# Inter-population variation in demographic responses to environmental changes: insights from four seabird populations

Camille Schatz, Karine Delord, Dominique Joubert, Samuel Peroteau, Ruijiao Sun, Christophe Barbraud

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Camille Schatz, Karine Delord, Dominique Joubert, Samuel Peroteau, Ruijiao Sun, et al. Inter-population variation in demographic responses to environmental changes: insights from four seabird populations. Oikos, In press, 10.1002/oik.11364 hal-05223587

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OIKOS

Research article

# Inter-population variation in demographic responses to environmental changes: insights from four seabird populations

Camille Schatz ID, ✉1, Karine Delord ID1, Dominique Joubert1, Samuel Peroteau ID1, Ruijiao Sun ID2 and Christophe Barbraud ID1

Correspondence: Camille Schatz (camille.schatz98@gmail.com)

Oikos

2025: e11364

doi: 10.1002/oik.11364

Subject Editor: Lucas Medeiros

Editor-in-Chief:

Paulo R. Guimaraes

Accepted 10 July 2025

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The variability in responses to environmental change between populations remains poorly quantified and understood. This shortcoming has major consequences, as variation and flexibility of life-history traits between populations contribute to the estimation of the adaptive potential, which is necessary for predicting the responses of interconnected populations to environmental change. Here, we use 20–50 years of capture-mark-recapture data from four populations of two closely related species in the southern Indian Ocean, the sooty albatross *Phoebetria fusca* and the light-mantled sooty albatross *Phoebetria palpebrata*, to analyze their demographic responses to environmental changes across an oceanic basin scale. We assessed the role of climate and oceanographic conditions as bottom-up forcings and fisheries bycatch as a top-down forcing on survival and three reproductive parameters in each population. We found expected demographic responses: negative effects of fishing effort on survival and breeding success, positive effects of wind velocity on breeding success, negative effects of warm sea surface temperature anomalies, and positive effects of sea ice concentration. Furthermore, our results highlight differences between populations, revealing three main outcomes. First, the two populations situated at the edge of their distribution seemed more affected by variations in oceanographic conditions than counterparts situated within the core of their ranges. Second, the two northernmost populations experiencing lower wind speed, had their breeding success affected by meridional wind speed. Third, a large-scale climate index had opposite effects on demographic parameters of the two populations situated in the western and eastern parts of the southern Indian Ocean. This comparison of the variation in demographic responses to environmental conditions at the inter-population level, sheds light on our limited understanding of the processes that account for life-history trait variation between populations and suggests that local environmental conditions and large-scale climate patterns may drive the diversity of local ecological responses across species' ranges.

Keywords: albatross, bycatch, climate change, longline fisheries, *Phoebetria*, range limit

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<footnote n="1">¹Centre d'Etudes Biologiques de Chizé, UMR 7372 CNRS-La Rochelle Université, Villiers-en-Bois, France</footnote>

<footnote n="2">²Marine Science Institute, University of California Santa Barbara, Santa Barbara, CA, USA</footnote>
Introduction

Global changes driven by human population growth and resource overconsumption pose significant threats to biodi- versity (Scheffer et al. 2001, Parmesan 2006, Bowler et al. 2020, Ganivet 2020). Understanding the driving forces of wild population dynamics is essential for effective conserva- tion and accurate predictions (Urban et al. 2016). Across species, it is clearly established that life-history strategies can modulate the impact of external drivers on individual fitness, demographic rates, and population dynamics (Clutton-Brock and Coulson 2002, Sæther et al. 2013, Colchero et al. 2019). Studies conducted at the individual level suggest that indi- viduals show different responses to extrinsic factors and that individual states are critical determinants of fitness and popu- lation growth (McNamara and Houston 1996, Inchausti and Ginzburg 2009, Jenouvrier et al. 2015). However, significant knowledge gaps remain on how responses to external drivers vary between populations within species. This shortcoming has major conservation implications, as understanding the variation and plasticity of life-history traits between popu- lations within species improve the estimation of adaptive potential (Milot et al. 2020), which is necessary for accu- rately predicting the responses of interconnected populations to environmental change (Reed et al. 2011, Swab et al. 2015, Hendry 2017).

Reported impacts of external drivers typically focused on a single population (Grosbois et al. 2008, Barbraud et al. 2012, Oro 2014, Jaggi et al. 2024) or, on a single demographic parameter when several populations are studied (Forchhammer et al. 2002, Harris et al. 2005, Jenouvrier et al. 2009, Nevoux et al. 2010, Gaillard et al. 2013, Guéry et al. 2017, Barras et al. 2021). Nevertheless, populations of the same species that may be characterized by different life-history traits (Morrison and Hero 2003, Blanck and Lamouroux 2007, Schultner et al. 2013), are subject to different levels of stochastic environmental variability, and are exposed to diverse local environmental conditions (Gaillard et al. 2013, Barras et al. 2021). As a consequence, population-level demographic responses to environmental conditions may vary within species across their distribution range (Nevoux et al. 2010, Barras et al. 2021). These different population-level responses may be due to local adaptation or to plasticity (Hendry 2017), two processes critical for population persistence. Therefore, successfully predicting the effects of future environmental change on populations and species requires good knowledge of population-level responses to external factors across the distribution range of animal populations, and across the diversity of life-history strategies.

Seabirds are an ideal system to study the responses to extrinsic factors amongst different populations as: their species have broad distributions and are likely experiencing distinct environmental stressors. Studies suggests that different seabird populations of the same species present different life-history strategies, plasticity, adaptability (Schreiber and Burger 2002, Nevoux et al. 2010). Their high longevity (especially albatrosses) is a key factor influencing the

emergence of life history strategies and plasticity (Hecht Orzack and Tuljapurkar 2001, Gaillard and Yoccoz 2003, Koons et al. 2009). Globally, seabirds are one of the most threatened avian taxa (Croxall et al. 2012). Mortality associated with fishery bycatch, climate change, and invasive alien species are the three most ubiquitous threats faced by seabirds (Dias et al. 2019). Increasing number of studies have examined the impacts of bycatch and climate change on seabird demography, but only a few have investigated the combined impacts of those threats, and only on a handful of species (reviewed by Barbraud et al. 2012, Thomson et al. 2015, Pardo et al. 2017, Gianuca et al. 2019, Cleeland et al. 2021). Climate factors impact seabird populations through bottom-up processes by altering the oceanographic environment, which shifts food webs, prey availability and distribution, ultimately affecting individual energy balance (Aebischer et al. 1990, Frederiksen et al. 2006, Quéroué et al. 2021). Fishery bycatch impacts seabird populations through top-down processes by reducing survival rates (Lewison et al. 2014, Gibson et al. 2023). Interactions between fishing boats and seabirds indicate variable susceptibility to bycatch risk depending on species and populations (Corbeau et al. 2021). Therefore, understanding the relative importance of bycatch and climate variability on seabird demography between different populations is required for reliable inference into the spatial variability and individual importance of multiple sources of environmental variability on seabird populations.

We aimed to fill this gap by analyzing the demographic responses of two vulnerable seabird species in the southern Indian Ocean to recent environmental change using long-term monitoring of four populations. Sooty albatross *Phoebetria fusca* (hereafter SOAL) and light-mantled sooty albatross *Phoebetria palpebrata* (hereafter LMSA) are two closely related long-lived seabirds. SOAL nests on islands throughout the Southern Atlantic and Indian oceans as LMSA, which also nests on islands south of New Zealand. Both species breed in sympatry in a handful of sites at Prince Edward and Crozet Islands. LMSA have a circumpolar distribution over the Southern Ocean and exhibit the most southerly distribution of all the albatross species (Delord et al. 2013).

SOAL and LMSA are designated as priority species and at stake for the Agreement for the Conservation of Albatrosses and Petrels (ACAP 2010). SOAL is classified as 'Endangered' (IUCN 2024) and is among the 10 species identified the most at risk by ecological risk assessment for seabirds and fisheries in southern Indian Ocean (Reid et al. 2023). To the best of our knowledge, detailed demographic analyses have been performed only for a single population at Crozet Islands for which adult survival was negatively linked to fishing effort in tuna longline fisheries operating in the southwestern Indian Ocean, and breeding success was affected by sea surface temperature anomalies (Rolland et al. 2010). SOAL breeding success has also been found to be negatively impacted by disease outbreaks at Amsterdam Island (Jaeger et al. 2018). LMSA is classified as 'Near Threatened' (IUCN 2024) as populations may be declining owing to bycatch in longline
fisheries. The species is ranked relatively low in the classification of exposure to bycatch risk (Reid et al. 2023). However, to our knowledge no detailed demographic analysis exists for this species. Hence, the external drivers of demographic rates are poorly known for both species and only for a pair of breeding sites.

Here, utilizing 50 (1971/1972–2020/2021) and 26 (1995/1996–2020/2021) years of capture–mark–recapture data of two populations of SOAL at Crozet and Amsterdam Islands respectively, and 20 (1971/1972–1990/1991) and 18 (1997/1998–2014/2015) years of capture–mark–recapture data of two populations of LMSA at Crozet and Kerguelen Islands, respectively, we aim to investigate: 1) differences in demographic traits, namely adult survival, return probability, breeding probability, success probability among populations, and 2) varying responses to environmental stressors between populations. All populations are considered as not demographically connected, as no movement of banded individuals between colonies was detected during monitoring, suggesting negligible or an absence of dispersal among them.

Based on the findings of a previous study on SOAL at Crozet (Rolland et al. 2010) and direct evidence for SOAL mortality in tuna longline fishing boats (Gales et al. 1998, Huang and Liu 2010), we expect negative relationships between adult survival and longline fishing effort for the two populations of this species. LMSA are accidentally caught in tuna longline fisheries in the Crozet basin (Huang 2017), and we expect a negative effect of fishing effort and adult survival for the Crozet population. The links between climate covariates and demographic parameters were investigated with local covariates (sea surface temperature, sea ice concentration, wind) and large-scale climate indices (Southern Annular Mode, Mascarene High), known to affect seabird demography (Barbraud et al. 2012, Oro 2014, Orgeret et al. 2022). From the seabird ecological literature, we expect demographic rates to be negatively affected by increasing sea surface temperature (Barbraud et al. 2012, Sydeman et al. 2021) and positively affected by wind velocity (Weimerskirch et al. 2012) and sea ice concentration (Post et al. 2013). The links between the

large-scale climate indices and the local climatic fluctuations differ among the foraging areas of SOAL and LMSA in the southern Indian and Southern oceans and among seasons (Ciasto and Thompson 2008, Ohishi et al. 2015, Zhao et al. 2023). We thus expect inter-population heterogeneity in the association between SOAL and LMSA demographic rates and large-scale indices, as adults from the four populations forage in different areas (Delord et al. 2013).

# Material and methods

## Study system and data

### Study species

LMSA and SOAL are the only two species of the *Phoebetria* genus. Both species are similar in body size (Table 1). SOAL breeds on islands north of the Polar Front between 37 and 49°S, whereas LMSA breeding sites are restricted to the Subantarctic region between 46 and 55°S. The marine distribution of SOAL is more northern than the distribution of LMSA as both species forage over subtropical and subantarctic waters, respectively (Delord et al. 2013). SOAL's pelagic distribution is mainly between 30 and 60°S in the southern Indian and Atlantic oceans, with a southern limit of ca 65°S near Antarctica and a northern limit of ca 20°S (Berruti 1979, Weimerskirch et al. 1986). LMSA's pelagic distribution is mainly between 40 and 60°S, and as far as pack ice (77°S) in the Ross Sea (Weimerskirch et al. 1986). Both species breed in sympatry at their respective range limit at Prince Edward and Crozet Islands constituting the northern and southern range limit of LMSA and SOAL, respectively (Weimerskirch et al. 1986, Brooke 2004). The two species are described as obligate biennial breeders (Jouventin and Weimerskirch 1988), lay a single egg in October that hatches in late December (Weimerskirch et al. 1986). The chick fledges in late May. Breeding adults that successfully raise a fledgling in year t take a sabbatical year in year t+1 during which they stay at sea. Those that failed to raise a fledgling in

<table><caption>Table 1. Main characteristics of the four *Phoebetria* populations. SOAL = sooty albatross, LMSA = light-mantled sooty albatross. Numbers in parenthesis indicate sample size.</caption><thead><tr><th></th><th>SOAL Crozet</th><th>LMSA Crozet</th><th>LMSA Kerguelen</th><th>SOAL Amsterdam</th></tr></thead><tbody><tr><td>IUCN status (2023)</td><td>Endangered</td><td>Near threatened</td><td>Near threatened</td><td>Endangered</td></tr><tr><td>Weight (g)</td><td>2485 ± 414 (30)<sup>1</sup></td><td>3070 ± 541 (18)<sup>3</sup></td><td>3005 ± 227 (5)<sup>5</sup></td><td>2602 ± 264 (140)<sup>5</sup></td></tr><tr><td>Wing length (mm)</td><td>512.4 ± 13.4 (31)<sup>1</sup></td><td>515.7 ± 19.2 (18)<sup>4</sup></td><td>545.0 ± 12.4 (10)<sup>5</sup></td><td>524.6 ± 15.8 (154)<sup>5</sup></td></tr><tr><td>Tarsus length (mm)</td><td>82.2 ± 3.0 (9)<sup>1</sup></td><td>81.9 ± 2.4 (18)<sup>4</sup></td><td>86.0 ± 1.2 (9)<sup>5</sup></td><td>82.8 ± 2.6 (153)<sup>5</sup></td></tr><tr><td>Bill length (mm)</td><td>110.3 ± 4.0 (33)<sup>1</sup></td><td>110.7 ± 7.9 (18)<sup>4</sup></td><td>104.3 ± 3.4 (12)<sup>5</sup></td><td>111.6 ± 4.9 (153)<sup>5</sup></td></tr><tr><td>Number of breeding pairs</td><td>≈ 2040<sup>2</sup></td><td>≈ 2300<sup>2</sup></td><td>≈ 3000–5000<sup>2</sup></td><td>≈ 394<sup>2</sup></td></tr><tr><td>Foraging area*</td><td>NW Crozet</td><td>S and SW Crozet</td><td>SE Kerguelen</td><td>Around Amsterdam</td></tr><tr><td>Breeding</td><td>NW and NE Crozet,</td><td>SW and SE Crozet</td><td>SW, E and S Kerguelen</td><td>E Amsterdam, south Australia</td></tr><tr><td>Nonbreeding</td><td>south Australia</td><td></td><td></td><td></td></tr><tr><td>Number of nests monitored (min–max)</td><td>22–110</td><td>7–70</td><td>18–62</td><td>30–107</td></tr><tr><td>Study duration</td><td>1966/67–2020/21</td><td>1965/66–1990/91</td><td>1995/96–2015/16</td><td>1992/93–2020/21</td></tr></tbody></table>

<footnote n="1">¹Jouventin and Weimerskirch 1984, ²Weimerskirch et al. 2018, ³Marchant and Higgins 1990, ⁴Nichols and Murphy 1914, ⁵This study. *See ‘Material and methods’ and the Supporting information.</footnote>
year *t* generally return to breed in year *t* + 1. More details are provided in the Supporting information.

### Mark-recapture data

The study was carried out on the French subantarctic islands, southern Indian Ocean: the Crozet archipelago (hereafter CRZ), Amsterdam Island (AMS), and the Kerguelen archipelago (KER). Number of nests monitored for each site are shown in Table 1. Field procedures for banding and resighting were the same for all populations: all birds were ringed with a stainless-steel band and an engraved plastic band which allowed an individual identification of both members of each pair at the time of the visits to the colony during the breeding season. Two to three visits were made just after the end of the laying period (October–November), one visit was made at hatching (December), and one visit was made just before the fledging period (late April) to determine breeding success and to ring chicks (Fig. 1). Each year, new individuals (breeders and non-breeders) found in the colony were ringed. Sex was not recorded in these monomorphic species in any of the studied populations.

### Mark-recapture modelling

#### Estimation of demographic parameters

The initial dataset included individuals banded as adults and chicks (Supporting information). Very few individuals banded as chicks were reobserved as adults on three study

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Figure 1. Life cycle of sooty and light-mantled albatrosses. Icons represent nest monitoring visits. Yellow and orange represent presence at the breeding colonies; blue represents time at sea. At the end of chick rearing (May), albatrosses either take a sabbatical year or return to the colony the following year in the event of an early failure. An individual breeding in year *t* will lay its egg in October of year *t* and fledge its chick in May of year *t* + 1. The individual can then either take a sabbatical year from June of year *t* + 1 to October of year *t* + 2 or come again to the colony to breed and lay an egg in October of year *t* + 1. Icons inside the life cycle represent the island monitored, Crozet and Amsterdam above and Kerguelen below. In the second cycle the silhouette of a sooty albatross is represented.

colonies, for LMSA (22 and 17 at CRZ and KER, respectively) and SOAL at Amsterdam (53). The only population where a significant number of chicks was reobserved as adults is for SOAL at CRZ (502). Thus, we considered only capture histories of individuals banded as adults to estimate demographic parameters, and for those ringed as chicks, we only considered their capture history following their first breeding event at the colony. This yielded a total of 1801, 665, 490 and 563 adult capture histories for SOAL and LMSA at CRZ, LMSA at KER and SOAL at AMS, respectively.

To explicitly consider the biennial breeding strategy of the two species, and uncertainty on breeding success information in the earlier part of the studies, we started with the multi-event model used by Barbraud and Weimerskirch (2012) for the wandering albatross *Diomedea exulans*. Following Pardo et al. (2013), we included non-breeder information to improve the estimation of demographic parameters (Supporting information). Our multi-events models were defined in terms of 1) survival probability ($\phi$), as the probability that an individual survives from year *t* to year *t* + 1, 2) probability of return (*R*), as the probability that an individual returns to the colony in year *t* + 1 given survival, 3) breeding probability ($\beta$), as the probability that an individual breeds in year *t* + 1 given that it survived and returned to the colony in year *t* + 1, and 4) probability of success ($\gamma$), as the probability that an individual successfully raises a chick until fledging in year *t* + 1, given that it survived, returned, and bred at the colony. Three additional parameters were estimated: the initial state proportion ($\pi$), the detection probability (*p*) in year *t*, and the probability that the state of an individual is observed in year *t* given detection in year *t* ($\delta$).

To ensure the reliability of multi-event models, goodness-of-fit (GOF) tests were used to verify the validity of the assumptions underlying the models (Williams et al. 2002). The assessment of goodness-of-fit remains an open question with multi-event models (Kendall 2009) and there is no GOF test for models with unobservable states (Pradel 2005). We thus examined the GOF tests of multi-state models with three states (SB – successful breeders, FB – failed breeders, NB – non breeders) in which individuals with an uncertain state were randomly reassigned to the FB or the SB state according to the proportions of FB and SB in the data for each population (Supporting information). Goodness-of-fit tests of the general model, corresponding to the Jolly-Move model (Brownie et al. 1993) were performed with the program U-CARE ver. 3.3 (Choquet et al. 2009a).

Model selection and parameter estimation was performed using E-SURGE ver. 2.2.3 (Choquet et al. 2009b), procedures are detailed in the Supporting information. We tested two types of environmental covariates using fisheries and climate data. Except for large-scale climate indices (see below), all covariates were extracted at the local scale based on previous knowledge of the foraging areas of the four populations (Supporting information), distinguishing the breeding and non-breeding season, and considering the time spent per cell obtained from geolocators data (Delord et al. 2013). We tested for the presence of correlations between the covariates tested for all populations (Supporting information).
## Fisheries covariates

We tested whether longline fishery bycatch affected survival and breeding success, as a widowed parent due to its partner death in fisheries is almost certain to fail to fledge the chick on its own, especially when happening early in the chick rearing period. We used fishing effort as a covariate, as accurate estimates of bycatch rate for each population were not available due to a lack or very incomplete on-board observers of fisheries on fishing vessels. Following Véran et al. (2007) we assumed that the level of bycatch was proportional to fishing effort which was generated from monthly reported longline fishing effort data (numbers of hooks deployed) in 5 × 5° spatial cells freely available since 1950 from the Indian Ocean Tuna Commission (IOTC) fisheries and from the Patagonian toothfish fisheries since 1996 from the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR). Fishing effort was expressed as the sum of the number of hooks deployed in the zone and period of interest for each population (Supporting information). We tested the effect of fishing effort during breeding season (i.e. from October in year t to May in year t+1) and during the entire year (i.e. from October in year t to September in year t+1). Fishing effort data from the southern Indian Ocean Fisheries Agreement (SIOFA) fisheries was not used as a covariate since these data were only available from 2010, which was insufficient to test for functional relationships with demographic parameters.

## Climate covariates

Two types of climate covariates were considered (Supporting information): large-scale climate indices (Southern Annular Mode – SAM; Mascarene High pressure – MH), and local covariates (sea surface temperature anomalies – SSTa; zonal u10 and meridional v10 winds; sea ice concentration – SIC). Information regarding the selected zone, period, and data sources for each covariate are provided in the Supporting information.

The SAM is the leading mode of climate variability in the Southern Hemisphere (Fogt and Marshall 2020). In the sub-tropical zone, positive phases of the SAM lead to warmer sea surface temperatures, reduced surface chlorophyll concentrations, associated with easterly winds anomalies that drive Ekman transport southward to the Subantarctic Zone. In contrast, at the south of the Subantarctic Front, cooler sea surface temperatures enhance chlorophyll concentrations, and westerly winds anomalies drive Ekman transport northward to the Subantarctic Zone. This leads to a convergence of waters in the Subantarctic Zone that increases downwelling and leads to low chlorophyll (Lovenduski and Gruber 2005). South of the polar front, positive phases of the SAM are associated with westerly winds anomalies. This induces Ekman transport leading to negative SSTa and increased upwelling. Consequently, the biological productivity and potential prey availability for albatrosses are higher during positive phases of the SAM, to the south of the polar front and vice versa to the north.

We thus predicted that the positive phases of SAM, potentially leading to poorer food availability in the areas

used by albatrosses, would have negative impacts on demographic rates for populations mainly foraging north of the Subantarctic Front and positive effects for those mainly foraging south of the Subantarctic Front.

The MH is a low-level anticyclone spanning over the southern Indian Ocean, connecting the tropical trade wind regime and the midlatitude westerly belt (Krishnamurti and Bhalme 1976, Zhao et al. 2023). The variation of sea surface temperature anomalies in the southern Indian Ocean leads to changes in MH, which in turn contribute to wind anomalies in the region (Morioka et al. 2015, Zhao et al. 2023). MH longitudinal movement forms a southwest-northeast dipole pattern of SST with positive (negative) SSTa in the northeast (southwest) part of the southern Indian Ocean (Ohishi et al. 2015). SSTa reflects local oceanographic conditions that influence the whole marine food web. High SST generally reduces vertical mixing and provides poor growing conditions for zooplankton communities, which, through bottom-up mechanisms, induces reduced trophic resources for seabirds (Barbraud et al. 2012, Sydeman et al. 2015). Therefore, we predicted that the magnitude of the MH at the center of the anticyclone would negatively (positively) affect populations foraging in the northeastern (southeastern) part of the southern Indian Ocean basin. Furthermore, if we assume that populations would be affected by MH when the center of high pressure is positioned over their side of the Indian Ocean basin, then populations within the direct range of the high-pressure center would likely experience even greater impact. Variabilities in the position and strength of MH has recently been shown for the first time to significantly affect the life-history traits of a marine top predator, the wandering albatross (Sun et al. 2025) breeding on Crozet Island. The MH anomalies were obtained from monthly reanalyzed sea level pressure data from ERA5 (Hersbach et al. 2020) to calculate MH indices. The MH area was defined as the total area within the region 30–120°E and 20–50°S above a sea level pressure of 1016.0 millibars. The center of the MH is identified as the location with the highest sea level pressure within the MH area. The MH strength is defined as the average sea level pressure value within the 1016.0 millibars contour. Here we focused on the MH when the center of the anticyclone was at the same longitudes as the population distributions (i.e. east of the Indian Ocen basin for SOAL in AMS and west of the Indian Ocean basin for the three other populations).

For local climate covariates, we used SSTa following the rationale explained above. We predicted negative effects of positive SSTa. Because LMSA forages close to the sea ice edge and in the marginal ice zone, we used SIC as a covariate for these two populations. Sea ice contributes to annual primary productivity in the seasonal ice zone and is known to affect seabird demography (Barbraud et al. 2000, Jenouvrier et al. 2005, Sauser et al. 2023). Thus, we predicted positive effects of increased SIC. Since wind intensity has been found to influence breeding success in albatrosses (Weimerskirch et al. 2012, Ventura et al. 2021), we tested the effects of zonal and meridional winds on breeding success, as winds may
influence the travel distances and the foraging areas of foraging albatrosses. Accordingly, we predicted positive effects of wind intensity.

The covariates tested and the underlying hypotheses are summarized in Table 2. All climatic covariates were available throughout all the monitoring period for each of the populations, except SIC which was available from 1981 (Supporting information). The Supporting information details the spatial areas and period of the year for which covariates were calculated; Fig. 2 and the Supporting information show the at sea distribution of the four populations during the breeding period and throughout the year and the corresponding covariate areas tested. All covariates were standardized on the study periods which allows to compare slopes obtained from relationships between different demographic parameters, different environmental covariates and different populations (Grosbois et al. 2008). Likelihood ratio tests are biased and tend to detect too often a covariate effect, contrary to analysis of deviance (ANODEV) tests that behave properly (Lebreton et al. 2012). We therefore used ANODEV tests (Grosbois et al. 2008) to test for the effect of the set of covariates defined. This test follows a Fisher–Snedecor distribution, where F is defined as:

$$F_{\text{test}}_{\text{cst/cov/t}} = \frac{\left( \frac{\text{Deviance}(F_{\text{cst}}) - \text{Deviance}(F_{\text{cov}})}{J-1} \right)}{\left( \frac{\text{Deviance}(F_{\text{cov}}) - \text{Deviance}(F_{t})}{n-J} \right)}$$

where F_cst is the model where the tested parameter is constant, F_t is the model where the tested parameter is time dependent,

F_cov is the model where the tested parameter is correlated to the tested covariate, n is the number of occasion - 1, and J is the number of parameters for the description of the covariate.

To make inferences on the effects of covariates, we used ANODEV tests considering a rejection threshold of 0.06 and the 95% confidence interval of the slope parameter describing the relationship between the demographic trait and the covariate (we rejected variables for which 95% confidence intervals included 0). The proportion of deviance explained by covariates (R²; Skalski 1996) was estimated as:

$$r^2 = \frac{\text{Deviance}(\text{Mcst}) - \text{Deviance}(\text{Mcov})}{\text{Deviance}(\text{Mcst}) - \text{Deviance}(\text{Mt})}$$

Where Mcov is the covariate model, Mt is the time-dependent model and Mcst is the constant model.

In order to avoid a spurious correlation with covariates in the presence of temporal trends, linear trends in demographic parameters were investigated using ANODEV tests, and in environmental covariates in R with linear regression. If temporal trends were detected, we followed the procedure in Grosbois et al. (2008), that is, using residuals of the linear regression of the covariates, to test whether the covariates accounted for a significant fraction of the variation in survival about the trend. In case of an impact of fisheries on a demographic parameter, we also calculated the demographic parameter in hypothetic condition where there would be no fishing effort from the functional relationships that related the demographic parameter with the fishing effort.

In order to assess the relative importance of each covariate according to the different demographic parameters, we compared the effect sizes of the covariates and calculated the

<table><caption>Table 2. Types of covariates tested for each demographic parameter and underlying hypotheses.</caption><thead><tr><th>Type of covariate</th><th>Survival probability</th><th>Breeding probability</th><th>Success probability</th><th>Hypothesis</th></tr></thead><tbody><tr><td>Fishing effort</td><td>↘</td><td></td><td>↘</td><td>Increased fishing effort causes bycatch mortality and a reduction in survival and breeding success as widowed parents may fail to fledge their chick</td></tr><tr><td>Southern Annular Mode (SAM)</td><td>Depending on species and location</td><td>Depending on species and location</td><td>Depending on species and location</td><td>SAM affects primary production and prey availability, which may affect demographic parameters</td></tr><tr><td>Sea ice concentration (SIC)</td><td>↘</td><td></td><td>↘</td><td>SIC affects primary production and prey availability. This may affect breeding success and survival of LMSA which forage in the marginal ice zone during breeding</td></tr><tr><td>Sea surface temperatures anomalies (SSTa)</td><td>↘</td><td></td><td>↘</td><td>SSTa impacts food abundance and availability and may affect survival and breeding success</td></tr><tr><td>Mascarene High (MH)</td><td>Depending on species and location</td><td>Depending on species and location</td><td>Depending on species and location</td><td>A subtropical zone of high pressure in the region 20–50°S, 30–110°E, which impacts wind speed and sea surface temperature, and thus demographic parameters</td></tr><tr><td>Wind speed</td><td></td><td></td><td>↘</td><td>Influences travel distances and foraging trip duration during breeding, thus potentially affecting breeding success of the current breeding cycle</td></tr></tbody></table>
<figure><img src="image_6.png" /><figcaption>Figure 2. Map showing the at sea distribution range of each population during breeding (A) and non-breeding periods (B) obtained from tracking data (Delord et al. 2013) for sooty albatrosses (orange) and light-mantled sooty albatrosses (blue). See the Supporting information for detailed at sea distributions. The polar front is represented as a dashed line and the Subantarctic Front as a dotted line. Regional Fisheries Management Organization limits are represented in full lines for the Commission for the Conservation of Antarctic Marine Living Resources – CCAMLR (green) and for the Indian Ocean Tuna Commission – IOTC (red). Grey lines represent exclusive economic zones. Map lines delineate study areas and do not necessarily depict accepted national boundaries. CRZ: Crozet, KER: Kerguelen, AMS: Amsterdam.</figcaption></figure>

partial derivative of the parameter with respect to each covariate in a sensitivity analysis (Horswill et al. 2014). The sensitivity (S) of the demographic trait (θ) to the covariate X was:

$$S = \frac{\partial \theta}{\partial X} = \hat{b}_1 (\hat{\theta} - \hat{\theta}^2)$$

where $\text{logit}(\hat{\theta}) = b_0 + b_1 X$, with $b_0$ the intercept and $b_1$ the slope parameter.

Linking climate drivers to demographic processes requires identifying the appropriate time windows during which climate influences vital rates (Evers et al. 2023). This requires knowledge of the ecology of preys consumed by the studied species when considering bottom-up processes. Unfortunately, the ecology of the squid species consumed by these two albatross species is almost unknown, basic information on life cycles and ecology lacking entirely for most species. Squids are known to grow relatively fast, with relatively short life cycles of a few months to a couple of years

(Arkhipkin 2004). Therefore, we considered a direct and a one-year lag for the effects of large-scale indices on survival. For breeding success and breeding probability, as the conditions of the previous year would rather impact the choice to breed or not and may modify body condition and thus breeding success, we considered a one-year lag.

## Results

### Goodness-of-fit tests

GOF tests were not statistically significant for LMSA, indicating that the general model fitted correctly to the data (i.e. model assumptions were valid). GOF tests were significant for SOAL (Supporting information). For three populations (SOAL in CRZ, SOAL in AMS and LMSA in KER) we found evidence for transience, i.e. there was a reduced expectation of re-observing individuals marked for the first time in comparison to other individuals already observed previously (see tests 3G.SR in the Supporting information). We thus
suppressed the first encounter for each individual in each dataset to minimize transience. This improved GOF tests for all populations (Supporting information). This resulted in a total of 1136 and 271 adult capture histories for SOAL and LMSA at CRZ, respectively, 287 adult capture histories for LMSA at KER and 430 adult capture histories for SOAL at AMS. For each population, we detected trap-shyness (M.ITEC tests; Supporting information). This was expected due to the biennial breeding strategy of SOAL and LMSA. Nevertheless, as explained above, our multievent models explicitly took trap-shyness into account by using unobserv- able states.

## Modelling the detection process

The model selection procedure retained the model where detection probability (p) varied between states and between years, assignment probability (δ) was constant, and ini- tial state proportion (π) was state dependent (Supporting information).

## Life-history trait variability among populations

Mean demographic parameters for the four populations are shown in Table 3. Mean adult survival was between 3.9 and 5.3% lower for Crozet populations compared to Kerguelen and Amsterdam. As expected, the mean return probability was high for individuals that were in a sabbatical the previ- ous year except for SOAL at Amsterdam (0.162 ± 0.076). Except for this population, the mean proportion of individu- als that did not take a sabbatical between two breeding season was around 45 to 50%. Mean annual breeding probability was generally high (from 0.77 to 0.93) except for SOAL that returned from a sabbatical at AMS (0.257). Mean success probability varied between 0.41 and 0.57 for all populations except for SOAL at AMS for which it was particularly low for breeders and high for individuals returning from a sabbati- cal. Year-specific demographic parameters are shown in the Supporting information.

Temporal trends in demographic parameters were detected in three populations (Supporting information). At CRZ the adult survival of SOAL and LMSA decreased dur- ing 1972–2020 and 1972–1990, respectively. Comparing trends in survival during the same time period (1972–1990) for the two species at CRZ indicated that both experienced

a decreasing trend in survival (SOAL: β₁ = −0.13 ± 0.10, ANODEV p-value < 0.001). At AMS, the probability of success of SOAL decreased over time, reaching nearly 0 in recent years.

## Impacts of fishing efforts

The results of the ANODEV tests are presented in the Supporting information for each of the four populations. Three significant relationships between one fishery covari- ate, the fishing effort reported by IOTC, and demographic parameters were identified for which 95% CIs of slope param- eters did not include 0 (Fig. 3). The IOTC fishing effort dur- ing the whole year in a sector northeast of CRZ and during the breeding period, was negatively related to the survival of SOAL in CRZ (β₁ = −0.13 ± 0.04 and β₁ = −0.14 ± 0.06, respectively). Similarly, a sector east of CRZ was negatively related to the survival of LMSA in CRZ (β₁ = −0.52 ± 0.15). The IOTC fishing effort in a sector north of CRZ during the breeding period was negatively correlated to the breeding success of SOAL in CRZ that were in sabbatical the previous year (β₁ = −0.29 ± 0.09).

## Different responses to climatic variables between populations

The results of the ANODEV tests are presented in the Supporting information for each of the four populations. Significant relationships for which the 95% CIs of the slope parameters did not include 0 were detected between five climate covariates and demographic parameters (Fig. 3). Among large-scale climate indices, SAM had a positive effect on SOAL breeding probability at AMS. The Mascarene High had opposite effects on SOAL demography at CRZ and AMS, with a positive effect on success probability at AMS and a negative effect on survival probability at CRZ. This negative effect was retained once survival and MH were detrended. The MH with a 1-year lag had the same negative effect on survival probability at CRZ. No effect of MH was detected on the two LMSA populations. Meridional winds (v10) were positively related to the breeding success probability of SOAL at CRZ (individuals that were in a sabbatical during the previous year) and at AMS (individuals at the colony the previous year). At AMS this positive effect was retained after success probability and v10 wind were detrended. SSTa was

<table><caption>Table 3. Estimation of the mean (± SE) demographic parameters for the four albatross populations. Estimates were obtained from the selected models where survival probability was constant, return, breeding and success probabilities were state dependent, capture probability was state, and time dependent and assignment probability was state dependent. Observed includes successful, failed and non-breeders, unob- served includes post-successful, post-fail and post non-breeders.</caption><thead><tr><th></th><th>State</th><th>SOAL Crozet</th><th>LMSA Crozet</th><th>LMSA Kerguelen</th><th>SOAL Amsterdam</th></tr></thead><tbody><tr><td>Study period</td><td></td><td>1972–2020</td><td>1972–1990</td><td>1998–2014</td><td>1995–2020</td></tr><tr><td>Survival</td><td>All</td><td>0.888 ± 0.003</td><td>0.884 ± 0.011</td><td>0.924 ± 0.008</td><td>0.933 ± 0.004</td></tr><tr><td>Return</td><td>Observed</td><td>0.487 ± 0.014</td><td>0.461 ± 0.050</td><td>0.441 ± 0.031</td><td>0.984 ± 0.004</td></tr><tr><td></td><td>Unobserved</td><td>1.000 ± 0.000</td><td>0.996 ± 0.054</td><td>1.000 ± 0.000</td><td>0.162 ± 0.076</td></tr><tr><td>Breeding</td><td>Observed</td><td>0.785 ± 0.020</td><td>0.926 ± 0.079</td><td>0.886 ± 0.075</td><td>0.916 ± 0.009</td></tr><tr><td></td><td>Unobserved</td><td>0.767 ± 0.021</td><td>0.855 ± 0.131</td><td>0.874 ± 0.083</td><td>0.257 ± 0.283</td></tr><tr><td>Success</td><td>Observed</td><td>0.413 ± 0.019</td><td>0.499 ± 0.094</td><td>0.534 ± 0.069</td><td>0.251 ± 0.015</td></tr><tr><td></td><td>Unobserved</td><td>0.561 ± 0.021</td><td>0.568 ± 0.096</td><td>0.532 ± 0.068</td><td>0.996 ± 0.028</td></tr></tbody></table>
<figure><img src="image_7.png" /><figcaption>Figure 3. Demographic parameters modelled as a function of covariates (plain line) with 95% confidence intervals and annual estimates obtained from the time dependent models for each population (filled circles). SOAL: sooty albatross, LMSA: light-mantled sooty albatross, CRZ: Crozet, KER: Kerguelen, AMS: Amsterdam. Error bars indicate ± SE. (A) Survival probability as a function of IOTC fishing effort, sea surface temperature anomalies (SSTa), and Mascarene High. (B) Breeding probability as a function of Southern Annular Mode. (C) Breeding success as a function of IOTC fishing effort, wind speed, Mascarene High, SSTa and sea ice concentration (SIC) in zone 1 (Z1). Effects of SIC and SSTa in zone 2 on LMSA at CRZ are represented in the Supporting information as they are quite similar as zone 1. Similarly, the effects of IOTC fisheries during breeding season and of Mascarene High with a 1-year lag on SOAL survival at CRZ are represented in the Supporting information. Only significant relationships are represented. ‘n.s.’ indicate non-significant relationships. All relationships tested and zones are detailed in the Supporting information.</figcaption></figure>
negatively related to survival probability of SOAL and LMSA at CRZ, and to success probability of LMSA at CRZ. There was also a positive relationship between success probability and SIC in LMSA at CRZ.

## Sensitivity analysis

The sensitivity analysis (Fig. 4, Supporting information) showed that success probabilities were the most sensitive demographic parameters to climate covariates. Success probability of LMSA at CRZ was most sensitive to SSTa and SIC. Similar sensitivities were found for success probability of SOAL at AMS to the MH and meridional winds. The lowest sensitivities were found for success probabilities of SOAL at CRZ to IOTC fishing effort and meridional winds. Survival sensitivities to environmental covariates were on average lower than for success probabilities, both for LMSA and SOAL at CRZ. Similarly, a low sensitivity was found for breeding probability to SAM for SOAL at AMS.

## Discussion

In this study, we analyzed the effects of climate as a bottom-up process and fisheries bycatch as a top-down process on survival and breeding performances of four populations from two seabird species with a distribution across an oceanic basin scale (Fig. 2). Results highlight differences between the four populations in terms of demographic traits and their respective trends, and in the environmental factors affecting those traits (Fig. 5). Results were consistent with our predictions, with negative relationships between fishing effort and

survival or breeding success, positive effects of wind velocity on breeding success, negative effects of warm sea surface temperature anomalies on survival and breeding success, and positive effects of sea ice concentration on breeding success. Large-scale climate indices had reversed effects on demographic traits of populations situated east and west of the ocean basin (Mascarene High) but seems to impact only SOAL. This large-scale comparison of inter-population variations in demographic responses to environmental conditions can first lend insights into our understanding of the processes that account for life-history trait variation between populations, and second suggests that local environmental conditions and large-scale climate patterns may drive variability in local ecological responses across species' ranges. Given some non-overlapping periods of population monitoring, we cannot entirely rule out that some of the differences detected between populations could partly result from period-specific effects rather than population responses to environmental conditions. This mainly concerns the LMSA population at Crozet for which the monitoring does not overlap with those from the LMSA at Kerguelen and SOAL at Amsterdam. Nevertheless, with this limitation in mind, the relationships between demographic parameters of LMSA at Crozet and the fisheries and climatic covariates, were coherent with our predictions and with the responses of other albatross populations to environmental variability (Pardo et al. 2017).

## Demographic characteristics of the four populations

Two patterns emerge from comparison of demographic rates of these four populations with those populations breeding elsewhere. First, adult survival was particularly low for

<figure><img src="image_8.png" /><figcaption>Figure 4. Sensitivities of demographic parameters to environmental and fisheries covariates according to populations for which significant relationships were detected. SOAL: sooty albatross, LMSA: light-mantled sooty albatross, CRZ: Crozet, KER: Kerguelen, AMS: Amsterdam, MH: Mascarene High, SSTa: Sea surface temperatures anomalies, SIC: Sea ice concentration, SAM: Southern Annular Mode. Z1 and Z2 refers to the zones for LMSA (detailed in the Supporting information).</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Figure 5. Diagram summarizing the relationships between environmental forcings, demographic parameters and individual attributes. Solid arrows represent the effect of forcings on demographic parameters, with the thickness proportional to the sensitivity of the parameter. Dotted lines show the effect of individual attributes on the demographic parameters.</figcaption></figure>

populations of the two species in Crozet, as reported earlier for SOAL (Rolland et al. 2010). We also found negative trends for the survival of these two populations, which is consistent with high survival estimates found at the beginning of the long-term monitoring during the period 1976–1982 (0.973 for LMSA and 0.950 for SOAL at Crozet; Weimerskirch et al. 1987). Adult survival of SOAL at Amsterdam and LMSA at Kerguelen were closer to survival expected for biennially breeding albatross species (i.e. grey-headed albatross *Thalassarche chrysostoma*: Waugh et al. 1999, Terauds et al. 2005, Pardo et al. 2017, Cleeland et al. 2021; wandering albatross: Nel et al. 2003, Terauds et al. 2006, Barbraud and Weimerskirch 2012, Pardo et al. 2017; Amsterdam albatross *Diomedea amsterdamensis*: Rivalan et al. 2010), and comparable to survival estimates obtained for LMSA at Macquarie island (0.924; Cleeland et al. 2021). Second, return, breeding and success probabilities were within the range of those found in other albatross populations (Rolland et al. 2010, Pardo et al. 2017, Cleeland et al. 2021), except for SOAL at Amsterdam. There, breeding success was two times lower than for other populations. This was likely due to avian cholera which causes high chick mortality in Indian yellow-nosed albatrosses *Thalassarche chlororhynchos* and SOAL at Amsterdam (Bourret et al. 2018, Jaeger et al. 2018). As a consequence of high breeding failure (occurring mainly during brooding or early chick-rearing period), most individuals that fail in a given year return and attempt to breed the following year, likely explaining the high values for these parameters. Although no significant trend was detected for the breeding success of SOAL at Crozet, our estimates were from 18 to 40% lower than those found by Rolland et al. (2010). This is partly due to relatively low breeding success in years following the study of Rolland et al. (2010) (Supporting information).

Low adult survival estimates for populations of both species at Crozet together with declining trends in these

populations (Weimerskirch et al. 2018), strongly suggest that relatively high adult mortality was a strong driver of population decline. Indeed, population growth rate is highly sensitive to adult survival in such long-lived species (Sæther and Bakke 2000). Although a retrospective analysis would be needed to precisely quantify the demographic causes of these declines, no population model could be built for the SOAL and LMSA populations due to the absence (or lack) of data for the juvenile components of the life cycle.

## Comparison of the impact of fisheries

The four albatross populations were impacted by fisheries in different ways. The two populations breeding in sympatry at Crozet (SOAL and LMSA) were most affected by fisheries, as revealed by the negative relationships between fishing effort in IOTC fisheries and adult survival. Rolland et al. (2010) previously identified such a relationship for SOAL at Crozet, but our results also revealed a negative effect on breeding success. This was likely due to the incapacity for a single individual to raise its chick until fledging when its partner died accidentally in fishing gear. Albatross bycatch rates in tuna longline fisheries that operate in the southeast and southwest Indian Ocean ranged from 0.001 to 0.024 albatrosses/1000 hooks, most medium-sized albatrosses being sooty albatrosses, and among Indian Ocean fisheries, they are the second most frequently bycatch albatross species (across all size categories), after Indian yellow-nosed albatrosses (Huang 2017). Although IOTC adopted recommendations and asked fishing vessels to take mitigation measures when operating in the southern Indian Ocean (Rollinson et al. 2016), our results suggest that mitigation measures are insufficient and/or are only partially applied. Illegal or unreported fishing that does not respect the mitigation measures could also explain this effect. The IOTC fishing effort in the sector used by SOAL from Crozet during the non-breeding
season (65–90°E, 30–45°S) and during the breeding season (30–65°E, 30–45°S) had the most significant negative effect on adult survival. Interestingly, highest rates of bycatch mortality for this species in the Indian Ocean was reported in these sectors (Huang 2017). This could indicate hotspots of bycatch mortality where mitigation measures should be reinforced to help improve the conservation status of this species. This is urgently needed as our results suggest that bycatch negatively affects survival and breeding success, which likely explains the strong declining trend of the Crozet population (–74.7% from 1980 to 2017; Weimerskirch et al. 2018). In addition, despite relatively low attraction rates to fishing boats (Banda et al. 2024, Fromant et al. 2024), we found some evidence of a negative effect of IOTC fishing effort in the sector used by SOAL from Crozet during the breeding season on survival (southeast Indian Ocean 30–65°E, 30–45°S), where bycatch was also reported (Huang 2017). Surprisingly, SOAL from Amsterdam did not seem to be affected by fishing effort. This may be explained by their different at sea distributions compared to SOAL at CRZ. During breeding, SOAL from Amsterdam forage within the Amsterdam Island Exclusive Economic Zone where longline fishing effort is extremely low (Heerah et al. 2019). During the non-breeding period, individuals move south to Australia where longline fishing effort is also relatively low (Tuck et al. 2003), and where low bycatch rates of SOAL have been reported (Gales et al. 1998, Huang 2017). This suggests that SOAL from Amsterdam are less exposed to fishing vessels than those from Crozet. Nevertheless, fishing effort data from the SIOFA fisheries were not used as a covariate because these data were only available starting in 2010, which was insufficient to examine functional relationships with demographic parameters, even though bycatch is suspected in these areas (Reid et al. 2023).

To our knowledge, this is the first time that a negative effect of fishing effort on adult survival of LMSA has been reported. However, this negative effect was only observed for the Crozet population, which may appear paradoxical as we considered that both populations foraged in the same zones. Two main reasons may explain this difference. First, although we assumed that both LMSA populations used the same areas at sea due to sparse tracking data for LMSA at Kerguelen, both populations may not use the same zones and LMSA from Kerguelen may be less exposed to longline fisheries. Second, this may be due to the monitoring of these two populations that did not occur at the same time. Indeed, fishing effort in the IOTC sector 80–110°E, 45–55°S increased from 1960s to 1980s, then decreased from the late 1980s (Tuck et al. 2003) and remained very low during the monitoring period of LMSA at Kerguelen.

Overall, our results combined with reported bycatch rates suggest that fisheries bycatch negatively impacted demographic parameters (mainly survival) of sooty and light-mantled sooty albatrosses at Crozet. Other populations at Kerguelen and Amsterdam show no demographic response to fishing effort in the southern Indian Ocean. Between species differences in responses to bycatch have been reported in different oceans basins (Rolland et al. 2010, Pardo et al. 2017,

Clay et al. 2019, Cleeland et al. 2021), but such differences may arise due to species-specific ecological and behavioral traits. Here, our results suggest between population differences at the intra- and inter-specific levels likely resulting from differences in spatial distribution at sea specific to each population, and to the spatio-temporal dynamics of fishing activities during the past 50 years. Although the bycatch threat for LMSA at Crozet has likely vanished due to very low longline fishing effort in recent years, it remains high for SOAL at Crozet. Based on our functional relationships, adult survival for SOAL and LMSA should be 0.907 ± 0.004 and 0.939 ± 0.008, respectively, if fishing effort equaled zero (i.e. 2.1 and 6.2% higher than observed), corresponding hypothetically to no bycatch in IOTC fisheries. Similarly, breeding success probability should be 0.708 ± 0.029 for SOAL at Crozet without bycatch (i.e. 26.2% higher than observed), which is nearly equal to values reported by Rolland et al. (2010). The effect of fishing effort may explain why the breeding success was lower in our study than in Rolland et al. (2010), as fishing effort doubled in the area since 2015.

## Effects of climate covariates on demographic parameters of the four populations

The influence of climate covariates on demographic parameters was different in the four study populations. Three main outcomes can be identified from this comparative analysis. First, the two populations situated at the edge of their distribution, i.e. SOAL and LMSA at Crozet, respectively, at their southern and northern distribution edge, seemed more affected by variations in oceanographic conditions (sea surface temperature and sea ice concentration) than their counterparts situated within the core of their ranges. Indeed, our results showed that while sea surface temperature was not associated with demographic parameters for SOAL at Amsterdam and LMSA at Kerguelen, it negatively affected demographic parameters of the two populations at Crozet (survival for both species, breeding success for LMSA), and breeding success of LMSA at Crozet was positively associated with sea ice concentration but not for LMSA at Kerguelen. Populations residing at distribution edges may experience environmental conditions that tend to be harsher and less predictable than those faced by populations in the core of the distribution range and may be more sensitive to climate variability (Sexton et al. 2009, Barras et al. 2021, Oro et al. 2024). For LMSA breeding at Crozet, reaching foraging grounds situated in the Subantarctic Zone and in the Antarctic Zone (Cherel et al. 2025) may be more energetically costly than for those breeding at more southerly latitudes such as Kerguelen. This is for example what was shown for king penguins *Aptenodytes patagonicus* breeding at Crozet, far from the Polar Front, and at Kerguelen, close to the Polar Front (Brisson-Curadeau et al. 2023). When oceanographic conditions deteriorate (higher SST, lower SIC) likely resulting in lower food resources, individuals may not be able to compensate their energetic balance and experience decreased breeding success or survival. Likewise, Oro et al. (2024) found differential effects of food resources
on the breeding performance of edible dormouse *Glis glis*
populations depending on the resemblance of their sur-
rounding environments to core or edge population habitats.
However, differences in responses between populations may
also partly stem from other factors than resources at sea,
such as local environmental factors at the breeding localities
(Harris et al. 2005), or asymmetries in gene flow affecting
local adaptation (Sexton et al. 2009).

Second, the two SOAL populations, but none of the LMSA populations, had their breeding success affected by meridional wind speed. Wind strength has been shown to affect breeding parameters of seabirds either indirectly through its role in favoring food availability by enhancing convective water mixing, or directly through its effect on the accessibility of foraging patches by shaping the commut- ing flight costs between the breeding colony and the forag- ing patches (Weimerskirch et al. 2012, Pardo et al. 2017, Cleeland et al. 2021, Ventura et al. 2021). During breeding, pelagic seabirds are wide-ranging predators that act as cen- tral place foragers and undertake long foraging trips during which they rely extensively on wind to move at low costs between breeding and foraging sites (Weimerskirch et al. 2000, Wakefield et al. 2009). During these trips they extract energy from the wind through a flight behavior known as dynamic soaring (Pennycuick 2002). Thus, wind conditions shape the energy expenditure faced by commuting seabirds, which in turn can determine the spatial extent of the areas accessible for seabirds to use and, ultimately, affect breeding success. Breeding individuals from both SOAL populations forage in waters between 30 and 45°S were wind strength is on average lower than in foraging areas of those from both LMSA populations, which forage south of 50°S (Remya et al. 2020). Therefore, we hypothesize that costs of foraging for SOAL are likely more affected by low wind conditions that occur more frequently in their foraging areas than in those of LMSA foraging at more southern latitudes. Such empiri- cal evidence of latitudinally related effects of wind on move- ments at the intraspecific level was found in the wandering albatross in which females have shifted poleward their north- ern range to a larger extent than males (Weimerskirch et al. 2012). This southward shift of the northern range allows females to fly at higher speeds in windier areas, as well as in waters less distant from the colonies, and thus to further reduce foraging time.

Third, the MH had opposite effects on demographic
parameters of the two SOAL populations situated in the
western and eastern parts of the southern Indian Ocean.
Adult survival of SOAL at Crozet was negatively associated
with MH of the same year and of the preceding year, whereas
the breeding success of SOAL at Amsterdam showed a posi-
tive association for the same year. This adds to the growing
number of studies suggesting that large-scale climate varia-
tion influence animals demographic traits (Stenseth et al.
2003, Grosbois et al. 2008, Oro 2014), and supports the
rationale of the use of large-scale climate indices in demog-
raphy and ecology (Forchhammer and Post 2004). However,
our results also highlight potential problems that may arise

when using large-scale climate indices to compare climatic
sensitivities across populations (Van De Pol et al. 2013).
Indeed, the relationship between large-scale indices and local
weather variables may vary geographically, raising the pos-
sibility that apparent inter-population differences in climate
sensitivity to indices could also reflect geographic variation
in how well indices function as a proxy for local climatic
drivers. Such contrasted effects of a large-scale climate index
(North Atlantic Oscillation) were found in a study com-
paring the demographic responses of three common eider
*Somateria mollissima* populations in Canada, Svalbard and
Norway (Guéry et al. 2017). The population-specific asso-
ciation between eider survival and the winter North Atlantic
Oscillation was attributed to the links between this large-
scale climate index and local weather experienced by eiders
on their wintering grounds. In our study, we have highlighted
opposite effects of MH magnitude on the two populations
of SOAL. High MH anomalies correspond to warm sea sur-
face temperature anomalies and weaker wind speed in the
foraging areas of SOAL at Crozet, whereas at Amsterdam
they correspond to colder sea surface temperatures (Meeker
2021). We suggest that these contrasted effects of MH on
oceanographic and climatic processes at the western and
eastern parts of the SOAL range in the southern Indian
Ocean could explain the observed intra-specific heterogene-
ity in demographic responses to MH. The MH position and
intensity is strongly correlated with mid-latitude (30–60°S)
westerly wind and enhanced MH indicates enhanced west-
erly (Zhao et al. 2023). However, no effect on LMSA was
evidenced. Thus, when the center of the anticyclone is within
population distribution, its impact is significant, whereas it
is not when it is at similar longitudes and could cause more
westerly winds. This suggests that MH impacts SOAL more
through SSTa variations than through wind changes.

Local environmental covariates were selected according to the tracking data collected between 2006 and 2009, which constitutes a potential limitation to our study. However, as shown by Carpenter-Kling et al. (2020) and by Bentley et al. (2024), sooty albatrosses do not seem to vary much in their foraging habitats between years, but rather vary in habitat type used depending whether they are in allopatry or in sym- patry, which was confirmed by a recent tracking study on SOAL at Crozet in 2022 (Fromant et al. 2024). LMSA seem to have much variance in their foraging environment over time (Carpenter-Kling et al. 2020, Bentley et al. 2024) and it would be useful to have more tracking data during recent periods. Additionally, as individuals were not sexed, we did not consider sex differences in demographic parameters and in their responses to environmental factors. Sooty albatrosses and light-mantled sooty albatrosses are not sexually dimor- phic, thus is it difficult to assess the sex of individuals without molecular sexing techniques, which did not exist when moni- toring stared in the early 1970s. As it has been shown that the effect of fishery and climate variability on demographic can be sex-specific (Gianuca et al. 2019), further studies would be needed to investigate sex effects in these albatrosses demography.
## Conclusion

Long-term studies investigating between population responses to environmental variability can lead to a deeper understanding of the variation in life-history traits in relation to eco-geographical gradients (Morrison and Hero 2003, Blanck and Lamouroux 2007, Alice Boyle et al. 2016, Scholer et al. 2020), and of the capacities of organisms to adapt to environmental changes (Sexton et al. 2009, Usui et al. 2023). By studying the demographic responses of four populations at the core and edge of the distribution range our study shed light on our limited understanding of the role of ecological factors that promote or hinder local adaptation (Kawecki and Ebert 2004, Oro et al. 2024). At the inter-population level, demography of albatross populations situated at range edges seemed more sensitive to climate variability than those at core areas, which may constrain plasticity (Usui et al. 2023). This was also apparent at the interspecific level, with northernmost foraging populations of albatrosses being more sensitive to wind variability than those of high latitude foraging species where wind strength is on average stronger. Besides, our results suggest that on average top-down forcings (mortality in fisheries) may further contribute to the demographic sensitivity of edge populations, threatening their persistence. The effects of fisheries on albatross survival indicate that current mitigation measures are either insufficient or only partially applied, at least in the areas tested. Regional Fisheries Management Organizations (RFMOs) have the responsibility to implement the most effective mitigation strategies and to ensure the protection of albatrosses (Beal et al. 2021).

**Acknowledgements** – This article is dedicated to the memory of Pierre Frigola, volunteer and ornithologist on the Crozet archipelago. On 22 April 22 1984, he fell to his death at Cape Gauss while monitoring light-mantled albatrosses. We thank all the field workers involved in albatross long-term monitoring studies from Crozet, Kerguelen and Amsterdam since 1965. We thank Daphnis De Pooter (CCAMLR) for assisting with the provision of the fishery data, and all data owners for their permission for the use of the data. Fishery data that were collected as part of the French Southern Ocean fisheries observation program (Gasco 2011) were entered on board into an electronic logbook designed by the Muséum National d'Histoire Naturelle de Paris (MNHN) and uploaded into the Oracle 11G database "PECHEKER" (Martin et al. 2021) hosted and managed by MNHN. Long-term demographic studies were logistically and financially supported by the Institut Polaire Français (IPEV project 109 ORNITHO2E, PI C. Barbraud), Terres Australes et Antarctiques Françaises, and Zone Atelier Antarctique et Terres Australes (LTSER France, CNRS). This study is part of the long-term Studies in Ecology and Evolution (SEE-Life) program of the CNRS. The Ethics Committee of IPEV and Comité de l'Environnement Polaire approved all field procedures.

**Funding** – CS acknowledge funding support from La Rochelle Université. RS acknowledge funding support from the University of California Climate Action 2023 R02CP7252.

**Permits** – The Ethics Committee of IPEV and Comité de l'Environnement Polaire approved all field procedures.

## Author contributions

**Camille Schatz:** Investigation (equal); Methodology (equal); Writing – original draft (equal); Writing – review and editing (equal). **Karine Delord:** Data curation (lead); Project administration (equal); Writing – review and editing (equal).

**Dominique Joubert:** Data curation (equal). **Samuel Peroteau:** Data curation (equal). **Ruijiao Sun:** Methodology (equal); Writing – review and editing (equal).

**Christophe Barbraud:** Conceptualization (lead); Funding acquisition (lead); Investigation (equal); Methodology (equal); Project administration (lead); Resources (lead); Supervision (lead); Writing – original draft (equal); Writing – review and editing (equal).

## Data availability statement

There are no additional data for this publication

## Supporting information

The Supporting information associated with this article is available with the online version.

## References

ACAP 2012. Agreement on the conservation of albatrosses and petrels. 2010. ACAP species assessments: sooty albatross *Phoebetria fusca*, light-mantled albatross *Phoebetria palpebrata*. – http://www.acap.aq.

Aebischer, N. J., Coulson, J. C. and Colebrook, J. M. 1990. Parallel long-term trends across four marine trophic levels and weather. – Nature 347: 753–755.

Alice Boyle, W., Sandercock, B. K. and Martin, K. 2016. Patterns and drivers of intraspecific variation in avian life history along elevational gradients: a meta-analysis. – Biol. Rev. 91: 469–482.

Arkhipkin, A. 2004. Diversity in growth and longevity in shortlived animals: squid of the suborder Oegopsina. – Mar. Freshw. Res. 55: 341–355.

Banda, S., Pistorius, P., Collet, J., Corbeau, A., Weimerskirch, H., Pajot, A., Keys, D. Z. and Orgeret, F. 2024. Gauging the threat: exposure and attraction of sooty albatrosses and white-chinned petrels to fisheries activities in the southern Indian Ocean. – ICES J. Mar. Sci. 81: 75–85.

Barbraud, C. and Weimerskirch, H. 2012. Estimating survival and reproduction in a quasi-biennially breeding seabird with uncertain and unobservable states. – J. Ornithol. 152: 605–615.

Barbraud, C., Weimerskirch, H., Guinet, C. and Jouventin, P. 2000. Effect of sea-ice extent on adult survival of an Antarctic top predator: the snow petrel *Pagodroma nivea*. – Oecologia 125: 483–488.

Barbraud, C., Rolland, V., Jenouvrier, S., Nevoux, M., Delord, K. and Weimerskirch, H. 2012. Effects of climate change and fisheries bycatch on Southern Ocean seabirds: a review. – Mar. Ecol. Prog. Ser. 454: 285–307.

Barras, A. G., Blache, S., Schaub, M. and Arlettaz, R. 2021. Variation in demography and life-history strategies across the range of a declining mountain bird species. – Front. Ecol. Evol. 9: 780706.

Beal, M. et al. 2021. Global political responsibility for the conservation of albatrosses and large petrels. – Sci. Adv. 7: eabd7225.
Bentley, L. K., Phillips, R. A., Carpenter-Kling, T., Crawford, R. J., Cuthbert, R. J., Delord, K., Dilley, B. J., Makhado, A. B., Miller, P. I. and Oppel, S. 2024. Habitat preferences of *Phoebetria* albatrosses in sympatry and allopatry. - J. Biogeogr. 51: 1986-1998.

Berruti, A. 1979. The breeding biologies of the sooty albatrosses *Phoebetria fusca* and *P. palpebrata*. - Emu 79: 161-175.

Blanck, A. and Lamouroux, N. 2007. Large-scale intraspecific variation in life-history traits of European freshwater fish. - J. Biogeogr. 34: 862-875.

Bourret, V., Gamble, A., Tornos, J., Jaeger, A., Delord, K., Barbraud, C., Tortosa, P., Kada, S., Thiebot, J. B. and Thibault, E. 2018. Vaccination protects endangered albatross chicks against avian cholera. - Conserv. Lett. 11: e12443.

Bowler, D. E. et al. 2020. Mapping human pressures on biodiversity across the planet uncovers anthropogenic threat complexes. - People Nat. 2: 380-394.

Brisson-Curadeau, É., Elliott, K. and Bost, C.-A. 2023. Contrasting bottom-up effects of warming ocean on two king penguin populations. - Global Change Biol. 29: 998-1008.

Brooke, M. 2004. Albatrosses and petrels across the world. - Oxford Univ. Press.

Brownie, C., Hines, J. E., Nichols, J. D., Pollock, K. H. and Hestbeck, J. B. 1993. Capture-recapture studies for multiple strata including non-Markovian transitions. - Biometrics 49: 1173-1187.

Carpenter-Kling, T., Reisinger, R. R., Orgeret, F., Connan, M., Stevens, K. L., Ryan, P. G., Makhado, A. and Pistorius, P. A. 2020. Foraging in a dynamic environment: response of four sympatric sub-Antarctic albatross species to interannual environmental variability. - Ecol. Evol. 10: 11277-11295.

Cherel, Y., Jaeger, A., Carravieri, A., Jaquemet, S., Phillips, R. A., Wanless, R. M. and Richard, P. 2025. Trophic ecology of sooty albatross, segregating mechanisms from the congeneric light-mantled sooty albatross, and conservation implications. - Endang. Species Res. 57: 45-57.

Choquet, R., Lebreton, J., Gimenez, O., Reboulet, A. and Pradel, R. 2009a. U-CARE: utilities for performing goodness of fit tests and manipulating CApture-REcapture data. - Ecography 32: 1071-1074.

Choquet, R., Rouan, L. and Pradel, R. 2009b. Program E-surge: a software application for fitting multievent models. - In: Thomson, D. L., Cooch, E. G. and Conroy, M. J. (eds), Modeling demographic processes in marked populations. Environmental and ecological statistics. Springer: 845-865.

Ciasto, L. M. and Thompson, D. W. 2008. Observations of large-scale ocean-atmosphere interaction in the Southern Hemisphere. - J. Clim. 21: 1244-1259.

Clay, T. A., Small, C., Tuck, G. N., Pardo, D., Carneiro, A. P., Wood, A. G., Croxall, J. P., Crossin, G. T. and Phillips, R. A. 2019. A comprehensive large-scale assessment of fisheries bycatch risk to threatened seabird populations. - J. Appl. Ecol. 56: 1882-1893.

Cleeland, J. B., Pardo, D., Raymond, B., Tuck, G. N., McMahon, C. R., Phillips, R. A., Alderman, R., Lea, M.-A. and Hindell, M. A. 2021. Disentangling the influence of three major threats on the demography of an albatross community. - Front. Mar. Sci. 8: 578144.

Clutton-Brock, T. and Coulson, T. 2002. Comparative ungulate dynamics: the devil is in the detail. - Philos. Trans. R. Soc. B 357: 1285-1298.

Colchero, F., Jones, O. R., Conde, D. A., Hodgson, D., Zajitschek, F., Schmidt, B. R., Malo, A. F., Alberts, S. C., Becker, P. H. and Bouwhuis, S. 2019. The diversity of population responses to environmental change. - Ecol. Lett. 22: 342-353.

Corbeau, A., Collet, J., Orgeret, F., Pistorius, P. and Weimerskirch, H. 2021. Fine-scale interactions between boats and large albatrosses indicate variable susceptibility to bycatch risk according to species and populations. - Anim. Conserv. 24: 689-699.

Croxall, J. P., Butchart, S. H. M., Lascelles, B., Stattersfield, A. J., Sullivan, B., Symes, A. and Taylor, P. 2012. Seabird conservation status, threats and priority actions: a global assessment. - Bird Conserv. Int. 22: 1-34.

Delord, K., Barbraud, C., Bost, C. A., Cherel, Y., Guinet, C. and Weimerskirch, H. 2013. Atlas of top predators from French southern territories in the southern Indian Ocean. - CEBC-CNRS. doi:10.15474/AtlasTopPredatorsOI_CEBC.CNRS_FrenchSouthernTerritories

Dias, M. P., Martin, R., Pearmain, E. J., Burfield, I. J., Small, C., Phillips, R. A., Yates, O., Lascelles, B., Borboroglu, P. G. and Croxall, J. P. 2019. Threats to seabirds: a global assessment. - Biol. Conserv. 237: 525-537.

Evers, S. M., Knight, T. M. and Compagnoni, A. 2023. The inclusion of immediate and lagged climate responses amplifies the effect of climate autocorrelation on long-term growth rate of populations. - J. Ecol. 111: 1985-1996.

Fogt, R. L. and Marshall, G. J. 2020. The southern annular mode: variability, trends, and climate impacts across the Southern Hemisphere. - WIREs Clim. Change 11: e652.

Forchhammer, M. C. and Post, E. 2004. Using large-scale climate indices in climate change ecology studies. - Popul. Ecol. 46: 1-12.

Forchhammer, M. C., Post, E., Stenseth, N. C. and Boertmann, D. M. 2002. Long-term responses in arctic ungulate dynamics to changes in climatic and trophic processes. - Popul. Ecol. 44: 113-120.

Frederiksen, M., Edwards, M., Richardson, A. J., Halliday, N. C. and Wanless, S. 2006. From plankton to top predators: bottom-up control of a marine food web across four trophic levels. - J. Anim. Ecol. 75: 1259-1268.

Fromant, A., Collet, J., Vansteenberghe, C., Musseau, R., Filippi, D., Delord, K. and Barbraud, C. 2024. Fine-scale behaviour and population estimates of endangered sooty albatross suggest low exposure (while not excluding high sensitivity) to bycatch. - Mar. Ecol. Prog. Ser. 749: 181-192. https://doi.org/10.3354/meps14719

Gaillard, J.-M. and Yoccoz, N. G. 2003. Temporal variation in survival of mammals: a case of environmental canalization? - Ecology 84: 3294-3306.

Gaillard, J., Hewison, A. M., Klein, F., Plard, F., Douhard, M., Davison, R. and Bonenfant, C. 2013. How does climate change influence demographic processes of widespread species? Lessons from the comparative analysis of contrasted populations of roe deer. - Ecol. Lett. 16: 48-57.

Gales, R., Brothers, N. and Reid, T. 1998. Seabird mortality in the Japanese tuna longline fishery around Australia, 1988-1995. - Biol. Conserv. 86: 37-56.

Ganivet, E. 2020. Growth in human population and consumption both need to be addressed to reach an ecologically sustainable future. - Environ. Dev. Sustain. 22: 4979-4998.

Gasco, N. 2011. Contributions to marine science by fishery observers in the French EEZ of Kerguelen. - Cybium 35: 93-98.
Gianuca, D., Votier, S. C., Pardo, D., Wood, A. G., Sherley, R. B., Ireland, L., Choquet, R., Pradel, R., Townley, S. and Forcada, J. 2019. Sex-specific effects of fisheries and climate on the demography of sexually dimorphic seabirds. – *J. Anim. Ecol.* 88: 1366–1378.

Gibson, D., Riecke, T. V., Catlin, D. H., Hunt, K. L., Weithman, C. E., Koons, D. N., Karpanty, S. M. and Fraser, J. D. 2023. Climate change and commercial fishing practices codetermine survival of a long-lived seabird. – *Global Change Biol.* 29: 324–340.

Grosbois, V., Gimenez, O., Gaillard, J. M., Pradel, R., Barbraud, C., Clobert, J., Møller, A. P. and Weimerskirch, H. 2008. Assessing the impact of climate variation on survival in vertebrate populations. – *Biol. Rev.* 83: 357–399.

Guéry, L., Descamps, S., Pradel, R., Hanssen, S. A., Erikstad, K. E., Gabrielsen, G. W., Gilchrist, H. G. and Béty, J. 2017. Hidden survival heterogeneity of three common eider populations in response to climate fluctuations. – *J. Anim. Ecol.* 86: 683–693.

Harris, M. P., Anker-Nilssen, T., McCleery, R. H., Erikstad, K. E., Shaw, D. N. and Grosbois, V. 2005. Effect of wintering area and climate on the survival of adult Atlantic puffins *Fratercula arctica* in the eastern Atlantic. – *Mar. Ecol. Prog. Ser.* 297: 283–296.

Hecht Orzack, S. and Tuljapurkar, S. 2001. Reproductive effort in variable environments, or environmental variation is for the birds. – *Ecology* 82: 2659–2665.

Heerah, K., Dias, M., Delord, K., Oppel, S., Barbraud, C., Weimerskirch, H. and Bost, C. 2019. Important areas and conservation sites for a community of globally threatened marine predators of the southern Indian Ocean. – *Biol. Conserv.* 234: 192–201.

Hendry, A. P. 2017. Eco-evolutionary dynamics. – *Princeton Univ. Press*.

Hersbach, H. et al. 2020. The ERA5 global reanalysis. – *Q. J. R. Meteorol. Soc.* 146: 1999–2049.

Horswill, C., Matthiopoulos, J., Green, J. A., Meredith, M. P., Forcada, J., Peat, H., Preston, M., Trathan, P. N. and Ratcliffe, N. 2014. Survival in macaroni penguins and the relative importance of different drivers: individual traits, predation pressure and environmental variability. – *J. Anim. Ecol.* 83: 1057–1067.

Huang, H.-W. 2017. Distribution of seabirds bycatch of Taiwanese longline fleets in Southern Ocean between 2010 and 2016. – *CCSBT-ERS/1703/Info05*. https://www.ccsbt.org/system/files/ERSWG12_Info05_TW_DistributionOfSeabirdsBycatchInSouthernOcean.pdf

Huang, H. W. and Liu, K. M. 2010. Bycatch and discards by Taiwanese large-scale tuna longline fleets in the Indian Ocean. – *Fish. Res.* 106: 261–270.

Inchausti, P. and Ginzburg, L. R. 2009. Maternal effects mechanism of population cycling: a formidable competitor to the traditional predator–prey view. – *Philos. Trans. R. Soc. B* 364: 1117–1124.

IUCN 2024. The IUCN Red List of threatened species, ver. 2024-2. – www.iucnredlist.org.

Jaeger, A., Lebarbenchon, C., Bourret, V., Bastien, M., Lagadec, E., Thiebot, J.-B., Boulinier, T., Delord, K., Barbraud, C., Marteau, C., Dellagi, K., Tortosa, P. and Weimerskirch, H. 2018. Avian cholera outbreaks threaten seabird species on Amsterdam Island. – *PLoS One* 13: e0197291.

Jaggi, H., Zuo, W., Kentie, R., Gaillard, J.-M., Coulson, T. and Tuljapurkar, S. 2024. Density dependence shapes life-history tradeoffs in a food-limited population. – *Ecol. Lett.* 27: e14551.

Jenouvrier, S., Barbraud, C. and Weimerskirch, H. 2005. Long-term contrasted responses to climate of two Antarctic seabird species. – *Ecology* 86: 2889–2903.

Jenouvrier, S., Thibault, J., Viallefont, A., Vidal, P., Ristow, D., Mougin, J., Brichetti, P., Borg, J. J. and Bretagnolle, V. 2009. Global climate patterns explain range-wide synchronicity in survival of a migratory seabird. – *Global Change Biol.* 15: 268–279.

Jenouvrier, S., Péron, C. and Weimerskirch, H. 2015. Extreme climate events and individual heterogeneity shape life-history traits and population dynamics. – *Ecol. Monogr.* 85: 605–624.

Jouventin, P. and Weimerskirch, H. 1984. L'albatros fuligineux à dos sombre *Phoebetria fusca*, exemple de stratégie d'adaptation extrême à la vie pélagique. – *Rev. Ecol. (Terre Vie)* 39: 401–429.

Jouventin, P. and Weimerskirch, H. 1988. Demographic strategies of southern albatrosses. – *Proc. Int. Ornithol. Cong.* 19: 857–865.

Kawecki, T. J. and Ebert, D. 2004. Conceptual issues in local adaptation. – *Ecol. Lett.* 7: 1225–1241.

Kendall, W. L. 2009. One size does not fit all: adapting mark-recapture and occupancy models for state uncertainty. – In: Thomson, D. L., Cooch, E. G. and Conroy, M. J. (eds), Modeling demographic processes in marked populations, vol. 3. Springer, pp. 765–780.

Koons, D. N., Pavard, S., Baudisch, A. and Metcalf, J. E. 2009. Is life-history buffering or lability adaptive in stochastic environments? – *Oikos* 118: 972–980.

Krishnamurti, T. N. and Bhalme, H. 1976. Oscillations of a monsoon system. Part I. Observational aspects. – *J. Atmos. Sci.* 33: 1937–1954.

Lebreton, J. D., Choquet, R. and Gimenez, O. 2012. Simple estimation and test procedures in capture–mark–recapture mixed models. – *Biometrics* 68: 494–503.

Lewison, R. L., Crowder, L. B., Wallace, B. P., Moore, J. E., Cox, T., Zydelis, R., McDonald, S., DiMatteo, A., Dunn, D. C. and Kot, C. Y. 2014. Global patterns of marine mammal, seabird, and sea turtle bycatch reveal taxa-specific and cumulative megafauna hotspots. – *Proc. Natl Acad. Sci. USA* 2014: 201318960.

Lovenduski, N. S. and Gruber, N. 2005. Impact of the Southern Annular Mode on Southern Ocean circulation and biology. – *Geophys. Res. Lett.* 32: 2005GL022727.

Marchant, S. and Higgins, P. J. 1990. Handbook of Australian, New Zealand and Antarctic birds. – *Oxford Univ. Press*.

Martin, A., Chazeau, C., Gasco, N., Duhamel, G. and Pruvost, P. 2021. Data curation, fisheries, and ecosystem-based management: the case study of the Pecheker database. – *Int. J. Digit. Curation* 16: 31–31.

McNamara, J. M. and Houston, A. I. 1996. State-dependent life histories. – *Nature* 380: 215–221.

Meeker, E. 2021. Historical variability and regional influence of the Mascarene High in austral winter. – *Unpublished report. Woods Hole Oceanographic Institution*.

Milot, E., Béchet, A. and Maris, V. 2020. The dimensions of evolutionary potential in biological conservation. – *Evol. Appl.* 13: 1363–1379.

Morioka, Y., Takaya, K., Behera, S. K. and Masumoto, Y. 2015. Local SST impacts on the summertime Mascarene High variability. – *J. Clim.* 28: 678–694.

Morrison, C. and Hero, J. 2003. Geographic variation in life-history characteristics of amphibians: a review. – *J. Anim. Ecol.* 72: 270–279.

Nel, D. C., Taylor, F. E., Ryan, P. G. and Cooper, J. 2003. Population dynamics of the wandering albatross *Diomedea exulans* at
Marion Island: longline fishing and environmental influences.
– Afr. J. Mar. Sci. 25: 503–517.

Nevoux, M., Forcada, J., Barbraud, C., Croxall, J. and Weimerskirchi, H. 2010. Bet-hedging response to environmental variability, an intraspecific comparison. – Ecology 91: 2416–2427.

Nichols, J. T. and Murphy, R. C. 1914. A review of the genus *Phoebetria*. – *Auk* 31: 525–534.

Ohishi, S., Sugimoto, S. and Hanawa, K. 2015. Zonal movement of the Mascarene High in austral summer. – *Clim. Dynam.* 45: 1739–1745.

Orgeret, F., Thiebault, A., Kovacs, K. M., Lydersen, C., Hindell, M. A., Thompson, S. A., Sydeman, W. J. and Pistorius, P. A. 2022. Climate change impacts on seabirds and marine mammals: the importance of study duration, thermal tolerance and generation time. – *Ecol. Lett.* 25: 218–239.

Oro, D. 2014. Seabirds and climate: knowledge, pitfalls, and opportunities. – *Front. Ecol. Evol.* 2: 79.

Oro, D., Freixas, L., Bartrina, C., Míguez, S. and Torre, I. 2024. Direct and indirect effects of climate and seed dynamics on the breeding performance of a seed predator at the distribution edge. – *Ecol. Evol.* 14: e70104.

Pardo, D., Weimerskirch, H. and Barbraud, C. 2013. When celi-
bacy matters: incorporating non-breeders improves demo-
graphic parameter estimates. – PLoS One 8: e60389.

Pardo, D., Forcada, J., Wood, A. G., Tuck, G. N., Ireland, L., Pradel, R., Croxall, J. P. and Phillips, R. A. 2017. Additive effects of climate and fisheries drive ongoing declines in multi-ple albatross species. – Proc. Natl Acad. Sci. USA 114: E10829-E10837.

Parmesan, C. 2006. Ecological and evolutionary responses to recent climate change. – *Annu. Rev. Ecol. Evol. Syst.* 37: 637–669.

Pennycuick, C. J. 2002. Gust soaring as a basis for the flight of petrels and albatrosses (Procellariiformes). – *Avian Sci.* 2: 1–12.

Post, E., Bhatt, U. S., Bitz, C. M., Brodie, J. F., Fulton, T. L., Heb-
blewhite, M., Kerby, J., Kutz, S. J., Stirling, I. and Walker, D.
A. 2013. Ecological consequences of sea-ice decline. – *Science*
341: 519–524.

Pradel, R. 2005. Multievent: an extension of multistate capture-recapture models to uncertain states. – *Biometrics* 61: 442–447.

Quéroué, M., Barbraud, C., Barraquand, F., Turek, D., Delord, K., Pacoureau, N. and Gimenez, O. 2021. Multispecies integrated population model reveals bottom-up dynamics in a seabird predator-prey system. – *Ecol. Monogr.* 91: e01459.

Reed, T. E., Schindler, D. E. and Waples, R. S. 2011. Interacting effects of phenotypic plasticity and evolution on population persistence in a changing climate. – *Conserv. Biol.* 25: 56–63.

Reid, K., Baker, G. B. and Delord, K. 2023. Ecological risk assessment in the southern Indian Ocean: towards better seabird bycatch mitigation. – *Aquat. Conserv. Mar. Freshw. Ecosyst.* 33: 1218–1228.

Remya, P. G., Kumar, B. P., Srinivas, G. and Nair, T. M. B. 2020. Impact of tropical and extra tropical climate variability on Indian Ocean surface waves. – *Clim. Dyn.* 54: 4919–4933.

Rivalan, P., Barbraud, C., Inchausti, P. and Weimerskirch, H. 2010. Combined impact of longline fisheries and climate on the persistence of the Amsterdam albatross. – *Ibis* 152: 6–18.

Rolland, V., Weimerskirch, H. and Barbraud, C. 2010. Relative influence of fisheries and climate on the demography of four albatross species: fisheries, climate and albatross demography. – *Global Change Biol.* 16: 1910–1922.

Rollinson, D., Wanless, R., Makhado, A. and Crawford, R. 2016. A review of seabird bycatch mitigation measures, including

experimental work, within South Africa's tuna longline fishery.
– IOTC working document SC19–13, 9.

Sæther, B.-E. and Bakke, Ø. 2000. Avian life history variation and contribution of demographic traits to the population growth rate. – *Ecology* 81: 642–653.

Sæther, B.-E., Coulson, T., Grøtan, V., Engen, S., Altwegg, R., Armitage, K. B., Barbraud, C., Becker, P. H., Blumstein, D. T. and Dobson, F. S. 2013. How life history influences population dynamics in fluctuating environments. – *Am. Nat.* 182: 743–759.

Sauser, C., Angelier, F., Blévin, P., Chastel, O., Gabrielsen, G., Jouanneau, W., Kato, A., Moe, B., Ramírez, F. and Tartu, S. 2023. Demographic responses of Arctic seabirds to spring sea-ice variations. – *Front. Ecol. Evol.* 11: 1107992.

Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. and Walker, B. 2001. Catastrophic shifts in ecosystems. – *Nature* 413: 591–596.

Scholer, M. N., Strimas-Mackey, M. and Jankowski, J. E. 2020. A meta-analysis of global avian survival across species and latitude. – *Ecol. Lett.* 23: 1537–1549.

Schreiber, E. A. and Burger, J. 2002. Biology of marine birds. – CRC Press.

Schultner, J., Kitaysky, A. S., Gabrielsen, G. W., Hatch, S. and Bech, C. 2013. Differential reproductive responses to stress reveal the role of life-history strategies within a species. – *Proc. R. Soc. B* 280: 20132090.

Sexton, J. P., McIntyre, P. J., Angert, A. L. and Rice, K. J. 2009. Evolution and ecology of species range limits. – *Annu. Rev. Evol. Syst.* 40: 415–436.

Skalski, J. R. 1996. Regression of abundance estimates from mark-recapture surveys against environmental covariates. – *Can. J. Fish. Aquat. Sci.* 53: 196–204.

Stenseth, N. Chr., Ottersen, G., Hurrell, J. W., Mysterud, A., Lima, M., Chan, K., Yoccoz, N. G. and Ådlandsvik, B. 2003. Review article. Studying climate effects on ecology through the use of climate indices: the North Atlantic Oscillation, El Niño Southern Oscillation and beyond. – *Proc. R. Soc. B* 270: 2087–2096.

Sun, R., Rouby, E., Weimerskirch, H., Barbraud, C., Delord, K., Ummenhofer, C. C. and Jenouvrier, S. 2025. Subtropical anti-cyclone impacts life-history traits of a marine top predator. – *bioRxiv* https://doi.org/10.1101/2025.02.27.640559.

Swab, R. M., Regan, H. M., Matthies, D., Becker, U. and Bruun, H. H. 2015. The role of demography, intra-species variation, and species distribution models in species' projections under climate change. – *Ecography* 38: 221–230.

Sydeman, W. J., Poloczanska, E., Reed, T. E. and Thompson, S. A. 2015. Climate change and marine vertebrates. – *Science* 350: 772–777.

Sydeman, W. J. et al. 2021. Hemispheric asymmetry in ocean change and the productivity of ecosystem sentinels. – *Science* 372: 980–983.

Terauds, A., Gales, R. and Alderman, R. 2005. Trends in numbers and survival of black-browed (*Thalassarche melanophrys*) and grey-headed (*T. chrysostoma*) albatrosses breeding on Macquarie Island. – *Emu* 105: 159–167.

Terauds, A., Gales, R., Baker, B. and Alderman, R. 2006. Population and survival trends of wandering albatrosses *Diomedea exulans* breeding on Macquarie Island. – *Emu* 106: 211–218.

Thomson, R. B., Alderman, R. L., Tuck, G. N. and Hobday, A. J. 2015. Effects of climate change and fisheries bycatch on shy albatross (*Thalassarche cauta*) in southern Australia. – *PLoS One* 10: e0127006.
Tuck, G. N., Polacheck, T. and Bulman, C. M. 2003. Spatio-temporal trends of longline fishing effort in the Southern Ocean and implications for seabird bycatch. – *Biol. Conserv.* 114: 1–27.

Urban, M. C., Bocedi, G., Hendry, A., Mihoub, J.-B., Pe’Er, G., Singer, A., Bridle, J., Crozier, L., De Meester, L. and Godsoe, W. 2016. Improving the forecast for biodiversity under climate change. – *Science* 353: aad8466.

Usui, T., Lerner, D., Eckert, I., Angert, A. L., Garroway, C. J., Hargreaves, A., Lancaster, L. T., Lessard, J.-P., Riva, F. and Schmidt, C. 2023. The evolution of plasticity at geographic range edges. – *Trends Ecol. Evol.* 38: 831–842.

Van De Pol, M., Brouwer, L., Brooker, L. C., Brooker, M. G., Colombelli-Négrel, D., Hall, M. L., Langmore, N. E., Peters, A., Pruett-Jones, S., Russell, E. M., Webster, M. S. and Cockburn, A. 2013. Problems with using large-scale oceanic climate indices to compare climatic sensitivities across populations and species. – *Ecography* 36: 249–255.

Ventura, F., Lukacs, P. M., Granadeiro, J. P., Matano, R. and Catry, P. 2021. Demographic responses to environmental change of the black-browed albatross, sentinel of the Patagonian Shelf large marine ecosystem. – *Mar. Ecol. Prog. Ser.* 668: 107–120.

Véran, S., Gimenez, O., Flint, E., Kendall, W. L., Doherty Jr, P. F. and Lebreton, J.-D. 2007. Quantifying the impact of longline fisheries on adult survival in the black-footed albatross. – *J. Appl. Ecol.* 44: 942–952.

Wakefield, E. D., Phillips, R. A., Matthiopoulos, J., Fukuda, A., Higuchi, H., Marshall, G. J. and Trathan, P. N. 2009. Wind

field and sex constrain the flight speeds of central-place foraging albatrosses. – *Ecol. Monogr.* 79: 663–679.

Waugh, S. M., Weimerskirch, H., Moore, P. J. and Sagar, P. M. 1999. Population dynamics of black-browed and grey-headed albatrosses *Diomedea melanophrys* and *D. Chrysostoma* at Campbell Island, New Zealand, 1942–96. – *Ibis* 141: 216–225.

Weimerskirch, H., Jouventin, P. and Stahl, J. C. 1986. Comparative ecology of six albatross species breeding on the Crozet Islands. – *Ibis* 128: 195–213.

Weimerskirch, H., Clobert, J. and Jouventin, P. 1987. Survival in five southern albatrosses and its relations with their life history. – *J. Anim. Ecol.* 56: 1043–1056.

Weimerskirch, H., Guionnet, T., Martin, J., Shaffer, S. A. and Costa, D. P. 2000. Fast and fuel efficient? Optimal use of wind by flying albatrosses. – *Proc. R. Soc. B* 267: 1869–1874.

Weimerskirch, H., Delord, K., Barbraud, C., Le Bouard, F., Ryan, P. G., Fretwell, P. and Marteau, C. 2018. Status and trends of albatrosses in the french southern territories, western Indian Ocean. – *Polar Biol.* 41: 1963–1972.

Weimerskirch, H., Louzao, M., de Grissac, S. and Delord, K. 2012. Changes in wind pattern alter albatross distribution and life-history traits. – *Science* 335: 211–214.

Williams, B., Nichols, J. and Conroy, M. 2002. Analysis and management of animal populations. – *Academic Press*.

Zhao, Y., Wen, Z., Li, X., Chen, R. and Chen, G. 2023. Meridional variation of the Mascarene High and atmospheric transient eddy dynamical forcing over the southern Indian Ocean in axial winter. – *J. Clim.* 36: 6937–6950.