogeneous time series, we converted all observations in breeding pairs-equivalent numbers and removed outliers, defined as data points reflecting a growth rate of more than 125% from one year to another (see Appendix A for details). A second difficulty lies in the missing data, which make up about half of the yearly observations. ITS analysis abhors a vacuum so we interpolated missing data assuming a geometric growth rate. We thus obtain a time series without gaps including 19 observations before the war, and 16 observations after.

Figure 1 displays our data for the Falkland Islands (including information on observed and interpolated data points) for the whole investigation period. The population is clearly increasing in the pre-treatment as well as in the post-treatment periods, but with a somewhat slower growth rate during the latter. The two negative shocks occurring in the immediate aftermath of the war and in 1989 are consistent with immediate and delayed effects of the war (i.e., adult and chick mortality, respectively). 7 Yet, these data points have to be considered with great caution. Because of the asynchronous breeding cycle of king penguins, yearly counts are susceptible to measurement error because the entire breeding population is never present at the colony at the same time: [START_REF] Foley | Correcting for within-season demographic turnover to estimate the island-wide population of king penguins (aptenodytes patagonicus) on south georgia[END_REF] estimate potential measurement errors up to 50% in the case of South Georgia. These errors partly explain why we sometimes observe growth rates higher than 50% in consecutive years, which is biologically impossible for king penguins (recall that one pair produces one single chick in a year at best). There are two implications. First, no firm conclusion can be drawn from the observation of individual data points, although the trend over a long period can be analyzed. Second, such an analysis requires sensitivity tests addressing the underestimation problem -an issue that will be dealt with later.

Method

Interrupted time series (ITS) analysis seeks to identify the effect of a shock (or 'treatment') in longitudinal data when no comparable control unit is available. This method is commonly used in epidemiological studies and for the evaluation of public health policies, where data are generally available at a population level and the treatment is affecting the whole population [START_REF] Bernal | Interrupted time series regression for the evaluation of public health interventions: a tutorial[END_REF]. Under these conditions, ITS provides the best way to assess the magnitude of the treatment effect [START_REF] Wagner | Segmented regression analysis of interrupted time series studies in medication use research[END_REF]. Interestingly, our case shares some similarities. First, Volunteer Point's king penguin population cannot be neatly divided into a

7 Chick mortality (which we do not observe) can be reflected in the adult population data with a delay of 5 to 8 years, which corresponds to the time king penguins take to reach maturity [START_REF] Weimerskirch | The breeding biology and population dynamics of king penguins Aptenodytes patagonica on the Crozet Islands[END_REF].

sub-population exposed to the war and one unexposed. In other words, there is no directly observable control group. Second, we detain only yearly data at the population level: this level of granularity of the data fits well with ITS. Concretely, ITS here relies on the use of annual time series on king penguins' abundance prior to 1982 to build a hypothetical scenario depicting what would have happened in the absence of the war (counterfactual). The effect of the war is thus measured by the difference between observed values and the counterfactual.

We use the log-abundance of breeding pairs (ln brp t ) to measure the effect of Falklands war on the population growth rate of the colony. Our main independent variables are the number of years elapsed since the beginning of the study (T t ) and since the beginning of the Falkland War (war t ).

We also include a set of variables controlling for climatic conditions, X t X t X t , which varies depending on the specification used. Climate change is one of the main threats to king penguins populations [START_REF] Bost | King penguin[END_REF][START_REF] Trathan | Pollution, habitat loss, fishing, and climate change as critical threats to penguins[END_REF][START_REF] Ropert-Coudert | Happy feet in a hostile world? the future of penguins depends on proactive management of current and expected threats[END_REF]. Direct effects of climate change include the increased frequency of heat waves, severe rains and storms during El Nino events [START_REF] Ropert-Coudert | Happy feet in a hostile world? the future of penguins depends on proactive management of current and expected threats[END_REF]. Indirect climate effects are food web shifts that threaten penguins' ability to feed themselves or their chicks: changes in water temperatures affect the distribution and abundance of preys through changes in either ocean productivity or the distribution of currents. As a consequence, warmer sea surface temperature have been found

to increase diving depth (which reflects higher energy expenditures) and foraging trip length and duration [START_REF] Péron | Projected poleward shift of king penguins'(aptenodytes patagonicus) foraging range at the crozet islands, southern indian ocean[END_REF][START_REF] Bost | Large-scale climatic anomalies affect marine predator foraging behaviour and demography[END_REF] among king penguins. As Southern Ocean warming has intensified since the 1980s, this may be an important confounder in our study.

We rely on prior studies by Le [START_REF] Bohec | King penguin population threatened by Southern Ocean warming[END_REF], [START_REF] Saraux | Effects of individual pre-fledging traits and environmental conditions on return patterns in juvenile king penguins[END_REF] and [START_REF] Bost | Large-scale climatic anomalies affect marine predator foraging behaviour and demography[END_REF] to identify climatic variables associated with higher mortality of adults, juveniles, and chicks (see details in Appendix B). These three studies rely on the Southern Oscillation Index (SOI), a global climatic variation index that captures a combination of several weather features (Le [START_REF] Bohec | King penguin population threatened by Southern Ocean warming[END_REF], and a more local indicator, namely sea surface temperatures (SST) averaged over the foraging area around the colony, as a proxy for food availability and abundance. However, these three studies use partly different measurement and time lags.8 Furthermore, given our data, we need to account for the fact that chick or juvenile mortality (which we do not observe) can be reflected in the adult population data with some delay-up to eight years since about 90% of king penguins have started breeding at age 8 [START_REF] Weimerskirch | The breeding biology and population dynamics of king penguins Aptenodytes patagonica on the Crozet Islands[END_REF]. We thus estimate several models with up to eight lags of SOI. With regard to SST, we use the same temporal lag structure, and we additionally define several zones over which temperatures are averaged. These measures are based on empirical studies recording foraging trips of several adult king penguins around the Falklands [START_REF] Pütz | Spatial and temporal variability in the foraging areas of breeding king penguins[END_REF][START_REF] Baylis | Winter foraging site fidelity of king penguins breeding at the Falkland Islands[END_REF] and on a general estimate of the maximal distance king penguins can travel on a single foraging trip before risking massive starvation of their chicks [START_REF] Péron | Projected poleward shift of king penguins'(aptenodytes patagonicus) foraging range at the crozet islands, southern indian ocean[END_REF]: details are provided in Appendix B. On this basis, we identify three foraging zones (FZ). F Z1 corresponds to a zone of 225 km radius around the Falklands-sector (50.5-52.4°S, 56.2-60°W ). F Z2 corresponds to a zone of 545 km radius, which is equivalent to (48.6-54.3°S, 52.5-63.7°W ) and F Z3 is the sector (46.7-56.2°S, 50.6-65.6°W )

corresponding to a maximum trip length of 722km -that is, the largest sustainable foraging zone according to [START_REF] Péron | Projected poleward shift of king penguins'(aptenodytes patagonicus) foraging range at the crozet islands, southern indian ocean[END_REF]. On this basis, considering the different lag structures and measurements, we identify 243 combinations of variables, which we test in separate specifications. We then select the 5 "best" models according to the Akaike's Information Criterion with correction for small samples (AICc). Details on the selection of predictors can be found in Appendix B and descriptive statistics are provided in Table A3.

The regression model used to fit these data is:

ln brp t = β 0 + β 1 T t + β 2 war t + β env X t β env X t β env X t + ε t (1)
where β 0 captures information relative to the situation in 1963, β 1 corresponds to the slope of the logabundance before the war, and β 2 captures the effect of the war on this slope. In other words, β 2 is the measure of the effect of the Falklands War on the log abundance of king penguins. Finally, β env β env β env are the coefficients associated with climatic variables.

Results

All results were obtained using Stata 14.2. 9 Table 2 presents the five models with the lowest AICc (full regression table are displayed in Appendix D). Falklands War is found to have a detrimental effect on king penguins' population growth. This effect is significant for Models 1-5, but also for all other estimated models.

For the sake of parsimony, we focus on our best model accordinf to the AICc criterion: M1. Estimated value of the coefficient associated with the Falklands War, β2 , is -0.106 (95%CI = -0.183 --0.029) which corresponds to the difference between the log-abundance slope before and after the war. The five best models are those with the lowest Akaike information criterion corrected (AICc). The best one is indicated in bold.

AICc = -2LogL + 2k + 2k(k+1) N -k+1
where k is number of parameters estimated, N the number of observations and LogL is the log likelihood of the estimated model. ∆AICc is the difference between the lowest the AICc of the model and the lowest one. 'Lags' refers to the lagged values potentially added in the model. In particular, L1-L4 in model 4 means that values of SST in t -1, t -2, t -3 and t -4 are included as control variables in (1). Only coefficients associated with the war variable are reported. Newey-west standard errors are reported and the lag structure is defined by the rule-of-thumb N 1/4 = 2. * , * * and * * * mean significant at 10%, 5% and 1%, respectively. Full regression table for M1-M5 is presented in Appendix D.

depicts a conservative scenario corresponding to the lower limit (LL) of the 95% CI of the war effect. 10 In that scenario, the effect size of the war corresponds to 194 breeding pairs, that is, 56.23% of the actual size of the population in this year. As a side note, the results presented above confirm the detrimental effect of climate warming, in line with previous studies by Le [START_REF] Bohec | King penguin population threatened by Southern Ocean warming[END_REF] and [START_REF] Bost | Large-scale climatic anomalies affect marine predator foraging behaviour and demography[END_REF]. We regularly find a negative and significant 10 One should note that the lower limit of the effect corresponds to the upper limit of the coefficient estimated. In the rest of the paper we will systematically use lower limit without specifying that we mean lower limit of the war detrimental effect.

impact of SST in t on the log-abundance of breeding pairs. Lagged values of SST do not consistently improve the predictive power of the models, but when they are included, their sign is also negative. SOI, on the other hand, does not exhibit the expected sign, but the variable does not attain statistical significance.

Sensitivity analysis

We perform several sensitivity tests. A first concern is that the start of our investigation period coincides with the origins of the Volunteer Point's colony. This raises two related issues: artificial growth due to potential immigration from South Georgia11 , and high variability of growth rates-since these tend to be more volatile in small populations [START_REF] Weimerskirch | The breeding biology and population dynamics of king penguins Aptenodytes patagonica on the Crozet Islands[END_REF]. More concretely, this means in our case that the trend in the first years might be abnormally high, leading to an overestimation of the slope of the logabundance in the pre-treatment (and thus of the effect of the war). Moreover, the diagnostic of the residuals of M1 -presented in Table A2 -suggests that the fit of the model is relatively poor up to 1970, which can cast doubt on our results. To address these concerns, we delay the start of our study to 1971 and re-estimate M1.

The war coefficient is -0.142 (95%CI=-0.190 --0.093) which is even stronger that the coefficient estimated for the full period. Second, we drop the 1982 data point because we lack information on the timing of the observation, i.e., we do not know whether the count took place before or after the war. We obtain: β2 = -0.095 (95%CI=-0.168 --0.021). Third, we estimate a model without any covariates: results remain remarkably stable. Fourth, for reasons that are related to the peculiarities of king penguins' breeding cycle, population counts tend to be chronically underestimated. [START_REF] Foley | King penguin populations increase on south georgia but explanations remain elusive[END_REF][START_REF] Foley | Correcting for within-season demographic turnover to estimate the island-wide population of king penguins (aptenodytes patagonicus) on south georgia[END_REF] estimate that measurement error can be as high as 50%. To address this concern, we run simulations in which each observation is multiplied by a random number lying between 1 and 1.5 to account for underestimation. We randomly draw 10,000 alternative times series and discard those presenting abnormal annual growth rate (i.e. superior to 125%). We obtain 6,080 credible alternative time series and re-estimate M1. The effect of the war on the slope of the log-abundance is -0.107 (95%CI = -0.169 --0.044) which implies that our main findings are not driven by measurement error. Moreover, in all 6,080 estimations, the coefficient associated with the war remains negative and at least significant at the 10% level. A visual representation of the lower limits (LL) of the 95% and 90% CI of the effect of war on the number of breeding pairs in 1997 is provided in Figure 4. Most of the 95% LL values lie between -200 and -400 meaning that the LL obtained with M1 (-194) is a quite conservative measure.

Fifth and finally, an autoregressive integrated moving average model with explanatory variables -in our case, an ARIMAX(0,1,0) -was used as an alternative to the Newey-West standard errors to account for autocorrelated data. We difference all variables included in M1 and we find a war coefficient equal to -0.232 (95%CI=-0.448 --0.015). If the size of the coefficient is larger than those obtained with log-abundance data, it is still in line with prior findings.

Controlled interrupted time series

In the previous analysis, the construction of the counterfactual was based on the pre-treatment population trend and the evolution of environmental conditions. However, Linden (2017) identifies some factors that can cast doubts on results obtained without considering any control group. First, the decrease in the trend observed after 1982 may have been caused by unobserved events (for example, conservation efforts leading to an increase of the number of predators, e.g., killer whales). Second, our period of investigation is long (35 years) and the evolution of measuring instruments may give rise to validity issues: for example, if measurement errors are decreasing over time, it may have impacted our statistical analysis. Third, the effects of climate change on king penguins are complex (Le [START_REF] Bohec | King penguin population threatened by Southern Ocean warming[END_REF] and it is possible that we only imperfectly captured the phenomenon. Consequently, a part of the decrease in the slope of king penguins log-abundance may be actually due to climate warming and not the war. For all these reasons, our coefficient of interest β2 may be biased by unobserved time-varying confounders.

A solution to deal with this issue is to include a control group in our analysis, i.e., to use a controlled interrupted time series (CITS) design [START_REF] Bernal | The use of controls in interrupted time series studies of public health interventions[END_REF]. Comparing the treated unit to other, comparable Based on 6,080 simulated time series corrected for measurement errors ranging from 0 to 0.5. The exponential of the difference between the fitted value and the lower limit of the counterfactual in 1997 are presented. For the sake of comparison, recall that the 95% LL was -194 in our main estimation.

but unexposed units would allow us to distinguish the effect that can safely be attributed to the war from the one that is in fact due to time-varying confounders. In the absence of perfectly comparable control unit, CITS relies on the creation of a "synthetic" control using the method developed by Abadie and colleagues [START_REF] Abadie | The economic costs of conflict: A case study of the Basque country[END_REF][START_REF] Abadie | Synthetic control methods for comparative case studies: Estimating the effect of California's tobacco control program[END_REF][START_REF] Abadie | Using synthetic controls:feasibility, data requirements, and methodological aspects[END_REF]. A synthetic control is a weighted average of control units unaffected by the treatment, whose aim is to closely replicate the pre-treatment trend of the treated unit in order to identify significant post-treatment divergences.12 Following Linden (2018), we first estimate the synthetic control so that its difference with the actual population is minimized over the period 1963-1981 (i.e. before the Falklands War). We then use the 'synthetic Falklands' as a control group to perform a CITS and we compare our results with those obtained in the single-group ITS presented above.

The first step is the construction of the synthetic Falklands. Based on the availability of data, we are able to recompose time series for thirteen colonies (donors) over the investigation period: Baie du Marin, Crique de la chaloupe, Jardin japonais and Petite Manchotière from the Crozet Islands; Spit Bay North, Spit Bay South, and Vahsel Moraine from Heard Island; Lusitania Bay from Macquarie Island; Archway Bay and Sealers' Beach from Marion Island; and Fortuna Bay, Gold Harbour and Saint Andrew's Bay from South Georgia. As for the Falklands Islands, missing data were filled using a geometric growth rate in order to obtain time series without gaps. Another issue comes from the fact that population size in 1963 varies from 1 (Falklands Islands) to 45,000 (Baie du Marin). Consequently, we re-scaled each time series by dividing each data point by the average number of breeding pairs in the colony during the period 1963-1997. By doing so, we obtain a donor pool made up of comparable time series (see Appendix E for a visual representation of re-scaled populations and for details on the Synthetic Control Method). We use the standard nested optimization procedure 13 to obtain the donors weights offering the best fit with the observed log-abundance of king penguins from Volunteer Point. Only past values of the re-scaled populations are used as predictors to avoid overfitting issues [START_REF] Kaul | Synthetic control methods: Never use all preintervention outcomes as economic predictors[END_REF]. The combination of weights minimizing the root mean squared prediction error (RMSPE) are presented in Table 3. The best way to build a comparable synthetic control is to use a combination of three colonies: Baie du Marin, Spit Bay North and Spit Bay South (the RMSPE over the pre-treatment equals 0.249). The comparison of the trends of the 'real' Falklands and the synthetic Falklands (displayed in Appendix E) suggests that the war has significantly reduced the population growth rate. More specifically, since this deceleration is visible from the end of the 1980s onward, it is more consistent with a decreased breeding success than with an increased adult mortality rate. However, given the poor quality of the data, this pattern must be interpreted with caution.

The second step consists in using this synthetic control in a CITS analysis, by estimating the following regression:

ln brp it = β 0 + β 1 T it + β 2 real it + β 3 post82 it + β 4 real_T it + β 5 war it + ε it (2)
where T it is the time elapsed since the start of the investigation period, real it is a variable that takes the 13 See [START_REF] Abadie | Synth: Stata module to implement synthetic control methods for comparative case studies[END_REF].

value of 1 for the "real" Falklands and 0 for the "synthetic" Falklands, post82 it is the time elapsed since the war, real_T it is the interaction between T it and real it , and war it is the interaction between real it and post82 it . war it is the variable measuring the effect of the war on the slope of king penguins log-abundance.

Regarding the interpretation of coefficients, β 0 captures the initial level of the log-abundance in the synthetic which confirms the results we obtained, both regarding the existence and the magnitude of a negative effect of the war on king penguins' log-abundance. Figure 3 provides a visual comparison with results from the single-group ITS.

Several issues are addressed in Columns ( 2)-( 4) of Table 4. First, one possibility is that colonies located in South Georgia were partially affected by the Falklands War because some fighting took place there. We thus exclude Fortuna Bay, Gold Harbour and Saint Andrew's Bay from our donor pool. Unsurprisingly, these exclusions do not change anything because these three colonies are not part of the synthetic control (see Column 4 addresses a final source of concern. Although king penguins tend to be faithful to the colony in which they were born, we cannot rule out the possibility of local migrations within each island, possibly following local shocks,15 and especially when colonies are relatively close to each other: such migrations may artificially inflate the growth rate of some colonies, thus putting into question the quality of the counterfactual. In order to mitigate that risk, we use data at a higher aggregation level, that is, at the island or archipelago level: considering the distance between locations in which king penguins breed,16 between-island migrations must be even rarer than within-island migrations. We obtain a donor pool composed of six islands/archipelagos: Crozet Islands, Heard Island, Kerguelen Islands, Macquarie Island, Marion Island, and South Georgia. Using the same procedure, we obtain a synthetic control only made up of Heard Island.

In other words, the best control group is the Heard Island re-scaled population. Thus, in essence, what we obtain is an interrupted time series analysis with a control group as described by Linden (2017), with the SCM approach ensuring that the treated and the control case are comparable. Column (4) exhibits results very close to those obtained at the colony level with the full donor pool: the war coefficient is -0.101 (95%CI=-0.163 --0.038).

Conclusion and discussion

All our results point toward the same conclusion: the Falklands War did have a detrimental effect on king penguins' population growth rate. The magnitude of the effect is quite large: the size of the colony should have been at least 56% larger than its observed size in 1997. These results are robust to changes in the model specification, in the estimation method and in the period, and are not affected by measurement error, in spite of some variability in the size of the confidence interval.

What happened to the Falklands' king penguins? Among possible impacts of the war, oil spills are the most likely. The Falklands War resulted in a dozen of shipwrecks, all of which occurred within king penguins' foraging range. Penguins are especially vulnerable to oil spills because they cannot feed ashore, do not fly (and are therefore probably unable to detect petroleum), and swim at or near the surface [START_REF] Garcia-Borboroglu | Petroleum pollution and penguins: marine conservation tools to reduce the problem. Marine pollution: new research[END_REF]. Furthermore, as central-place foragers, they are susceptible to swimming repeatedly through contaminated zones [START_REF] Trathan | Pollution, habitat loss, fishing, and climate change as critical threats to penguins[END_REF]. Today, oil pollution through shipwrecks and oil leakages is considered one of the main anthropogenic causes of mortality among penguins [START_REF] Trathan | Pollution, habitat loss, fishing, and climate change as critical threats to penguins[END_REF]. Among adverse consequences, oil causes feathers to lose their waterproofing ability (which, in turn, impacts the bird's thermoregulatory capacities during its stay in the water) and can even reduce buoyancy: birds that depend on marine resources for food then face a trade-off between starvation and drowning or hypothermia [START_REF] Leighton | The toxicity of petroleum oils to birds[END_REF][START_REF] Garcia-Borboroglu | Petroleum pollution and penguins: marine conservation tools to reduce the problem. Marine pollution: new research[END_REF]. Moreover, poisoning can follow from ingestion of contaminated water or food sources (e.g., marine invertebrates or fishes), with potentially irreversible damages to vital organs such as the stomach, the liver and kidneys, or to the immune system [START_REF] Leighton | The toxicity of petroleum oils to birds[END_REF][START_REF] Garcia-Borboroglu | Petroleum pollution and penguins: marine conservation tools to reduce the problem. Marine pollution: new research[END_REF]. Although oil ingestion has often sublethal effects on adults, exposure of eggs during incubation can reduce eggshell thickness and slow embryonic growth, resulting in high rates of embryo mortality (even after minimal exposure) and developmental defects [START_REF] Leighton | The toxicity of petroleum oils to birds[END_REF]. Reduced reproductive output can also result from a decrease of sexual hormones [START_REF] Fowler | Hormonal and reproductive effects of low levels of petroleum fouling in magellanic penguins (spheniscus magellanicus)[END_REF], and from the fact that oiled breeders have lessened foraging abilities and are unable to meet their chick's energy demands [START_REF] Wolfaardt | Breeding success of african penguins spheniscus demersus at dassen island, especially after oiling following the apollo sea spill[END_REF].

Oil spills probably affected adults only. As discussed earlier, chicks ashore in the colony were relatively insulated from land and sea combats. No habitat loss occurred: for example, we found no report of landmines laid at Volunteer Beach. However, one factor that perhaps affected chicks is exposure to noise. The RAF organized several long-distance aerial bombing of Stanley's airport (Operations Black Buck 1 to Black Buck 7) during which bombers arriving from Ascension Island (a military airfield in the Atlantic) overflew the northeastern part of East Falkland at short distance from the colony.17 Noise-which has recently been recognized as a global pollutant-can impact birds in multiple ways [START_REF] Ortega | Effects of noise pollution on birds: A brief review of our knowledge[END_REF]. Among immediate effects, approach of aircraft can induce panic responses and lead birds to flee and trample their eggs or chicks, or leave them undefended against predators [START_REF] Cooper | Airdrops and king penguins: a potential conservation problem at sub-antarctic marion island[END_REF][START_REF] Hughes | Short-term responses of king penguins aptenodytes patagonicus to helicopter disturbance at south georgia[END_REF].18 Among long-term effects, noise can trigger stress responses resulting in weight loss, change in hormone levels, and damages to the immune system, and cause hearing impediments19 that compromise adults' ability to hear predators, to communicate with their conspecifics, or to identify their chicks in a crowded colony [START_REF] Ortega | Effects of noise pollution on birds: A brief review of our knowledge[END_REF]. The latter aspect is especially important as King penguins recognize their mates and chicks exclusively through vocalization [START_REF] Aubin | Cocktail-party effect in king penguin colonies[END_REF][START_REF] Jouventin | Finding one's mate in a king penguin colony: efficiency of acoustic communication[END_REF].

An important limitation of the present study is that we are unable to distinguish between these mechanisms. This is due both to data limitations and to the indeterminacy of some of the potential effects. For example, adult mortality during the war was necessarily followed by chick mortality, since chicks are unable to forage for themselves until fledgling. Likewise, a long-term decrease of the population growth rate (if any) could be attributed to damages to the marine ecosystem but this effect could be confounded by lasting impacts of war exposure on affected individuals: for example, oil spills can have sublethal effects that do not kill adults immediately but reduce their life expectancy in the long run, and [START_REF] Wolfaardt | Breeding success of african penguins spheniscus demersus at dassen island, especially after oiling following the apollo sea spill[END_REF] find that oiled penguins have a reduced breeding success several years after exposure. This drawback is partially compensated by the fact that we have time series that are long enough for delayed responses (e.g., reproductive success) to be incorporated in the data. Yet, while scholars [START_REF] Mallory | Seabirds as indicators of aquatic ecosystem conditions: a case for gathering multiple proxies of seabird health[END_REF] recommend using multiple indicators (e.g., contaminants concentration in feathers, body mass, changes in diet, increases in foraging distance or duration, breeding success) to reflect changes at various temporal scales, our main indicator is too crude to distinguish between, for example, decreased breeding success vs. long-term decrease of adults' life expectancy, or to detect possible sublethal effects.

The results of this study have two implications for the analysis of the ecological impact of warfare. First, we show that conflicts can have deleterious consequences on the local ecosystem, even in the absence of deliberate strategies to destroy the environment, and even when the conflict did not have severe economic and institutional repercussions in the affected country. Second, the ecological consequences of armed conflict are lasting and go beyond mere accidental killings: the present analysis shows that the Falklands War affected king penguins' population growth rate long after the hostilities stopped. These conclusions must be nuanced, given the specificity of our case: the Falklands War was a technologically heavy conflict and king penguins are a highly sensitive species. Future research will have to assess whether the present results can be generalized to other types of conflicts and ecosystems.

Appendices

A Data appendix (Falkland Islands)

This appendix presents our data for the Falkland Islands. Data were gathered from a variety of sources, which are displayed in the table below. In addition to large public datasets, we used estimates found in scholarly articles and reports.

These counts do not necessarily rely on similar methods. Broadly speaking, systematic census data rely either on counts of breeding pairs, often made at the beginning of the reproductive season,20 or on counts of chicks at the end of the austral winter. The latter method is used more often in the Falklands, where breeding success is relatively high [START_REF] Pütz | Spatial and temporal variability in the foraging areas of breeding king penguins[END_REF][START_REF] Otley | Breeding patterns of king penguins on the falkland islands[END_REF] and the number of living chicks can be viewed as a proxy of the number of breeding pairs with a margin of error due to winter mortality [START_REF] Bingham | The distribution, abundance and population trends of gentoo, rockhopper and king penguins in the Falkland Islands[END_REF]. For each data point, we indicated the counting method when available, but note that many of them are not census data but rough estimates and were not provided with precise information.

One important step was to convert estimates expressed in chicks or adults in breeding pairs-equivalent numbers. Following [START_REF] Jiguet | Individual breeding decisions and long-term reproductive strategy in the King Penguin Aptenodytes patagonicus[END_REF] and Le [START_REF] Bohec | Population dynamics in a long-lived seabird: I. impact of breeding activity on survival and breeding probability in unbanded king penguins[END_REF], the number of adults was converted into numbers of breeding pairs assuming a 15% rate of non-breeders (these articles estimate this rate at 13% and 17%, respectively). The sole exception is data provided by [START_REF] Paleczny | Population trend of the world's monitored seabirds, 1950-2010[END_REF]: since they converted estimates given in breeding pairs in adult population estimates assuming one-third of non-breeding birds, we converted these numbers back to their original unit. The number of chicks in a colony can be considered a reasonable approximation of the number of breeding pairs21 once chick mortality is accounted for: estimates expressed in number of chicks were converted into numbers of breeding pairs assuming a 20% mortality rate [START_REF] Bingham | The distribution, abundance and population trends of gentoo, rockhopper and king penguins in the Falkland Islands[END_REF].

These conversion procedures assume that (1) the ratio of non-breeders in the total adult population is stable over time, and (2) chicks' mortality rate is also stable over time. The first assumption is reasonable:

it would be false only if king penguins were more likely to refrain from breeding under poor environmental conditions, but to the best of our knowledge, there are no reports of such strategies among king penguins.

King penguins are more likely to take a sabbatical following a successful attempt in the previous year (Le [START_REF] Bohec | Population dynamics in a long-lived seabird: I. impact of breeding activity on survival and breeding probability in unbanded king penguins[END_REF], which suggests that the share of nonbreeders may increase after exceptionally good years; however, the annual proportion of non-breeders displays a small inter-annual variability and does not seem to be influenced by environmental conditions [START_REF] Jiguet | Individual breeding decisions and long-term reproductive strategy in the King Penguin Aptenodytes patagonicus[END_REF][START_REF] Bohec | Population dynamics in a long-lived seabird: I. impact of breeding activity on survival and breeding probability in unbanded king penguins[END_REF].

Assumption ( 2) is more problematic because it depends on actual breeding success, which may vary depending on environmental conditions and food availability before and during the winter fast. However, the amount of error is limited due to constraints on the reproductive output of king penguins (i.e., the chicks will never exceed a certain share of the total population).

Regardless of the method, population estimates are susceptible to systematic underestimation. As noted by [START_REF] Foley | Correcting for within-season demographic turnover to estimate the island-wide population of king penguins (aptenodytes patagonicus) on south georgia[END_REF] 22 A 50% growth rate from Y t-1 to Yt is unrealistic but theoretically possible. It assumes a) a null or negative growth rate from Y t-6 to Y t-1 (recall that it takes on average 5-6 years before chicks are recruited into the breeding population) and b) a 100% breeding success in Y t-6 . Notes:

Data source

Location

e In 1983and 1984[START_REF] Pistorius | Population development and historical occurrence of king penguins at the Falkland Islands[END_REF] report two counts (July and September, and June and July, respectively). Given the season in which these counts were made, we took the earliest ones (i.e., July 1983 andJune 1984), which correspond to the largest numbers in both cases. In both years, counts of chicks and adults are provided: however, we selected only counts of chicks, since the counts were made in periods in which a large number of adults were on winter foraging trips while all chicks were ashore. In both cases, the data suggests that the number of adults is underestimated given the number of chicks (78 chicks and 135 adults in July 1983, and 83 chicks and 115 adults in June 1984).

f Pre-fledged chicks (i.e., those that survived the austral winter) are counted in November at Volunteer Point. Overwinter mortality is comparatively low on the Falklands, where about 90% of chicks survive winter [START_REF] Otley | Breeding patterns of king penguins on the falkland islands[END_REF]. This estimate is consistent with the 20% overall mortality rate we apply to convert the number of fledglings into numbers of breeding pairs. g

We took the largest estimate from the range given by the authors.

B Selection of covariates in the main estimations

Environmental conditions

We use two indicators of climate change. First, the Southern Oscillation Index (SOI) is a global climatic variation index that captures a combination of several weather features (Le [START_REF] Bohec | King penguin population threatened by Southern Ocean warming[END_REF]. Second, we use a more local indicator, namely sea surface temperatures (SST) averaged over the area around the Falkland Islands, as a proxy for food availability and abundance. Both SST and SOI have been identified

as important predictors of king penguins survival and breeding success (Le [START_REF] Bohec | King penguin population threatened by Southern Ocean warming[END_REF][START_REF] Saraux | Effects of individual pre-fledging traits and environmental conditions on return patterns in juvenile king penguins[END_REF][START_REF] Bost | Large-scale climatic anomalies affect marine predator foraging behaviour and demography[END_REF].

Monthly sea surface temperatures are averaged in king penguins' foraging zone, which we define as an area around the Falklands23 representing the distance potentially covered by a "typical" adult king penguin during one of its foraging trips. 24 We use several estimates of king penguins' foraging range, two of which rely on empirical studies [START_REF] Pütz | Spatial and temporal variability in the foraging areas of breeding king penguins[END_REF][START_REF] Baylis | Winter foraging site fidelity of king penguins breeding at the Falkland Islands[END_REF] that recorded foraging trips of several adult king penguins around the Falklands and reported, for each individual trip, the date and the maximal distance to the colony as the crow flies. We use the median of maximal distances reported by both studies (N = 47) in selected months. First, we compute the median maximal distance in April and May to define F Z1 (225 km, N = 31). April and May are the end of the chick-rearing period, that is, a period in which food abundance starts to decrease while chicks' energy demands are growing (i.e., they must be fed abundantly and frequently in order to be able to sustain several months of fasting during winter). The area thus defined corresponds to a narrow foraging range, in which SST increase may jeopardize king penguins' reproductive success. 25Second, we define F Z2 (545 km, N=16) as the median maximal distance to the colony for foraging trips undertaken in June, July and August. The austral winter months correspond to a period of poor food availability, in which chicks fast and adults undertake longer foraging trips to feed.

Although they are empirically grounded, these two measures have a limitation: they rely on a relatively small number of cases in two particular years, which makes them dependent on the climatic conditions during those years. 26 For example, if SST were relatively close to average in these years, foraging trips were correspondingly short and foraging areas are therefore too narrowly defined. In order to address this issue, We converted these distances in geographic coordinates. We then drew a square area around Volunteer Point's coordinates (51.47°S, 57,84°W) so that the maximal distance to the colony is 225 km (F Z1), 545 km (F Z2) and 722 km (F Z3). The size of each area is defined as follows:

Area(F Z i ) = 2×Max.Dist.i √ 2 km 2
where Volunteer Point is the center of the square. After converting kilometers into degrees using standard conversion rules, we obtained areas displayed in Table A1 and Figure A1. We use monthly values of SST (averaged over F Z1, F Z2 and F Z3) using data from the NCEP Reanalysis Dataset [START_REF] Kalnay | The ncep/ncar reanalysis 40-year project[END_REF]. This dataset has a spatial resolution of 2.5 × 2.5 degree latitude and longitude, therefore grid cells exceeding the FZ's delineation are weighted accordingly. In the end, we obtain three time series labelled: SST t /F Z1, SST t /F Z2 and SST t /F Z3.

Potential covariates not included

Human activities

The main estimates do not include covariates capturing the impact of human activities. This choice is partly justified by the fact that human activities are not currently considered one of the most significant threats to king penguins [START_REF] Trathan | Pollution, habitat loss, fishing, and climate change as critical threats to penguins[END_REF]. In what follows, we discuss potential anthropogenic causes of king penguins mortality in the Falklands (beyond climate change) and detail the reasons why we do not include them.

One obvious candidate is the size of the Falklands' human population. However, data limitations prevents us to include this variable in the predictors (population counts are available only every five years, i.e., 80% of the time series would be missing). Furthermore, there are several reasons to believe that human population size per se did not influence king penguins' demography. First, the population of the Falklands is a small one, and did not change dramatically during the investigation period (from ca. 2,100 inhabitants in 1965 to ca. 2,500 in 1995). Second, the Falkland Islands have an extremely low population density. Most of the inhabitants live in Stanley (the capital), which is about 70 kilometers away from Volunteer Point (king penguins' main breeding site) by land, and there are no sizeable settlements in the immediate vicinity of Volunteer Point. It seems that the main source of direct human disturbance are punctual visits by tourists [START_REF] Otley | Nature-based tourism: experiences at the volunteer point penguin colony in the falkland islands[END_REF]. Third, the effect of human presence is not straightforward: there is evidence that king penguins repeatedly exposed to human presence learn to consider these disturbances as innocuous and exhibit attenuated stress responses [START_REF] Viblanc | Coping with continuous human disturbance in the wild: insights from penguin heart rate response to various stressors[END_REF]. [START_REF] Bingham | The decline of falkland islands penguins in the presence of a commercial fishing industry[END_REF] compares the breeding success of several Falkland Islands penguin species in colonies exposed and unexposed to tourism during two breeding seasons; the impact of human presence is virtually null. A further study by [START_REF] Otley | Nature-based tourism: experiences at the volunteer point penguin colony in the falkland islands[END_REF] broadly confirms these findings with regard to Gentoo penguin colonies.

Although it seems unlikely that king penguins' demography was affected by human population per se, it might have been influenced by its correlates. The Falkland Islands' economy, which used to be primarily based on agriculture (especially wool export), grew and diversified during the postwar period [START_REF] Royle | Changes in the Falkland Islands since the conflict of 1982[END_REF][START_REF] Royle | The falkland islands. Extreme Tourism: Lessons from the World's Cold Water Islands[END_REF]: developing sectors include oil, fishing industries, and tourism. For reasons explained above, tourism is not a concern; neither is oil since drilling began after our post-treatment period [START_REF] Bingham | The decline of falkland islands penguins in the presence of a commercial fishing industry[END_REF][START_REF] Garcia-Borboroglu | Petroleum pollution and penguins: marine conservation tools to reduce the problem. Marine pollution: new research[END_REF]); yet, fisheries might be. Industrial fishing in the waters surrounding the Falklands began in the 1970s but really took off by the end of the 1980s, as the Falkland Islands government started to sell licenses allowing foreign vessels to fish within the Falklands Islands Conservation Zone (a zone of 240 km radius around the islands). The selling of these licenses has become the main source of government income shortly after the war [START_REF] Royle | Changes in the Falkland Islands since the conflict of 1982[END_REF][START_REF] Bingham | The decline of falkland islands penguins in the presence of a commercial fishing industry[END_REF]. The development of commercial fishing is considered a leading cause of the dramatic decline of the Magellanic and Southern Rockhopper penguin populations of the Falklands during the 1980s and 1990s [START_REF] Bingham | The decline of falkland islands penguins in the presence of a commercial fishing industry[END_REF].

Industrial fishing can impact penguin population trends in at least three ways. First, industrial fishing comes with undesirable side effects such as maritime pollution and the spread of debris. Second and most importantly, fishing vessels may harvest species that are an essential part of penguins' diet, leading to chick mortality due to lengthened foraging trips, higher rates of nest abandonment or forced diet change.

This mechanism probably accounts for the aforementioned mortality among Magellanic and Rockhopper penguins, as these two species feed on squids and fishes that constitute the main targets of the Falklands' fishing industries [START_REF] Bingham | The decline of falkland islands penguins in the presence of a commercial fishing industry[END_REF]. Finally, foraging adults or juveniles may get entangled in fishing nests and drown. We can safely rule out the two latter mechanisms. With regard to competition for sea resources, king penguins feed on myctophids (Le [START_REF] Maho | Undisturbed breeding penguins as indicators of changes in marine resources. Marine ecology progress series[END_REF][START_REF] Olsson | Seasonal effects of timing and reproduction in the king penguin: a unique breeding cycle[END_REF], which are not caught commercially [START_REF] Bingham | The decline of falkland islands penguins in the presence of a commercial fishing industry[END_REF]. With regard to accidental bycatch of king penguins, this seems to be an extremely rare phenomenon in the vicinity of the Falklands: as an order of magnitude, one fatality was reported for the 1995-2001 period, and another in 2009 [START_REF] Crawford | Tangled and drowned: a global review of penguin bycatch in fisheries[END_REF]. These numbers are relatively recent and may partly reflect technical measures taken to reduce the risk of bycatch; however, it is very unlikely that the Falklands' king penguins experienced mass mortality due to bycatch prior to the 1990s. Regarding the first mechanism, it is difficult to estimate to which extent maritime traffic impacted Falklands' king penguins, so we cannot completely exclude this possibility. We performed additional estimations including fish catches in the waters around the Falklands28 and found no significant effect of fisheries activities (results regarding the effect of the war remain unchanged; results are available upon request). However, these results must be interpreted with caution, since more declared catches can also be a sign of fish abundance (or more generally higher ocean productivity), which can also benefit king penguins.

Natural mortality causes

Disease outbreaks are a potential cause of mass mortality among king penguins [START_REF] Trathan | Pollution, habitat loss, fishing, and climate change as critical threats to penguins[END_REF], but we did not find reports of any epidemics affecting the Falklands' population during the investigation period.

Predators are also one of the leading factors affecting breeding failures [START_REF] Descamps | Impact of predation on king penguin aptenodytes patagonicus in crozet archipelago[END_REF] but in the absence of annual data on the population of common predators, we assume that the mortality rate due to predation remained constant throughout the investigation period. The only factor that would cast doubt on this assumption is a systematic growth of predators' population in the post-treatment period, due to, e.g., conservation efforts. However, such a trend would be captured by the inclusion of a control group (see Section 4.5).

C Diagnostic of M1

We present here a full diagnostic of our most predictive model, M1, which includes current SOI and current SST values in the narrowest foraging zone FZ1 (diagnostics of M2-5, which are very similar, are omitted here but available upon request).

Stationarity

We check the stationarity of the dependent variable and climatic variables using the Augmented Dickey-Fuller (ADF) and Phillips-Perron tests (PP). Results are reported in Table A2. Both tests are always passed at a maximum 10% level of significance, ensuring that the risk of spuriousness is minimal. To be conservative, we also estimate an ARIMAX(0, 1, 0) as a sensitivity test because ∆ln(brp t ) strongly passes . Results of the model in first differences are presented in Section 4.4. The trend option is used for ln brpt and SSTt/F Z1 because the time series grows over time. The lag structure was determined using Akaike's information criterion. The larger number of observations for climatic variables is due to the use of lags in the estimations.

Descriptive statistics

D Regression tables (main analysis and sensitivity tests)

This appendix presents all regression tables for M1-M5 for the main analysis and for sensitivity analysis. The main advantage of this sample is that it is relatively representative in terms of locations, since it includes colonies from the Indian Ocean and the Pacific Ocean besides the South Atlantic. To put it otherwise, it limits the risk that the construction of the synthetic is fully driven by some idiosyncratic local conditions.

Time series for the donor pool are based on counts of breeding pairs (the most frequent case), adults, chicks, or (more rarely) total population. Appendix F reports all data points with the corresponding source andwhenever possible-the census method. A few remarks are in order. First, there seems to be no systematic variation in the counting method over time, meaning that we can confidently exclude the possibility of artificial trend breaks due to changes in counting methods. Most of the variation is local (as some colonies were repeatedly censused by the same researcher team) and/or dependent on the colony size. Second, while all counting methods are susceptible to systematic underreporting [START_REF] Foley | Correcting for within-season demographic turnover to estimate the island-wide population of king penguins (aptenodytes patagonicus) on south georgia[END_REF], we do not know in which case the bias is the most severe. Third, the most important comparability issue arises from possible differences in chick mortality between locations: indeed, breeding success seems to be higher in the Falklands than in most other locations [START_REF] Pütz | Spatial and temporal variability in the foraging areas of breeding king penguins[END_REF][START_REF] Otley | Breeding patterns of king penguins on the falkland islands[END_REF]. Although this means that population trends of the treated case and the donor pool are not perfectly comparable, this also means that the synthetic control is likely to be an underestimate of the trajectory of the counterfactual Falklands if population data for the donors relies on chick counts.

We cannot use raw data as an outcome variable because population sizes are very heterogeneous among This colony is named "Grande Manchotière" in [START_REF] Weimerskirch | The breeding biology and population dynamics of king penguins Aptenodytes patagonica on the Crozet Islands[END_REF], and "Baie du Marin" in [START_REF] Delord | Long-term trends in the population size of king penguins at Crozet archipelago: environmental variability and density dependence?[END_REF].

f Counts were obtained from lateral photographs taken from promontories in January. Only birds incubating an egg were counted. The colony apparently did not exist until the 1966/67 breeding season and was possibly brought into existence following a shock occurring at a neighboring colony: by the end of the 1960s, the extension of a French research station reduced the surface of the Baie du Marin colony (a.k.a. Grande Manchotière) by about 30%. As a result, breeding king penguins of this colony (which is located in the bottom of a valley) started colonizing new nesting sites on the valley's slopes [START_REF] Weimerskirch | The breeding biology and population dynamics of king penguins Aptenodytes patagonica on the Crozet Islands[END_REF]. However, we cannot rule out the possibility that some individuals migrated to neighboring colonies (incl. Crique de la Chaloupe), and that the growth rate of these colonies was sustained by regular immigration over the whole investigation period.

Data source (continued)

h An island-wide census was conducted at Possession Island in 1962. Counts of breeding pairs for each colony were obtained from lateral photographs taken from promontories in January [START_REF] Weimerskirch | The breeding biology and population dynamics of king penguins Aptenodytes patagonica on the Crozet Islands[END_REF]. However, colonies at Île de l'Est and Île aux Cochons were not systematically counted. Estimates of population sizes for both islands were added to the Possession Island population to obtain an estimated total population size for Crozet archipelago. Notes:

i An island-wide census was conducted at Possession Island in 1985, during which counts of breeding pairs were obtained from lateral photographs taken from vantage points in early January [START_REF] Weimerskirch | The breeding biology and population dynamics of king penguins Aptenodytes patagonica on the Crozet Islands[END_REF]. The population size at Île aux Cochons was estimated using satellite remote sensing [START_REF] Guinet | Satellite remote sensing in monitoring change of seabirds: use of spot image in king penguin population increase at ile aux cochons, crozet archipelago[END_REF]. The numbers for Île de l'Est are rough estimates.

j
Estimates are rarely expressed in total number of birds. Considering that about 15% of adults are non-breeders, that one breeding pair equals two adults, and that the number of chicks is about 80% of the number of adults (see Appendix A), we estimate that the number of breeding pairs corresponds to roughly 31.7% of the total number of birds. We use this conversion rule for all counts of individuals.

k Birds of each colony were counted on the ground by researchers. The count was replicated once or twice. Ground counts were complemented by oblique ground and aerial photography. n Counts were made in January using aerial photographs (taken from a helicopter) or oblique photographs (taken from a vantage point). Population size was estimated on the basis of breeding density calculated on photographs and colony area.

o Chicks were counted in August (i.e., by the end of the austral winter) on the basis of photographs. Counts were made by piercing the image of each chick and counting the number of holes made on the reverse of the photograph.

p See footnote (o) for the counting method. [START_REF] Rounsevell | Growth rate and recovery of a king penguin, aptenodytes patagonicus, population after exploitation[END_REF] warn that the number of chicks might be slightly underrestimated (by about 5%) because bad weather forced chicks to gather in particularly dense creches during this year.

Figure 1 :

 1 Figure 1: King penguins' population in the Falkland Islands

Figure 2

 2 Figure 2 illustrates this negative slope change attributable to the Falklands war. The dashed lines represent counterfactual scenarios in the absence of war, which are clearly above the fitted values estimated in M1. The first scenario (black dashed line) is a projection of the log-abundance once the estimated effect of the war is removed. Cumulative effects between 1982 and 1997 are very important due to the magnitude of the coefficient β2 . In 1997, the model predicts that the population of the Volunteer Point colony would have been of 1,715 breeding pairs (95%CI = 539 -5,453), instead of 345. The gray dashed line in Figure 2 9 StataCorp. 2015. Stata Statistical Software: Release 14. College Station, TX: StataCorp LP

Figure 2 :

 2 Figure 2: Estimation of the log-abundance of king penguins at Volunteer Point, 1963-1997

Figure 3 :

 3 Figure 3: Comparison of the magnitude of the war's effect (95%CI) on the king penguins log-abundance trend in Volunteer Point using different models

Figure 4 :

 4 Figure 4: Simulations of the lower limit of the detrimental effect of Falklands War on king penguins

  control (SC), β 1 indicates the slope of the synthetic control before 1982, β 2 represents the difference between the Falklands and Synthetic Falklands regarding the initial level of log-abundance, β 3 is the difference in the slope before and after 1982 for the SC, β 4 is the difference in slope between the Falklands and Synthetic Falklands before 1982, and β 5 indicates the the difference between the Falklands and Synthetic Falklands after 1982 relative to the the difference before 1982 (close to the difference-in-differences of trends).

a

  The final count (rounded) is expressed in breeding pairs (O: original estimate; C: converted estimate; A: averaged; I: interpolated data). b Pistorius et al. (2012) also report counts of chicks in the years 1971 and 1972, but since it might lead to an underestimate of the number of breeding pairs, we use only adults counts. c Another estimate is available from Paleczny et al. (2015) for the year 1971, but they report an impossibly high number of adults (660), so we discarded this data point. d The census method is unknown. Data source (continued)

Figure A1 :

 A1 Figure A1: Three foraging zones for king penguins from Volunteer Point

Figure A2 :

 A2 Figure A2: Residuals of model 1 (M1) over years

F

  Data and sources (donor pool)

l

  Censuses were made using aerial photography. These are not individual counts but population estimates based on population density in each area and the total surface of the colony.m Census method unknown.

Table 1 :

 1 War months and king penguins' breeding cycle

		War months
		April	May	June
	Early breeders Chick feeding/winter foraging trips	Winter foraging trips	Winter foraging trips
	Late breeders	Brooding/chick feeding	Chick feeding/winter foraging trips Winter foraging trips
	Chicks	Ashore	Ashore	Ashore
	Note: adapted from Stonehouse (1956)
	4 Empirical analysis
	In order to assess the effect of the Falklands War on king penguins' population dynamics, we use inter-
	rupted time series analysis. The outcome of interest is the size of the population (expressed in breeding pairs)
	before and after 1982. The investigation period starts in 1963 and ends in 1997: 1963 corresponds to the
	onset of the development of the king penguins colony at Volunteer Point (with the first successful breeding
	occurring in 1965). The choice of 1997 as an end point is motivated by the fact that drilling for oil started in
	1998 around the Falklands, resulting in three oil spills that killed hundreds of penguins (Garcia-Borboroglu

Table 2 :

 2 Results of linear regressions on the log-abundance of kings penguins from Volunteer Point

			Climatic variables		War variable	Goodness of fit
	Global | Lags Local	| Lags	war t	(sdt. err.)	∆AICc
	M1 SOI t	-	SST t /F Z1 -	-0.106 * * * (0.038)	0.000
	M2 SOI t	-	SST t /F Z3	-	-0.103 * *	(0.038)	0.28
	M3 SOI t	-	SST t /F Z2	-	-0.104 * * *	(0.038)	0.45
	M4 SOI t	-	SST t /F Z1	L1-4	-0.156 * * *	(0.039)	0.87
	M5 SOI t	-	SST t /F Z1	L1	-0.114 * * *	(0.039)	1.38

Table 3 :

 3 Weights used to build the synthetic Falklands

	Colonies	Weights Colonies	Weights
	Archway Bay	0	Petite Manchotière	0
	Baie du Marin	0.025	Sealer's Beach	0
	Crique de la Chaloupe	0	Spit Bay North	0.22
	Fortuna Bay	0	Spit Bay South	0.755
	Gold Harbour	0	Saint Andrew's Bay	0
	Jardin Japonais	0	Vashel Moraine	0
	Lusitania Bay	0

Table 4 :

 4 Results of the controlled interrupted time series with a synthetic control ) is the model with a synthetic control built on the basis of the full donor pool. In (2) we exclude colonies located in South Georgia from the donor pool. In (3) we keep the 7 colonies with the highest number of real data points during the pre-treatment period. In model (4) we consider a synthetic control built at the island level. All details regarding the construction of the synthetic controls are provided in Appendix E Column (1) in Table4reports values of estimated coefficients. To ensure the comparability of the Falklands and Synthetic Falklands, an important prerequisite is the absence of notable differences between them prior to the war. Considering the non-significant values of coefficients associated with real it and real_T it , the real and the synthetic Falklands are comparable before 1982, both in terms of initial values of log-abundance and growth rate. The coefficient capturing the impact of war -β5 -equals -0.099 (95%CI=-0.2 -0.002) 14

		(1)	(2)	(3)	(4)
	T it	0.198*** 0.198***	0.2***	0.189***
		(0.016)	(0.016)	(0.016)	(0.007)
	real it	-0.109	-0.109	-0.076	-0.015
		(0.435)	(0.435)	(0.438)	(0.409)
	post82 it	-0.0005	-0.0005	-0.004	0.001
		(0.036)	(0.036)	(0.035)	(0.013)
	real_T it	-0.009	-0.009	-0.011	0.0002
		(0.03)	(0.03)	(0.03)	(0.026)
	war it	-0.099*	-0.099*	-0.095*	-0.101**
		(0.05)	(0.05)	(0.05)	(0.038)
	Constant	1.101*** 1.101*** 1.067*** 1.001***
		(0.176)	(0.176)	(0.182)	(0.097)
	Level	Colonies Colonies Colonies	Islands
	# of donors (in the SC)	13 (3)	10 (3)	7 (2)	6 (1)
	Observations	70	70	70	70
	Newey-West standard errors in parentheses (maximum lag = 2), with *** p<0.01,
	** p<0.05, * p<0.1. (1

Table 3 )

 3 . Coefficients are thus identical in Columns (1) and (2). Second, data gathered at the colony level are very scarce, notably prior to the Falklands war (see Appendix F): as a consequence, the fitting procedure may be affected by long interpolations over the period 1963-1981. One risk is that a colony enters the synthetic control's composition by error, which can cast doubts on the quality of the counterfactual. In order to address this issue, we only retain donors with the highest number of real data points in the pre-treatment period and thus exclude Archway Bay, Baie du Marin, Gold Harbour, Jardin Japonais, Petite Manchotière and Sealer's

Beach from the donor pool (see Appendix E for more details). Coefficients are reported in Column (3) and are very similar.

  , "[a]synchronously breeding species present unique challenges to population monitoring, because there is no single point in the season where all potential breeders (or offspring) are available to be counted" (pp. 251-252). For example, counts of adults made in January miss all the late breeders that start breeding at a later point. Counts based on chicks are also problematic because of either mortality (for late counts) or delayed births (for early counts). According to[START_REF] Foley | Correcting for within-season demographic turnover to estimate the island-wide population of king penguins (aptenodytes patagonicus) on south georgia[END_REF], counting error can be as high as 50%. As a consequence, interpreting individual data points in the time series is risky at best, and only the analysis of the general trend can provide reliable information.

Counting errors can explain abnormal growth rates observed in some periods of the time series, even with harmonized counting methods. For example, Volunteer Point's population more than doubled between 2005 and 2006, which is biologically impossible: recall that a breeding pair can only produce a chick (that is, roughly speaking, half a breeding pair) per breeding season, i.e., the highest possible growth rate from one year to another is 50%. 22 However, once counting errors are taken into account, it is possible to observe yearly variations such as (brp t /brp t-1 ) ≤ 1.5(1 + m) ≤ 2.25, brp being the number of breeding pairs and m the 50% measurement error. Considering this rule, there are no outliers in the Falklands' time series, except for one impossibly high estimate in 1971 which we discarded.

Table A1 :

 A1 Foraging zones' main characteristics

		Maximal distance Size of the rea Geographic coordinates
	FZ1	225 km	101,250km²	(50-52.9°S , 55.6-60.2°W)
	FZ2	545 km	594,050km²	(48-54.9°S , 52.7-63.9°W)
	FZ3	722 km	1,004,568km² (46.9-56.1°S , 51.1-66.1°W)

Table A2 :

 A2 Unit root tests for time series used in the paper.

		Z-statistics	MacKinnon p-values
		ADF	PP	ADF	PP
	ln brp t	-3.370 -3.235	0.0555	0.0777
	SOI t	-4.633 -5.102	0.0001	0.0000
	SST t /F Z1 -3.305 -7.708	0.0655	0.0000
	∆ln(brp t )	-4.786 -4.741	0.0001	0.0001

Table A3 :

 A3 Descriptive statistics of data used in the interrupted time series analysis

		N mean	sd	min	max
	ln brp t	35 3.872 1.595	0	5.971
	T t	35	17	10.25	0	34
	war t	35 3.429 4.937	0	15
	SOI t	46 -0.786 7.118 -13.07 13.60
	SST t /F Z1 46 6.891 0.189 6.541 7.435
	SST t /F Z2 46 7.100 0.169 6.767 7.586
	SST t /F Z3 46 7.128 0.169 6.820 7.639

Table A4

 A4

			: M1-M5, main analysis
		M1	M2	M3	M4	M5
	T t	0.195***	0.194***	0.194***	0.242***	0.202***
		(0.0274)	(0.0272)	(0.0273)	(0.0304)	(0.0287)
	war t	-0.106*** -0.103** -0.104*** -0.156*** -0.114***
		(0.0377)	(0.0376)	(0.0378)	(0.0389)	(0.0391)
	SOI t	-0.000359 -0.00267	-0.00209	-0.0101	0.00130
		(0.00691) (0.00678) (0.00688) (0.00977) (0.00707)
	SST t /F Z1	-0.438*			-0.734**	-0.541**
		(0.238)			(0.277)	(0.222)
	SST t-1 /F Z1				-1.164*	-0.458*
					(0.568)	(0.249)
	SST t-2 /F Z1				-0.856
					(0.543)
	SST t-3 /F Z1				-0.967
					(0.590)
	SST t-4 /F Z1				-1.112**
					(0.520)
	SST t /F Z2			-0.404
				(0.255)
	SST t /F Z3		-0.451*
			(0.252)
	Constant	3.940**	4.137**	3.803**	33.62**	7.725***
		(1.498)	(1.663)	(1.665)	(14.64)	(2.190)
	Observations	35	35	35	35	35
	R-squared	0.935	0.934	0.934	0.954	0.938
	AICc	47.44	47.72	47.89	48.31	48.82
	Newey-West standard errors in parentheses (maximum lag=2), with *** p<0.01, **
	p<0.05, * p<0.1. M1-M5 are the most predictive models described in Table 2.

Table A5

 A5

		: M1-M5 over the period 1971-1997 (sensitivity test)
		M1	M2	M3	M4	M5
	T t	0.229***	0.227***	0.227***	0.232***	0.236***
		(0.0134)	(0.0147)	(0.0145)	(0.0160)	(0.0143)
	war t	-0.142*** -0.138*** -0.139*** -0.145*** -0.149***
		(0.0232)	(0.0252)	(0.0246)	(0.0268)	(0.0246)
	SOI t	0.00362	0.000555	0.00133	0.00349	0.00534
		(0.00637) (0.00647) (0.00663)	(0.0106)	(0.00710)
	SST t /F Z1	-0.560**			-0.511** -0.645***
		(0.222)			(0.200)	(0.193)
	SST t-1 /F Z1				-0.313*	-0.348**
					(0.164)	(0.123)
	SST t-2 /F Z1				0.119
					(0.209)
	SST t-3 /F Z1				0.0992
					(0.190)
	SST t-4 /F Z1				-0.153
					(0.270)
	SST t /F Z2			-0.542**
				(0.240)
	SST t /F Z3		-0.571**
			(0.239)
	Constant	4.185**	4.406**	4.186**	5.512	7.085***
		(1.636)	(1.835)	(1.831)	(3.801)	(1.676)
	Observations	27	27	27	27	27
	R-squared	0.970	0.968	0.967	0.975	0.973
	AICc	1.964	3.810	4.181	12.85	1.798
	Newey-West standard errors in parentheses (maximum lag=2), with *** p<0.01, **
	p<0.05, * p<0.1. M1-M5 are the most predictive models described in Table 2.

Table A6

 A6

		: M1-M5 without 1982 (sensitivity test)
		M1	M2	M3	M4	M5
	T t	0.191***	0.191***	0.190***	0.237***	0.198***
		(0.0275)	(0.0273)	(0.0274)	(0.0304)	(0.0288)
	war t	-0.0947** -0.0921** -0.0929** -0.143*** -0.102**
		(0.0359)	(0.0357)	(0.0359)	(0.0372)	(0.0371)
	SOI t	0.00371	0.00120	0.00187	-0.00464	0.00526
		(0.00546) (0.00544) (0.00547) (0.00910) (0.00581)
	SST t /F Z1	-0.471*			-0.808*** -0.568**
		(0.242)			(0.276)	(0.229)
	SST t-1 /F Z1				-1.098*	-0.430*
					(0.549)	(0.241)
	SST t-2 /F Z1				-0.799
					(0.548)
	SST t-3 /F Z1				-0.997
					(0.603)
	SST t-4 /F Z1				-0.979*
					(0.531)
	SST t /F Z2			-0.468*
				(0.254)
	SST t /F Z3		-0.517**
			(0.247)
	Constant	4.187***	4.623***	4.271**	32.61**	7.741***
		(1.516)	(1.613)	(1.645)	(14.70)	(2.228)
	Observations	35	35	35	35	35
	R-squared	0.938	0.938	0.938	0.956	0.941
	AICc	45	45.16	45.37	46.45	46.47
	Newey-West standard errors in parentheses (maximum lag=2), with *** p<0.01, **
	p<0.05, * p<0.1. M1-M5 are the most predictive models described in Table 2.

Table A7 :

 A7 M1-M5 without climatic variables (sensitivity test)

		(1)
	T t	0.190***
		(0.0253)
	war t	-0.0996***
		(0.0359)
	Constant	0.992**
		(0.398)
	Observations	35
	R-squared	0.932
	AICc	43.59
	Newey-West standard errors in
	parentheses (maximum lag=2),
	with *** p<0.01, ** p<0.05, *
	p<0.1.

Table A8 :

 A8 First difference M1-M5 (sensitivity test)

		M1	M2	M3	M4	M5
	∆war t	-0.232**	-0.232**	-0.232**	-0.230**	-0.231**
		(0.106)	(0.106)	(0.107)	(0.104)	(0.104)
	∆SOI t	0.00497	0.00377	0.00404	0.00347	0.00580
		(0.00564) (0.00566) (0.00571) (0.00585) (0.00554)
	∆SST t /F Z1	-0.381**			-0.596**	-0.539**
		(0.180)			(0.271)	(0.204)
	∆SST t-1 /F Z1				-0.658*	-0.313
					(0.353)	(0.203)
	∆SST t-2 /F Z1				-0.344
					(0.353)
	∆SST t-3 /F Z1				-0.420
					(0.360)
	∆SST t-4 /F Z1				-0.521*
					(0.288)
	∆SST t /F Z2			-0.400*
				(0.210)
	∆SST t /F Z3		-0.458**
			(0.223)
	Constant	0.280*** 0.281*** 0.280*** 0.293*** 0.282***
		(0.0705)	(0.0708)	(0.0714)	(0.0692)	(0.0689)
	Observations	34	34	34	34	34
	R-squared	0.242	0.237	0.223	0.378	0.299
	AICc	24	24.25	24.83	30.66	24.30
	Standard errors in parentheses, with *** p<0.01, ** p<0.05, * p<0.1. M1-M5 are the
	most predictive models described in Table 2.

Table A9 :

 A9 Donors at the colony level , we could not find suitable data for the Kerguelen Islands at the colony level). Note also that some locations are overrepresented among donors (e.g., the Crozet Islands). Furthermore, parts of some archipelagos are absent from the donor pool: for example, we only have colonies for Marion Island and Possession Island, and none for the remaining parts of the Prince Edward and the Crozet Islands (that is, Prince Edward Island, and Île de l'Est and Île aux Cochons, respectively).

	Colony	Island	Archipelago	Location	Country
	Archway Bay	Marion Island	Prince Edward Islands Indian Ocean	South Africa
	Baie du Marin	Possession Island Crozet Islands	Indian Ocean	France
	Crique de la Chaloupe Possession Island Crozet Islands	Indian Ocean	France
	Fortuna Bay	South Georgia	-	Atlantic Ocean United Kingdom
	Gold Harbour	South Georgia	-	Atlantic Ocean United Kingdom
	Jardin Japonais	Possession Island Crozet Islands	Indian Ocean	France
	Lusitania Bay	Macquarie Island -	Pacific Ocean	Australia
	Petite Manchotière	Possession Island Crozet Islands	Indian Ocean	France
	Sealers' Beach	Marion Island	Prince Edward Islands Indian Ocean	South Africa
	Spit Bay North	Heard Island	Heard & McDonald	Indian Ocean	Australia
	Spit Bay South	Heard Island	Heard & McDonald	Indian Ocean	Australia
	St. Andrew's Bay	South Georgia	-	Atlantic Ocean United Kingdom
	Vahsel Moraine	Heard Island	Heard & McDonald	Indian Ocean	Australia
	(for example

  The estimate relies on field counts, direct counts from photographs, and population density estimates assuming a ratio of eight adults per square yard.Chicks were counted in September/October on the field, using a tally counter. According to[START_REF] Crawford | Recent trends in numbers of four species of penguins at the prince edward islands[END_REF], overwinter survival of chicks at Marion Island is relatively high, but the conversion of chicks numbers into numbers of breeding pairs might result in a slight underestimate.

	1,606 (C)	45,000 (O)	43,400 (O)	32,000 (O)
	Yes c	Yes f	Yes f	Yes f
	Crawford et al. (2003)	Weimerskirch et al. (1992)	Weimerskirch et al. (1992)	Weimerskirch et al. (1992)
		45,000 breeding pairs	43,400 breeding pairs	32,000 breeding pairs
		1962	1965	1981
		Baie du Marin e	Baie du Marin	Baie du Marin	Notes:

a The final count (rounded) is expressed in breeding pairs (O: original estimate; C: converted estimate; A: averaged). b c d Abnormally low count.

e

The last significant battle fought in the vicinity of the Falkland Islands occurred in 1914, as a German fleet unsuccessfully attempted to destroy the British naval base on the Falklands[START_REF] Chehabi | Self-determination, territorial integrity, and the Falkland Islands[END_REF].

Oil spills in the marine environment amounted to the equivalent of 6-8 millions of barrels[START_REF] Price | Impact of the 1991 Gulf War on the coastal environment and ecosystems: current status and future prospects[END_REF].

In March 1982, a team of Argentine metal workers illegally landed on South Georgia and raised the Argentine flag. It is unclear whether the action was planned by the junta.

More generally, the longer the investigation period, the more vulnerable the analysis to (observed or unobserved) confounding events-for example, a toxic algal bloom occurred in the Falklands in 2002[START_REF] Trathan | Pollution, habitat loss, fishing, and climate change as critical threats to penguins[END_REF].

For example, Le[START_REF] Bohec | King penguin population threatened by Southern Ocean warming[END_REF] find that adult survival rates are predicted by sea surface temperatures in t -2. This result can be explained by (a) a time lag between SST warming and repercussions on ocean productivity; and (b) a delayed response of adults to increased energy expenditures during poor years.

The first adults that recolonized the Falklands probably originated from South Georgia. Massive migrations between these two locations are very unlikely, since South Georgia lies about 1,550 km away from the Falklands and king penguins are generally philopatric, but considering that the Falklands' population is a very small one at the beginning of the treatment, even sporadic migrations may have affected the overall trend.

Technical details regarding the Synthetic Control Method are provided elsewhere[START_REF] Abadie | Synthetic control methods for comparative case studies: Estimating the effect of California's tobacco control program[END_REF][START_REF] Abadie | Using synthetic controls:feasibility, data requirements, and methodological aspects[END_REF].

The corresponding p-value is 0.054.

An example of such a shock can be found at Crozet archipelago: by the end of the 1960s, the extension of a French research station reduced the surface of the Baie du Marin colony by about 30%[START_REF] Delord | Long-term trends in the population size of king penguins at Crozet archipelago: environmental variability and density dependence?[END_REF]. Neighboring colony Crique de la Chaloupe, which did not exist until 1965, was possibly brought into existence by this event, and we cannot exclude the possibility that the growth rate of this colony was sustained by regular immigration over the whole investigation period.

For example, the distance between South Georgia and Macquarie Island is about 7,800 km. The closest locations, Heard Island and the Kerguelen Islands, lie more than 500 km away from each other.

The precise flight trajectory and the exact distance from the colony are unknown.

[START_REF] Cooper | Airdrops and king penguins: a potential conservation problem at sub-antarctic marion island[END_REF] report an incident of mass mortality at the Lusitania Bay colony on Macquarie Island, which was possibly the result of a flypast that caused the birds to panic and die by asphyxiation. They suggest that king penguins may perceive aircraft as aerial predators.

According to Ortega (2012), physical damages to ears are rarely permanent among birds, but the recovery time varies from one species to another.

These methods are typically used for large colonies such as the ones at Crozet archipelago[START_REF] Delord | Long-term trends in the population size of king penguins at Crozet archipelago: environmental variability and density dependence?[END_REF].

This makes sense because a) a breeding pair is defined by the fact that it produces a chick, and b) a breeding pair never produces more than one chick per year.

King penguins from other colonies typically travel South, but Falklands' king penguins have been found to travel North and East as well[START_REF] Pütz | Spatial and temporal variability in the foraging areas of breeding king penguins[END_REF][START_REF] Baylis | Winter foraging site fidelity of king penguins breeding at the Falkland Islands[END_REF]. Thus, the foraging areas we define encompasses every direction around the Falklands.

The three aforementioned studies by[START_REF] Bohec | King penguin population threatened by Southern Ocean warming[END_REF],[START_REF] Saraux | Effects of individual pre-fledging traits and environmental conditions on return patterns in juvenile king penguins[END_REF] and[START_REF] Bost | Large-scale climatic anomalies affect marine predator foraging behaviour and demography[END_REF] use data from the Crozet islands, which means that, although we attempt to replicate their main specifications including lag structures as closely as possible, we define foraging areas in a different way.

If food abundance in this narrow foraging area decreases due to poor climatic conditions, adults feed their chicks either less abundantly (if they remain in this area) or less frequently (if they lengthen their foraging trips to find food). In both cases, chicks face a higher mortality risk.

There seems to be variability in the length of foraging trips depending on the year:[START_REF] Pütz | Spatial and temporal variability in the foraging areas of breeding king penguins[END_REF] reports larger values than[START_REF] Baylis | Winter foraging site fidelity of king penguins breeding at the Falkland Islands[END_REF].

This does not mean that a king penguin is never allowed to forage farther than 700 km-indeed, both[START_REF] Baylis | Winter foraging site fidelity of king penguins breeding at the Falkland Islands[END_REF] and[START_REF] Pütz | Spatial and temporal variability in the foraging areas of breeding king penguins[END_REF] report longer trips in their studies on the Falklands, which is why we chose to use the median of maximal distance-but that chicks' survival would be jeopardized if foraging trips systematically exceeded this distance.

We used the yearly log of tones of declared fishes catches in the water around the Falklands (ln(catch)). Data are collected by the Sea Around Us project[START_REF] Zeller | Reconstructing marine fisheries catch data. Catch reconstruction: concepts, methods and data sources[END_REF].

Note that the migration issue is only partly addressed, since we could not find data for the Prince Edward (close to Marion Island) and the McDonald Islands (close to Heard Island).

Residuals analysis

A visual inspection of Figure A2 suggests the existence of a pattern in the residuals distribution. We first perform the Breush-Pagan/Cook-Weisberg test, which indicates the presence of heteroskedasticity (p-value= 0.000). Moreover, the examination of the table of autocorrelations and partial autocorrelations also indicates issues regarding residual auto-correlation. Consequently, we systematically report the Newey-West standard errors with a maximal lag of 2. This number was defined using the following rule-of-thumb maximum lag= N 1/4 , where N = 35 is the number of observations. Moreover, according to Shapiro-Wilk and Shapiro-Francia normality tests, residuals are not normally distributed (p-values are respectively 0.081 and 0.035).

Possible reasons are: i) measurement errors in the dependent variable, or ii) omission of crucial independent variables. In our sensitivity analysis, we deal with measurement error by generating 6,080 alternative time series, and obtain results that are very close to those of M1. The omission of predictor variables is also partially addressed in our sensitivity analyses: we estimate 243 different combinations of lags of SST and SOI, and define foraging areas in three alternative ways. Again, results are very similar to those of the main estimates. We also rerun M1 while controlling for the log of tones of declared fish catches in the waters of Falkland Islands (ln catch), as an indicator of human activities. Data are collected by the Sea Around Us project following the method developed by [START_REF] Zeller | Reconstructing marine fisheries catch data. Catch reconstruction: concepts, methods and data sources[END_REF]. In this estimation, the coefficient associated with war t equals -0.086 (95%CI=-0.143 --0.028), which is a little larger than in our main estimate but remains of the same order of 95%CI. Finally, we address the non-normality issue by shortening the period of investigation in order to eliminate extreme values of residuals that are concentrated at the very beginning of our study (i.e. 1963-1970). We still find negative and significant effects of the war (-0.142 with 95%CI=-0.190 --0.093). In light of these results, we can reasonably assume that the non-normality issue does not cast doubts on the main findings. We also check the variance inflation factors to ensure the absence of multicolinearity.

Finally, we conduct visual inspection of residuals plots against predictors, and do not identify any systematic pattern.

E Controlled interrupted time series with a synthetic control

We follow Linden (2018) by using a synthetic control in a CITS analysis. This appendix briefly introduces the synthetic control method (SCM) and provides more details on the CITS analysis included in the paper.

Brief description of the synthetic control method

In order to find a suitable control, we use the SCM developed by [START_REF] Abadie | Synthetic control methods for comparative case studies: Estimating the effect of California's tobacco control program[END_REF]. The SCM examines how a treatment (here, the Falklands War) affects an outcome variable (the log-abundance of kink penguins). The general idea is to build a synthetic counterfactual unit ('Synthetic Falklands') using a weighted combination of observed control units which were not exposed to the treatment.

We follow here Klößner et al. (2018, p. 4)'s presentation. The SCM requires two types of information: the outcome variable -labelled Y -and the k predictors, X. We denote by the subscript 1 the treated unit, and by 0 the control units. We build a (k × J)-matrix X pre 0 and a k-dimensional vector X pre 1 including the averages of the k predictors in the pre-treatment period (1963)(1964)(1965)(1966)(1967)(1968)(1969)(1970)(1971)(1972)(1973)(1974)(1975)(1976)(1977)(1978)(1979)(1980)(1981) respectively for control units and the treated unit. We also build a (L × J)-matrix Y pre 0 and a L-dimensional vector Y pre 1 reporting the value of the outcome variable for the J control units and the treated one, where L is the duration of the pre-treatment period. In our main application, we have a pool of 13 control units (J = 13), the pre-treatment lasts 18 years (L = 18) which also corresponds to the number of predictors (k = 18).

For given predictor weights V = (v 1 , ..., v k ), the donor weights W * (V ) is the solution of the following program:

where 1 is a J-dimensional vector of ones. In the SCM, optimal predictor weights, V * , are the solution of:

The SCM offers a counterfactual Ŷ post

Finally, the effect of the treatment, α is therefore given by: [START_REF] Abadie | Synthetic control methods for comparative case studies: Estimating the effect of California's tobacco control program[END_REF] show that if the pre-treatment period is sufficiently long, 29 then the difference between the treated unit and its synthetic is an unbiased estimation of the effect of the treatment on the outcome variable.

Donor pool

King penguins have a circumpolar distribution and consistently breed on several sub-Antarctic islands between latitude 45°south and 55°south. During our investigation period, 30 colonies could be found in the Falklands and South Georgia in the South Atlantic Ocean, Prince Edward, Crozet, Kerguelen and Heard islands in the Indian Ocean, and Macquarie Island in the Pacific Ocean [START_REF] Bost | King penguin[END_REF].

Volunteer Point is the only king penguin colony of the Falkland Islands, meaning that it can be meaningfully compared to either total (island-wide) populations or to other colonies: indeed, most of the aforementioned islands host several king penguin colonies. 31 Both strategies have their own strengths and drawbacks.

At the colony level, data is more abundant (albeit relatively scarce) and probably more reliable. The main inconvenient of this level of analysis is that we cannot exclude the possibility that migrations take place between colonies within each island or archipelago, thus artificially inflating the growth rate of some colonies among our donors (something we cannot check because we do not have exhaustive data on every colony of each island). This argues in favor of an analysis at the island level. However, even barring data quality issues, such an analysis would not allow us to capture population dynamics that are specific to each colony and do not necessarily reflect at the whole population level. Indeed, the literature shows that there can be significant differences in the growth rate of colonies that are situated on the same island-e.g., Possession

Island [START_REF] Weimerskirch | The breeding biology and population dynamics of king penguins Aptenodytes patagonica on the Crozet Islands[END_REF][START_REF] Delord | Long-term trends in the population size of king penguins at Crozet archipelago: environmental variability and density dependence?[END_REF] or Heard Island [START_REF] Budd | Changes in heard island glaciers, king penguins and fur seals since 1947[END_REF]. 32

We thus pursue both strategies: we begin with an analysis at the colony level, which we then complement with an analysis at the island/archipelago level to probe the robustness of the findings. We start at the colony level and gather population data for thirteen donors, which are listed in Table A9.

This sample is by no means exhaustive with regard to the number of existing colonies, or even islands 29 Costalli et al. (2017, p. 83) use the following criteria "[f]or wars lasting less than ten years, we require a pre-treatment period of at least ten years". In the present work, we have a 19-year pre-treatment period for a three-month war.

30 Tierra del Fuego's small colony exists since the 2010s, and first breeding events have recently been reported at the South Shetland Islands [START_REF] Juáres | Breeding events of king penguin at the south shetland islands: Has it come to stay?[END_REF]. These locations are not included since they did not exist at all during the investigation period.

31 A partial exception is Macquarie Island, in which only one colony-Lusitania Bay-existed at the beginning of the pretreatment period.

32 To take a hypothetical example, consider a colony in which local conditions deteriorate for unknown reasons. If this colony is situated on an island in which several other colonies exist, individuals have the possibility of migrating, which means that the colony's population would decrease while the whole island population would remain stable. If, on the other hand, there is only one colony on the island-as in the case of the Falklands-the whole island population would decrease, considering that king penguins are often unable to breed successfully without a large colony. Thus, from a certain point of view, the possibility of migration argues in favor of an analysis at the colony level. locations: for example, at the beginning of our period of investigation, there were 45,000 breeding pairs at Baie du Marin. We thus re-scaled populations with the following transformation:

where brp t is the number of breeding pairs in period t. Re-scaled populations are displayed in Table A3. The synthtetic control

We use the Stata package SYNTH with the "nested" option and all pre-war outcomes included as predictors. Weights of Synthetic Falklands are reported in Table 3 and a visual comparison between Falklands and its synthetic is provided in Figure A4. The fit is reasonably good for the period 1963-1982 (RM SP E = 0.2487)

and the two curves clearly split up after the war. In order to be sure that this result is not entirely driven by one particular donor, we run a leave-one-out test. This sensitivity test consists in excluding one of the thirteen donors and running the exact same procedure with the twelve donors left. Figure A5 shows that the synthetic control obtained in the main procedure (black dashed line) is very similar to to leave-one-out estimates (gray solid lines).

CITS analysis with the synthetic control

Once we obtain the synthetic control, we need to make it comparable with our 'real' time series of breeding pairs log-abundance. Concretely, we multiply the synthetic control time series by the average number of breeding pair in Volunteer Point during the pre-treatment period (22.972). We thus obtain a time series corresponding to the counterfactual number of breeding pairs between 1962 and 1997. Finally, we compute the log-abundance of the synthetic control. Figure A6 displays the two curves. These two curves represent the time series which are compared in the CITS analysis (see Table 4). A quick visual inspection confirms the existence of a similar trend during the pre-treatment and a sharp difference after 1982.

Figure A6: Log-abundance : Falklands Vs Synthetic Falklands

Other sensitivity analyses

As mentioned in Section 4.5, several potential issues may cast some doubt on the validity of the CITS.

First, one may suspect that colonies located in South Georgia are partially affected by the Falklands War. We thus exclude Fortuna Bay, Gold Harbour and Saint Andrew's Bay from the donor pool. This does not have any impact on our results because these three colonies were not part of the synthetic control. Second, the number of observations in the time series is sometimes very small, especially during the pre-treatment period (see Appendix F). In particular, one risk is that a colony may artificially enter the synthetic control due to the long interpolations. In order to address this concern, we compute a new synthetic control with a donor pool made of the 50% colonies having the most available observations. Concretely, we exclude Archway Bay, Baie du Marin, Gold Harbour, Jardin Japonais, Petite Manchotière and Sealer's Beach (6 out of 13 donors).

New weights are reported in Table A10. Because the two main contributing donors Spit Bay North and Spit Bay South belong to the set of the relatively better documented colonies, results are very close to those exhibited in Table 3. We then perform the same procedure as above to obtain values of the model (3) in Table 4. The last test addresses the aforementioned issue of migrations within the donor pool. In order to mitigate that risk, we gathered aggregate data at the island level and obtain a smaller pool of six donors: Crozet Islands, Heard Island, Kerguelen Island, Macquarie Island, Marion Island, and South Georgia. 33 Re-scaled populations at the island level are displayed in Table A7. Consequently, the synthetic control used in the CITS presented in Table 4 -model (3) -is a direct comparison between the log-abundance of king penguins in the Falkland Islands and a re-scaled measure of the logabundance of those located in Heard Island (the re-scaling procedure is the same as above). Results are very similar to those obtained at the colony level with the full donor pool. Notes:

q Chicks were counted in August (i.e., by the end of the austral winter) on the basis of photographs taken in the colony of Lusitania Bay (which was the only king penguin colony at Macquarie Island at the time). Counts were made by piercing the image of each chick and counting the number of holes made on the reverse of the photograph.

r For census methods, see note (q) above. [START_REF] Rounsevell | Growth rate and recovery of a king penguin, aptenodytes patagonicus, population after exploitation[END_REF] warn that the number of chicks might be slightly underrestimated (by about 5%) because bad weather forced chicks to gather in particularly dense creches.

s

We added 10 breeding pairs to the count made in Lusitania Bay [START_REF] Rounsevell | Growth rate and recovery of a king penguin, aptenodytes patagonicus, population after exploitation[END_REF] in order to take into account the recent development of a second king penguin colony in Sandy Bay. Lusitania Bay was the only colony on Macquarie Island until 1975. In 1976, there were about 10 breeding pairs in Sandy Bay according to [START_REF] Wienecke | Foraging areas of king penguins from macquarie island in relation to a marine protected area[END_REF].

t This is a sum of the number of breeding pairs in Sandy Bay and Lusitania Bay (80 and 46,595, respectively). There was no other king penguin colony at Macquarie Island at this point.

u

Censuses were conducted during January 1974-April 1975and April 1976-May 1977. All breeding birds were counted on the ground. Counts in large colonies were then checked using aerial photographs taken from a helicopter. Notes:

Data source (continued)

w Census method unknown.

x

The date is an approximation (counts were made during the 1960s but at different points of time).

y This is a projection of the number of breeding pairs based on counts of chicks and eggs. Chicks and eggs were counted on the ground during field expeditions to Heard Island [START_REF] Budd | Further population growth in the heard island king penguins[END_REF][START_REF] Budd | Status of the heard island king penguins in 1971[END_REF][START_REF] Gales | Recovery of the king penguin, aptenodytes-patagonicus, population on heard island[END_REF].

Data source (continued) Notes:

z Spit Bay South is called Doppler Hill in [START_REF] Moore | Seasonal change in foraging areas and dive depths of breeding king penguins at heard island[END_REF]. Since the Doppler Hill colony is never mentioned in earlier reports [START_REF] Gales | Recovery of the king penguin, aptenodytes-patagonicus, population on heard island[END_REF][START_REF] Budd | Changes in heard island glaciers, king penguins and fur seals since 1947[END_REF] in spite of its size and is situated at the same place as Spit Bay South, we assume that this is the same colony. Likewise, "Spit Bay North" becomes "Spit Bay" in [START_REF] Moore | Seasonal change in foraging areas and dive depths of breeding king penguins at heard island[END_REF].

CEE-M Working Papers