Recents Projects 

Results Of Prior NSF Support
Broader Significance: Background, Publications, Presentations
Intellectual Merit
Field Plan and Role of Collaborators
Broader Significance of Proposed Project
Literature Cited, including Papers resulting from this Research

Current Project – 2020

Technical description:
Leveraging 25 years of data on marked individuals from two Adélie penguin colonies in the Ross Sea, combined with new biologging tags that track detailed penguin foraging efforts and environmental conditions, researchers will accomplish three major goals: 1) assess the quality of natal conditions by determining how environmental conditions, relative prey availability, and diet composition influence parental foraging behavior, chick provisioning, and fledging mass; 2) determine the spatial distribution and foraging behavior of juvenile Adélie penguins and the relative influence of natal versus post-fledging environmental conditions on their survival; and 3) determine the role of natal and post-fledging conditions in shaping individual life history traits and colony growth. Data from several types of penguin-borne biologging devices will be used to provide multiple lines of evidence for how early-life conditions and penguin behavior relate to penguin energetics and population size. This study is the first to integrate salinity, temperature, light level, depth, accelerometry, video loggers, and GPS data with longitudinal demographic information, providing an unprecedented ability to understand how penguins use the environment and enabling new insights from previously collected data. Changes in salinity due to increased glacial melt have important implications for sea ice formation, ocean circulation and productivity of the Southern Ocean, and potentially global temperature change. The penguin-borne sensors deployed in this study will support the NSF Office of Polar Programs priority: How does society more efficiently observe and measure the polar regions? It represents only the second study to track juvenile Adélie penguins at sea, the first in the Ross Sea region, the first with substantial sample sizes, and the first to assess juvenile survival rates directly, integrating early life factors and environmental conditions to better understand colony growth trajectories.

Non Technical description:
Polar regions are experiencing some of the most dramatic effects of climate change resulting in large-scale changes in sea ice cover. Despite this, there are relatively few long-term studies on polar species that evaluate the full scope of these effects. Over the last two decades, this team has conducted globally unique demographic studies of Adélie penguins in the Ross Sea, Antarctica, to explore several potential mechanisms for population change. This five-year project will use penguin-borne sensors to evaluate foraging conditions and behavior and environmental conditions on early life stages of Adélie penguins. Results will help to better understand population dynamics and how populations might respond to future environmental change. To promote STEM literacy, education and public outreach efforts will include multiple activities. The PenguinCam and PenguinScience.com website (impacts of >1 million hits per month and use by >300 classrooms/~10,000 students) will be continued. Each field season will also have ‘Live From the Penguins’ Skype calls to classes (~120/season). Classroom-ready activities that are aligned with Next Generation Science Standards will be developed with media products and science journal papers translated to grade 5-8 literacy level. The project will also train early career scientists, postdoctoral scholars, graduate students and post-graduate interns. Finally, in partnership with an Environmental Leadership Program, the team will host 2-year Roger Arliner Young Conservation Fellow, which is a program designed to increase opportunities for recent college graduates of color to learn about, engage with, and enter the environmental conservation sector.

Recent Projects

COLLABORATIVE: ADÉLIE PENGUIN RESPONSE TO CLIMATE CHANGE IN THE ROSS SEA REGION – A FULL LIFECYCLE APPROACH

Principal investigators: D Ainley, G Ballard, K Dugger, A Lescroël, A Schmidt

RESULTS OF PRIOR NSF SUPPORT

OPP 9526865, 9814882, 0125608, 0440463, ANT- 0944411 (current study) — COLLABORATIVE: Adélie penguin response to climate change at the individual, colony and metapopulation levels. $3,300,000 (current 5 yrs = $1,707,000). 20 years: 23 August 1996-31 July 2016. Co-PIs: D. Ainley, G. Ballard, L. Ballance, K. Dugger, N. Nur, G. Rau, M. Massaro, A. Lescroël, S. Jenouvrier, and C. Ribic. Collaborators: CNRS France (D. Grémillet), Canterbury University (A. Varsani), LandCare Research NZ (P. Lyver, P. Wilson), University of Arizona (J. Russell), University of Minnesota (D. Siniff, M. LaRue), Moss Landing Marine Lab (S. Kim), NASA (C. Parkinson), Virginia Institute of Marine Science (W. Smith) and Stanford University (K. Arrigo), all having been funded independently. Please see Broader Significance for a review of products, including published papers (available, too, in respective co-PI CVs and designated with an asterisk (*) in Literature Cited.

INTELLECTUAL MERIT (and Background for Current Proposal)
We have completed 19 (of 20) NSF-funded field seasons to explain: 1) how demographic factors explain population trends in the southernmost Adélie Penguin range (Ross Sea); 2) why high variability in rate of population change and colony size; and 3) how foraging ecology is related to these patterns (Fig. 1). We chose a metapopulation of 4 colonies on Ross and Beaufort islands that together comprise 8% of this species’ world population and range in size by three orders of magnitude: Cape Crozier (~250,000 pairs) to Cape Bird and Beaufort Island (~60,000) and Cape Royds (~2,000). The Crozier colony doubled in size during our study and is now likely the largest for this species. We compared the reproductive and foraging effort and success of penguins of known age and history at these colonies and continue to estimate and model vital rates (Fig. 2). A sub-goal has been to determine the longevity of this species, and the possible influence of senescence on life history, and we propose herein to continue toward this goal. While the current longevity record is 20 y (Ainley 2002), a number of individuals banded at the onset of our study (19 y ago) are still breeding, about to equal that previous limit. We also found that a small minority of breeders are always successful, and have been attempting to figure out why, but realize we still have much more to learn (see below). Finally, we hypothesize that we are seeing effects of the growing sea ice season and extent documented for the Ross Sea region (Turner et al. 2009, Stammerjohn et al. 2012) on Adélie penguin demography, and wish to explore this question further.

Fig. 1. Changes in breeding numbers of Adélie penguins in the Beaufort/Ross Island metapopulation, 1981-2012. Colonies differ in size by successive orders of magnitude: Royds smallest (currently 3083 pair), Cape Bird (75,696), Beaufort (63,760), Crozier largest (272,343 pair). Mega-icebergs that affected growth especially of Cape Royds were present 2001-05 (see below). From Lyver et al. 2014.

Fig. 2A. Annual survival rates for breeding, adult Adélie penguins at 3 colonies in the Ross Sea during 1996 – 2006 (from Dugger et al. 2010). Estimates are from best model including different survival rates for the Cape Royds birds compared to Cape Bird and Cape Crozier and a general, additive time effect (ϕ (B=C, R + t).

Fig. 2B. Age-specific survival of Pre-breeders & the probability of transitioning from a Pre-breeder to a Breeder averaged over years and applied to a hypothetical group of 1000 female Adélie penguin chicks (starting at age 0) for Cps Royds Bird & Crozier (from Dugger et al. ms1).

Based on a retrospective analysis of colony-size data, it appeared that changes in sea-ice extent and especially prevalence of the Ross Sea Polynya, as a function of climate factors, was responsible, in part (30% variation explained), for the population trends during the 1980s (Wilson et al. 2001, Ainley et al. 2005). However, we subsequently concluded that the extraction of 15,000 minke whales (direct competitors of penguins; Ainley et al. 2006, ms1) from the Ross Sea sector during the 1970-80’s was likely contributing to the penguin increase (Ainley et al. 2007, 2009a). As the whale population recovered upon cessation of whaling, penguin increase ceased. Now the colonies are increasing again, and we hypothesize that this is due to competitive release as large Antarctic toothfish (Dissostichus mawsoni), which eat the same fish prey, are removed from the system (Lyver et al. 2014; Fig. 1, see below).

Seeking to understand possible intraspecific competition as one factor in colony size change, we compared the ‘quality of life’ of penguins at colonies of different size. We developed banded, known-age, known-history populations at each colony, a process taking several years owing to the long life of the species and delayed recruitment (5-6 y). We helped pioneer the use of implanted RFID (Radio Frequency Identification) tags and computerized weighbridges (WB) to assess adult mass, meal size fed to chicks, and feeding frequency (Fig. 3). Reliable WB’s have been deployed at 3 colonies for 9-19 seasons. We found that penguins are optimal foragers on trips ≤2 d (food loads increase with trip length) but as trips increase >2 d in order to find quality prey patches farther away (Ford et al. 2015), meal size, adult body mass, and chick growth decrease (Ainley et al. 1998, 2004; Ballard et al. 2001, 2010a). Further, foraging behavior varies by sex, body size, and individual quality (“BQI”: an index related to the propensity of a given individual to breed successfully relative to the rest of the population) as well as sea ice conditions (Ainley et al. 1998; Ballard et al. 2001, 2010a; Lescroël et al. 2009, 2010, 2014; Fig. 3). Longer trip times, lighter adult mass, and higher energy expenditure occur at the largest colony, as well as an expanding foraging area at this colony excluding individuals from other colonies as the chick-provisioning period progresses, indicating intra-specific competition within and between colonies (Ainley et al. 1998, 2004; Ballance et al. 2009; Ford et al. 2015). Trip times were also related to the number of minke and killer whales in the penguins’ foraging area, through inter-specific competition for food (more whales, longer trips and prey switch; Ainley et al. 2006, ms1;Ford et al. 2015). Finally, coupling the above results with IPCC climate model-projected changes to the Ross Sea environment, we made qualitative predictions for how Adélie Penguins would respond over the next 50 years (Ainley et al. 2010).

Fig. 3. Weighbridge-derived data (1997 – 2010) showing catch per unit effort (CPUE, actually provisioning efficiency) as affected by sea ice cover (SIC) in the Ross Sea Polynya marginal ice zone (MIZ): A) CPUE under usual conditions (1997-2000, 2006-2010), and B) CPUE when mega-icebergs, representing an extreme climatic event, altered the MIZ (2001-2005), i.e. phenotypic plasticity was dampened. Thick lines (purple = female, blue = male) represent average CPUE; thin lines are 95% highest priority density intervals (from Lescroël et al. 2014; see also Dugger et al. 2014).

We assessed breeding efficacy by measuring foraging effort, chick growth, breeding success, numbers of breeding pairs, and breeder survival (Ainley et al. 1998, 2004; Ballard et al. 2001; Lescroël et al. 2009; Dugger et al. 2006, 2010, 2014, ms1). Chick mass was inversely related to colony size (Whitehead et al. 2015), but breeding success did not vary among colonies during typical years (Fig. 4; Dugger et al. 2014). Survival of breeding adults was lower at Royds compared to Bird and Crozier across all age classes (Dugger et al. 2010, ms1; Fig. 2), which may reflect colony-based differences in wintering areas or migration chronologies (Ballard et al. 2010b; Fig. 5). We also found that age-at-1st-reproduction for birds hatched at Bird and Crozier was on average later than at Royds (6.8 vs. 6.2 y; Fig. 4; Dugger et al. ms1) and that emigration rates varied in accord with environmental conditions (Dugger at al. 2010), and in some cases, nesting habitat availability (LaRue et al. 2013).

Fig. 4 (A) Predicted chick mean mass (gr; with 95% CI) at 5 wks post-peak hatch from best model plotted against colony size; (B) mean number of chicks per pair (with 95% CI) at Cps Crozier and Royds, when icebergs were (2001-2005) and were not present (1997-2000, 2006-2010); from Dugger et al. 2014.

To complement the WB, we extensively deployed instruments of various types to quantify foraging behavior (radios, tdrs, satellite tags, accelerometers), especially employing penguins of known age and history. We assessed seasonal, regional and annual variation in diet, including the role of intra- and interspecific competition for prey (Ainley et al. 2003, 2006, ms1), as well as the role of competition in affecting foraging range and diving behavior (Ainley et al. 2004, ms1; Ford et al. 2015). Sea ice conditions affected foraging (Ainley et al. 1998, Lescroël et a. 2010, 2014), and some penguins, (i.e. those of high BQI) exhibited more plastic foraging behavior to allow them to cope with variable conditions (Lescroël et al. 2010). Although Adélie penguins certainly exhibit central-place-foraging, in order to cope with conditions, they often violated certain of the classical assumptions of CPF theory (Ford et al. 2015). To quantify foraging behavior relative to prey availability we deployed an AUV glider in 2012, finding that penguins do deplete prey in surface waters and near the colony, thus forcing longer trips and deeper diving (cf. Ford et al. 2015, Ainley et al. ms1; NSF grant ANT 1141948, $79,286).

We have also used geolocation (Global Location Sensing, or GLS) tags to show that during winter penguins from Ross Island move north of the Antarctic Circle to remain in the outer reaches of the pack ice and to forage where there is some light during the day (Ballard et al. 2010b; also Ainley & Ballard 2011; Fig. 5). These findings are consistent with those of Wilson et al. (2001) — in winters of unusually extensive sea ice, penguins were forced to less productive waters north of the southern boundary of the Antarctic Circumpolar Current (dotted line, Fig. 5), which potentially reduced their survival.

Fig. 5. Locations during mid-winter of penguins from Crozier (yellow) and Royds (blue) colonies. Having departed the colony a month earlier (early Feb) to molt in the eastern Ross Sea, Crozier penguins became entrained in the Ross Gyre as the sea ice field grew, eventually traveling clockwise to arrive again in the eastern Ross Sea by September. Royds penguins, which molt at the colony and so did not migrate until late Feb, were advected northwestward by the winds and ice. Royds penguins in spring had to migrate against the ice drift, thus explaining their later return to the colony in spring. From Ballard et al. (2010b)

During our study’s middle years large icebergs grounded adjacent to Ross Island (C-16, 10 x 45 km; B-15, 40 x 160 km; Fig. 6 Arrigo et al. 2002). They formed a barrier 165 km long (~1.5o latitude) and 50m high, and changed between-colony movement patterns (i.e., altered philopatry; Shepherd et al. 2005, LaRue et al. 2013). Under this unparalleled “natural experiment” (“extreme” and “rapid climate change” event; Emslie et al. 2003, Lescroël et al. 2014, Dugger et al. 2010, 2014), we found that 1) Adélie Penguins occasionally relax otherwise extremely high philopatry (Fig. 7A; Dugger et al. 2010); 2) exhibit poor reproductive success, decreased breeding population size and decreased breeding propensities at capes Royds, Bird and Crozier due to more challenging conditions in local waters (Dugger et al. 2010, 2014; Whitehead et al. 2015; Fig. 2); and 3) the penguin movement resulting from such events, which probably occur 2X per millennium during interglacial periods (D. Macayeal, pers. comm.), indicates a mechanism for the species’ genetic homogeneity (Roeder et al. 2001), and the rate of DNA microevolution, at least over the past 6,000 yr (Shepherd et al. 2005). The long-term demographic effects of this event are just beginning to be realized (Fig. 7B; Dugger et al. 2010, ms1), but it is clear that a greater understanding will be important in gauging Adélie penguin response to climate change at the within-colony and metapopulation scales (LaRue et al. 2013).

Fig. 6. The icebergs B-15 and C-16 as they were positioned between February 2001 and January 2003. During 2003, B-15 broke into three pieces, which remained just slightly separated in the same configuration until June 2006. McMurdo Sound, which lies to the west of Ross Island, is covered by fast ice in this image. This fast ice, which normally breaks out to leave all of the Sound north of Cape Royds open, was present to varying degrees during the residence of the icebergs. Extensive fast ice forces the penguins to exert increased effort and changes the propensity of individuals to occupy, visit or recruit to and among the 3 western colonies depending on relative access to open water.

Fig. 7 (A) Altered movement probabilities of breeding penguins with (2002-2005) and without iceberg influence (1996-2001, 2006). (B) Proportion of 4-yr olds out of total number seen 2000 – 2007. See LaRue et al. 2013.

Finally, the Ross Sea Antarctic toothfish fishery, established in 1996, reached harvest quotas by 2001 (Ainley & Pauly 2013), and began to remove a potentially major penguin trophic competitor. Both penguins and toothfish prey heavily on Antarctic silverfish (Pleuragramma antarcticum), and with fewer large competing toothfish around Ross Island (Ainley et al. 2012) perhaps not coincidentally, penguin populations are growing dramatically (Lyver et al. 2014; Fig. 1). We have also been investigating possible effects of removing toothfish on cetaceans and seals, which also are potential penguin competitors (Ainley et al. 2009b, Ainley & Ballard 2012, Ainley & Siniff 2009, Kim et al. 2011), and have participated heavily in attempts to ensure a science-based approach to fisheries management in the Ross Sea, which included organizing three workshops (one NSF funded: PEHS-1237403, $37,000, 2012), and submitting two major reports to CCAMLR, one regarding mesopredator use of the Ross Sea (Ballard et al. 2012).

BROADER SIGNIFICANCE (more Background to Current Study)

Besides hosting a ‘Teacher Experience in Antarctic’ participant during 1999, and training12 pre-or mid-graduate school interns (generally 1/y), two post-docs, an MSc and two doctoral students, our contributions to the scientific and popular literature have been:
Publications. Thus far, we have produced 71 peer-reviewed publications, including 2 books (1 scientific, 1 educational); 26 peer-reviewed publications and 13 reports to CCAMLR and IWC. Publications from this project are listed with (*) in the Literature Cited; available at www.penguinscience.com (under Research).
Presentations. We have presented papers at many meetings, including SCAR Open Science (2011, 2013, 2014), 7th – 9th International Penguin Conferences (2004-2011), Pacific Seabird Group Annual Meetings (2010, 2012, 2015), 2014 Joint Meeting of the AOU, COS and SCO, and Gordon Conferences (2009, 2013). We have also been featured on CBS NEWS (Sunday Morning, The Early Show, CBS Evening News), CNN (CNN Presents) and TV1 NZ (Sunday), as well as Time Magazine, Time Magazine for Kids, and Science News. We’ve done a few dozen radio interviews, including NPR All Things Considered, regarding climate change, the big icebergs, and the state of the Ross Sea, for programs in the U.S., U.K., N.Z. and Australia. We’ve been featured in three documentary films, Encounters at the Edge of the World (Herzog, Academy Award nomination), Last Ocean (Young, 15 film festival awards), and Thin Ice (Singleton & Lamb, 2 awards); segments watched >40,000 times on YouTube.

Second, we’ve made four short webisodes (posted on the website) highlighting aspects of our research. These have been watched more than 30,000 times, so interest continues.

Third, we have devised downloadable educational materials geared to 5th-8th graders on the
Penguin Science website (www.penguinscience.com), with our activities/results summarized as follows: a) educational materials, plus other items, increased ‘hits’ to our website from 200/mo in 2006 to >1M/mo by 2007 and continuing into 2014 (~1.5M hits); b) >50,000 school children have prepared a drawing of “penguins and climate change” on a post-card, which they mailed to us in Antarctica, stamped and postmarked, there to be returned to the students; c) 143-300 classrooms per year were engaged in an educational program related to penguins and climate change through the website’s Field Book; d) competitions among middle school classes to design a flag, each of which (~75 flags thus far) have been flown at Cape Royds in view to the world via the in-colony PenguinCam; and e) PowerPoint presentations delivered online, ~80 to date, including, on occasion, simultaneously to classrooms on five continents.

PROPOSED STUDY INTELLECTUAL MERIT

The effects of climate change are currently most dramatic at the poles, and in Antarctica both the marine and terrestrial habitats critical to population change in the Adélie penguin are being affected (cf. Ducklow et al. 2007, Trivelpiece et al. 2011, Fraser et al. 2013). Between 1979 and 2010, the “sea ice season” (SIS; annual period between advance and retreat of sea ice; Stammerjohn et al. 2012), has declined by 3 months in the Antarctic Peninsula region but, along with increased sea-ice extent (SIE), has extended by two months in the Ross Sea region (Parkinson 2002, Stammerjohn et al. 2012, Holland & Kwok 2012). These changes are among the largest physical shifts so far associated with anthropogenic climate change and have had profound consequences for Antarctic ecosystems (Ducklow et al. 2007, Schofield et al. 2010, Lynch et al. 2012a, Sailley et al. 2013). Increased snow fall is negatively affecting Antarctic Peninsula penguins as well (Fraser et al. 2013). While atmospheric warming has many negative implications for Adélie penguins (Ducklow et al. 2007, Trivelpiece et al. 2011, Lynch et al. 2012), it can also have positive effects. For example, the retreat of glaciers and snowfields opens up potential ice-free nesting habitat and facilitates local colony expansion (LaRue et al. 2013) and colonization (Lynch & LaRue 2014). Adélie penguin breeding populations (“colonies”) have varied over the course of previous millennia, in some cases going locally extinct and then being recolonized in accord with changes in SIE and SIS, as well as availability of nesting habitat (Emslie et al. 2007, Li et al. 2014). Currently, Adélie penguin breeding populations are retreating southward from the northwest coast of the Antarctic Peninsula but expanding to the south (Lynch et al. 2012a). At the same time, populations in East Antarctica have been increasing slowly (Woehler et al. 2001, Ainley et al. 2010) and very rapidly in the southern Ross Sea (Lyver et al. 2014). While the reasons for the rapid growth in the Ross Sea are not clear, it is likely related to climate (e.g. Ainley et al. 2005, LaRue et al. 2013) as well as direct anthropogenic factors (e.g. changes in the food web due to fishing; Ainley et al. 2007, 2009a; Lyver et al. 2014). Understanding the mechanisms behind these population responses to unprecedented habitat changes is therefore especially timely.

While our previous work allowed us to understand how Adélie penguins respond to variability in sea ice conditions during the 3-4 month summer breeding season (e.g. Ainley et al. 1998, Ballard et al. 2010a, Lescroël et al. 2010, Lescroël et al. 2014, Dugger et al. 2014), we know much less about how sea ice conditions affect penguins during the remaining 8-9 mo of the year (Fig. 8). Information about influences on life-history events within the entire annual cycle is critical for predicting population-level responses to climate change (Winkler et al. 2002, Ådahl et al. 2006). Indeed, individuals of many species undertake long distance seasonal migrations to avoid deterioration in weather and food availability around breeding sites, but may face energetically challenging conditions along the migration route (Newton 2008) or during the rest of the non-breeding period (e.g., Fort et al. 2013, Reiertsen et al. 2014). Although often difficult to quantify, mortality that occurs in winter (e.g. Harris & Wanless 1996), or during migration, can have an important influence on population dynamics (Genovart et al. 2013, Sandvik et al. 2014), and it is believed that mortality during winter is significant for Adélie penguins (Ainley & DeMaster 1980, Ainley 2002). What happens to individuals during winter is a ‘black box’ for many species, but with the development and refinement of miniaturized geolocation loggers (Wilson 1992; hereafter “GLS tags”) over the past 20 years, it has become possible to follow individuals throughout the course of their annual cycle. Tags now available (GLS-TDR, recording geolocation, depth and temperature) allow us to evaluate the influence of the currently expanding SIE and SIS on winter behavior and survival for different segments of the penguin population (e.g. age classes, quality) in different phases of the annual cycle.

Conditions during one stage can also have longer-term, “carry-over” effects (COEs; Norris & Marra 2007) on subsequent stages of a species’ annual cycle. COEs are events and processes occurring in one season that affect individuals as they transition between seasons, altering later performance (Harrison et al. 2011). In many cases, COEs arise because of variation among individuals in their ability to access resources. This phenomenon potentially explains a large amount of variation in individual fitness, but so far has only been described in a limited number of species (Harrison et al. 2011). This is largely due to difficulties associated with tracking individuals throughout the annual cycle. The use of GLS-TDR tags will allow us to track individuals from one phase to another, collecting data on their movements and foraging behavior in winter, and incubation and chick provisioning/foraging behavior in summer.

Fig. 8: Annual life-cycle of the Adélie penguin, illustrating the principal variables of interest for this proposal. We will assess the effect of changing environmental conditions (blue boxes; extrinsic factors) on major life-history traits of Adélie penguins (black text) throughout their annual cycle, including carry-over effects from one stage (winter, mating, incubation, chick rearing, molt) to another, or from one cycle to another. We will also assess the effect of intrinsic factors (age, sex, experience, quality of the individuals) on these environment/life-history trait relationships. Links between variables are solid lines when demonstrated by published studies, dashed lines where hypothetical, and thicker dashed lines which we propose to evaluate.

While differential access to resources is undoubtedly a major factor influencing COEs and differences in breeding performance, mechanisms causing such differences are likely to be derived from a combination of intrinsic (i.e., an individual’s quality, modulated by age, experience or sex) and extrinsic (i.e., environment and habitat) factors (Daunt et al. 2014). Experimental manipulation (generally not possible for us due to Antarctic Conservation Act permit restrictions) has helped to demonstrate causality in either intrinsic or extrinsic drivers of COEs (Betini et al. 2013, Catry et al. 2013). An alternative approach involves longitudinal measurements across a range of conditions, whereby individuals act as their own controls. Based on our 19 years of data on the breeding success of known-age and known-history penguins (> 10,000 individuals resighted), we are in a strong position to assess the intrinsic quality of individuals, accounting for their age, experience and sex. Tracking these birds of known intrinsic quality throughout multiple annual cycles with GLS-TDR tags will allow us to establish the interplay between environmental and intrinsic factors in determining COEs for this species.

Not only are sea-ice conditions changing rapidly but the terrestrial nesting habitat of Adélie penguins is also undergoing major changes as summer air temperatures rise, particularly true of the Antarctic Peninsula thus far (Fraser et al. 2013). From 1958-2010, average air temperature in Oct-Dec increased by more than 3°C on Ross Island, leading to glacier retreat and snow melt, exposing additional potential nesting habitat for Adélie penguins (LaRue et al. 2013). On the other hand, it is predicted that increasing summer temperatures will also lead to greater snowfall in the high latitude Antarctic (Ainley et al. 2010, Ballerini et al. 2015) burying nests, covering potential nesting habitat, and increasing nest flooding from meltwater (Fig. 9). Understanding how individuals of different quality respond to changing habitat is central to understanding a population’s ability to cope with environmental change (Nussey et al. 2007). By using data primarily acquired over the past 19 yrs, we aim to characterize the quality of nesting habitat within and between colonies to better understand the interactions between habitat characteristics, individual quality, and colony growth, and how these interactions could be affected by climate change.

To summarize, we propose to accomplish three goals, each of which relates to how Adélie penguins adapt to, or cope with environmental change in different phases of their annual life-cycle, both at sea and on land, and how outcomes in one phase may affect another:

• Determine how changing winter sea ice conditions in the Ross Sea affect the migration, behavior and survival of Adélie penguins and identify the COEs to subsequent reproduction;
• Determine the interplay between extrinsic and intrinsic factors influencing COEs over multiple years of an individual’s lifetime;
• Determine how climate change may affect population change at colonies via impacts to nesting habitat, interacting with individual quality and COEs.

We will also continue building a population of known-aged, RFID-tagged birds and monitoring banded known-age birds to pursue investigations into reproductive ecology, including senescence, lifetime reproductive success, and age-related survival and productivity.

Goal 1: Determine how changing winter sea ice conditions in the Ross Sea affect the migration, behavior and survival of Adélie penguins and identify the COEs to subsequent reproduction. Using GLS tags over a previous 3-yr study, we showed that Adélie penguin wintering location varied latitudinally by year and longitudinally by colony, and was correlated with variation in timing of sea-ice formation, sea-ice motion and SIE (Ballard et al. 2010b; Fig. 6). Based on these results, we hypothesized that unusually great SIE in one year took Adélie penguins farther away than normal from Ross Island, thus necessitating a longer total migration. Greater SIE also resulted in birds wintering across the Southern Boundary of the Antarctic Circumpolar Current (SbACC) in less productive waters (Tynan 1998, Nicol et al. 2004), resulting in delayed negative consequences for colony growth (Wilson et al. 2001, Ballard et al. 2010b). Since then, winter sea-ice has increasingly extended beyond the SbACC in the Ross Sea region (Stammerjohn et al. 2012), but consequences for Adélie penguins are unknown. In our previous study, we worked with birds of unknown history, and the tags available at the time did not record foraging behavior, thus limiting our understanding of the mechanisms of coping with winter conditions.

We propose to expand upon our earlier winter tracking study (add more years) to incorporate more variability in sea-ice conditions (e.g., timing, extent, and concentration), increase sample size of individuals, and include known-age, known-history birds to assess the potential effects of age and breeding quality (BQ) on wintering and migratory behavior. Within the 19-yr time series studied thus far, 2009 and 2013 (not included in the previous wintering study) were years of especially expanded winter SIE, coinciding with subsequent late and asynchronous penguin arrival and laying, and negative effects on reproduction (i.e., presumably negative COEs). We anticipate that, as SIE and SIS continue to increase (Turner et al. 2009, Stammerjohn et al. 2012), more seasons like these will occur, with perhaps even greater degrees of asynchronous arrival and laying and consequences to breeding success, and maybe also adult survival. Also, during summer, foraging behavior correlates with the BQ of a given individual, with higher BQ individuals showing higher foraging efficiency when environmental conditions are demanding (Lescroël et al. 2010). We have not previously assessed penguin foraging behavior during winter (i.e., we knew where they were, but not what they were doing), but new technology enables the addition of that component.

Research hypotheses & predictions related to Goal 1:
H0: Timing of colony departure is affected by breeding status (successful, failed, or skipped breeding attempt).

• Prediction: Timing of colony departure is delayed for successful breeders compared with non-breeders and failed breeders.
• Prediction: Successful breeding lowers subsequent over-winter survival when fall/winter sea ice is more extensive.

H1: Timing of colony departure affects molting and wintering location.

• Prediction: Early departure and molt in eastern Ross Sea will entrain penguins within the circular ice motion of the Ross Gyre whereas late departure forces penguins northward in the western Ross Sea ice flow towards the Balleny Islands and outside the Ross Gyre.
• Prediction: Early departing penguins experience better pre-molt foraging conditions, leading to higher foraging efficiency and better fall body condition.
• Prediction: At the population level, late departure results in decreased survival across all age classes and breeding states.

H2: Greater SIE and co-incident motion results in penguins wintering in less productive waters north of the SbACC.

• Prediction: In winters of greater SIE, Adélie penguins, in association with the marginal ice zone, will be advected farther north, exhibit higher foraging effort and lower foraging success.
• Prediction: At the population level, years of more northward wintering area, i.e., across the SbACC, will result in decreased survival across all age classes and breeding states.

H3: Older and/or higher BQ penguins are better able to cope with unfavorable winter conditions.

• Prediction: Migration from the wintering area will take longer for younger and/or lower BQ birds in years of greater SIE, resulting in delayed and/or increased asynchrony of arrival and laying at colonies.
• Prediction: At the population level, SIE in winter/spring is positively correlated to the average age at first breeding attempt and negatively correlated to the percentage of young birds (3-4 yr. old) breeding during the subsequent summer.

H4: Foraging efficiency and location in winter affects subsequent arrival timing, condition and breeding success.

• Prediction: Higher foraging efficiency in winter and more southerly wintering location result in arriving earlier and in better condition on the breeding grounds.
• Prediction: Individuals starting the breeding season in better condition are able to invest more in parental care, resulting in higher breeding success.

Methods related to Goal 1:
To test the hypotheses about winter location and foraging efficiency (as well as hypotheses from Goal 2), we will deploy GLS-TDR tags (Ultra Light Loggers (http://www.iphc.cnrs.fr/Bio-loggers-LUL.html) available through a collaboration with Institut Pluridisciplinaire Hubert Curien (CNRS, Strasbourg, France). The loggers (16 x 8 x 4 mm, 0.76 g without casing) can record temperature and light for geolocation purposes every 20 seconds and pressure every second for more than 1 year, resulting in detailed dive profiles during both the breeding and non-breeding period. The method of affixing these tags using small leg bands is described in Ratcliff et al. (2014), a slight improvement over methods we have employed previously and shown to have minimal impact on the penguins (Ballard et al. 2010b). We may use newer or modified tags depending on technological advances between now and study implementation. To minimize the impact of added external gear on penguins’ behavior, for known-age banded penguins we will remove the band when attaching the tag and implant them with a RFID tag (nests are marked with large nails and cattle tags and GPS). Sampling design will be evenly spread over young (3-6 yr old), middle-age (7-10 yr old) and old (11+ yr old) breeders as well as stratified by BQ (described below). We plan to initially equip 130 known-age birds at Cape Crozier (colony size: 280 000 breeding pairs) in Year 1, aiming to follow at least 30 of the same individuals over the entire 5 yr study period (see Goal 2). Based on a conservative tag return rate of 0.70 (Ballard et al. 2010b, Ratcliffe et al. 2014), this allows >230 overwinter tracks (91 in Year 1-2, 64 in Year 2-3, 45 in Year 3-4, 32 in year 4-5) at Cape Crozier. We also will be equipping weighbridge (WB) birds for whom we have long histories (but not age), 30 new birds (~15 mated pairs) each year (30×4 years = 120 total). At Cape Royds (colony size: 2500 breeding pairs), we will equip 62 birds in Year 1, adding 13 more birds in Year 3 and Year 4 so as to have at least 30 individuals for each winter and preceding summer (>160 overwinter tracks, 43 year 1-2, 30 year 2-3, 30 year 3-4, 30 year 4-5). There is no WB at Royds. In subsequent years, GLS-TDR tags will be collected from known-age birds at the beginning of the breeding season and replaced with new tags to monitor for the next 12 months (see Goal 2). Note that these are the largest sample sizes we believe are feasible given considerations of disturbance to penguins and field efforts required, and compare favorably with similar studies (cf. Daunt et al. 2014). We acknowledge that we may not be fully able to disentangle interactions between, for example, age and BQ (depending on effect sizes), but in any case we will still be able to address most of our hypotheses.

We will build a new WB (current one is 20 yr old) with improved weighing performance (sampling at >60 Hz vs. 12.5 Hz for current version) and much higher data storage capacity required to post-process the data (following Ballard et al. 2010a). With the new WB we will monitor relative mass changes throughout the season in relation to winter location and initial body mass; as well as to relate diving characteristics (e.g. number of undulations in the dive profile, time spent at the bottom of the dive, etc.) to mass changes in order to use this relation for assessing foraging success in winter and summer based on diving characteristics.

To test the predictions of H0 and H1, the light data from retrieved tags will be processed using package {TripEstimation} in R to calculate position coordinates (2 positions per day) with associated spatial likelihood of estimates following Thiébot & Pinaud (2010). This method uses a Kalman filter improved by a sea surface temperature matching procedure and a land mask, allowing us to correct position estimates that might otherwise be rejected, especially during equinox periods. This will allow us to calculate the overall bearing of each reconstructed individual track. We will determine timing of departure from the breeding colonies and timing of molt from the position data, the pattern of diving activity and the temperature recordings (as penguins stay hauled out when molting).Estimated locations as well as uncertainties in locations will be gridded following Ratcliffe et al. (2014b). Analysis of the time spent by birds from a given group (e.g. early vs. late departing individuals) within the grid will allow us to produce 50% and 95% isopleths for each group and each part (outgoing vs. returning) of the winter migration to allow further comparisons of spatial distribution among groups. Foraging efficiency during the pre-molt period will be calculated as foraging success/foraging effort. Foraging effort will be measured as 1) time spent diving per 24 hrs, 2) total vertical distance travelled per 24 hrs, from GLS-TDR data, and foraging success as the number of undulations and/or another index validated from instrumented WB birds (relative to mass gain). Under the assumption that birds that are able to acquire more nutritional resources will allocate more of these resources to plumage production (Murphy et al. 1988), we will also measure feather corticosterone following Kouwenberg et al. (2015) as a measure of nutritional status (Will et al. 2014) and stress during the period of feather growth (Bortolotti et al. 2008, Harms et al. 2015).

For all banded- and WB birds, we will estimate age-, breeding state- (subadults: 0-2 yr; pre-breeders: >3 yr; breeders and non-breeders: >3 yr) and colony-specific annual survival rates incorporating the potential effects of environmental conditions during migration and on wintering areas using multi-state mark-recapture models incorporating unobservable states and accounting for detection rates <1.0 (e.g., Lebreton et al. 2003). We will develop an a priori model set reflecting predictions regarding survival and transition probabilities and use Program MARK to generate estimates and model selection results (White et al. 2006). We will use an information theoretic approach and model selection statistics to evaluate our a priori model set (Burnham & Anderson 2002), including Akaike’s Information Criteria adjusted for small sample size and overdispersion (QAICc) if necessary, differences between model QAICc and the model with the lowest QAICc (ΔQAICc = model QAICc − minimum QAICc), and Akaike weights (Burnham and Anderson 2002). We will also use estimates of regression coefficients (β) and their 95% confidence limits and estimates of effect size to provide additional information (strength of evidence) for specific effects.

To test the predictions of H2, we will additionally calculate SIE from microwave (SSM/I and SSMIS) data available from NASA via the National Snow and Ice Data Center. Sea ice motion vectors will also be obtained from NASA. To test the predictions of H3, we will use a broken stick modeling approach for inferring individual homing decision dates from geolocation data (Thiebot et al. 2014). We will calculate an index of intrinsic breeding quality based on our 19 years of data on the breeding success of known-age and known-history penguins (> 10,000 individuals resighted). The index will be calculated for each of the instrumented birds, based on their age and previous history (i.e., a measure of the long-term relative breeding performance of an individual compared to others). We will develop two metrics for this index: one of them will be an adaptation of the Breeding Quality Index (BQI) that we developed for birds of known history but unknown age (i.e., the mean of differences between actual and predicted breeding success over an individual’s lifetime; Lescroël et al. 2009, 2010), but here we will also take into account age, breeding experience and cohort; the second metric will be a cumulative version of this new index (i.e., the difference between the actual and the predicted number of successful events over an individual’s lifetime). Individual timing of arrival to the breeding colonies will be derived from the geolocation data and pattern of diving activity and temperature. We will determine breeding phenology and success as we have for the past 19 years. In summary, nests of all penguins in the study containing at least one egg will be marked with large nails and cattle tags and GPS’d. They will be checked from a distance of at least 5m (using binoculars as needed) every 2-5 d to determine hatching and crèching success. Chicks will be marked with fishtags (eventually removed) to determine whether or not they joined a crèche. Laying dates will be determined from direct observations and/or retro-calculated from hatching dates. Average age at first breeding attempt and percentage of young birds (3-4 yr. old) breeding will be calculated from our past 19 years of demographic data.

To test, the predictions of H4, spring body condition for known-age birds (outside WB) will be assessed by capturing birds on arrival weighing and measuring them (bill length, depth, flipper length, torso circumference) and using a scaled Mass Index (Peig & Green 2009, Whitehead et al. 2015). Initial body mass and subsequent relative mass changes of equipped birds nesting within the WB subcolony will be monitored by our new WB.

Goal 2: Determine the interplay between extrinsic and intrinsic factors influencing COEs over multiple years of an individual’s lifetime. Behavioral and physiological plasticity (i.e., within-individual variation in behavioral or physiological traits along an environmental gradient) is recurrently seen as a major component of immediate strategies for coping with climate change (Huey et al. 2012), whose pace often exceeds the potential evolutionary response of species to selection (Canale & Henry 2010). Additionally, individuals may respond differently to the same environmental changes (i.e., between-individual variation in average trait value or in plasticity). Between-individual variation in plasticity (also called individual by environment interaction ‘‘IxE’’) has been highlighted as an important, but underexplored possibility with consequences for population-level phenomena (Nussey et al.2007, Grémillet & Charmantier 2010). While we explored this interplay between extrinsic (environmental) and intrinsic (individual characteristics) factors in determining foraging efficiency of Adélie penguins in summer (Lescroël et al. 2014), we have yet to explore its fitness consequences (i.e., effect on reproductive success and/or survival). Furthermore, fitness consequences of ecological variation may not be realized immediately, but instead have repercussions at a later life-history stage. Here, we propose to test the effects of extrinsic and intrinsic factors simultaneously in determining COEs of winter conditions on subsequent reproductive success and survival over multiple years of an individual’s lifetime. We will use data from our long-term longitudinal demographic study (19 yrs of data already collected) and from longitudinal measurements of foraging behavior and body condition across a range of environmental conditions (4 yrs of data to be collected during current project). To our knowledge, only one recent study (Daunt et al. 2014) has been able to use longitudinal measurements for testing extrinsic and intrinsic factors simultaneously. However, the individuals followed in this study were of unknown age and unknown previous breeding history (same in Lescroël et al. 2014) and the authors were not able to investigate the mechanisms involved in within and between-individual variation. Using banded individuals of known age and known breeding history, as well as RFID’d individuals of known breeding, condition, and foraging history, we expect our study to be truly transformative and lead to a better understanding of the drivers of COEs in the context of rapid environmental change.

Research hypotheses & predictions related to Goal 2:
H5: The relationship between foraging efficiency and winter environmental conditions varies amongst individuals of different intrinsic quality.

• Prediction: Individuals of higher BQ exhibit higher overall foraging efficiency in winter (between-individual variation in average trait value: higher intercept) over a gradient of a) latitudinal position relative to the SbACC, b) daylight duration, c) sea ice concentration (SIC).
• Prediction: Individuals of higher BQ are better able to buffer changes in foraging efficiency in winter (between-individual variation in plasticity: lower slope) over a gradient of a) latitudinal position relative to the SbACC, b) daylight duration, c) SIC.

H6: Foraging efficiency in winter and summer varies amongst years and within individuals, depending on age class.

• Prediction: Within individuals, foraging efficiency in winter and summer increases from year to year in young individuals (followed from 3 to 7 yr old), remains stable in middle-age individuals (followed from 7 to 11 yr old) and decreases in old individuals (followed from 11 to 15 yr old).

H7: COEs of winter foraging efficiency on spring body condition, breeding propensity and phenology, breeding success, and subsequent annual survival vary amongst years and individuals.

• Prediction: Individuals of higher BQ exhibit consistently higher foraging efficiency in winter and this translates into consistently better spring body condition, consistently higher probability of breeding and earlier breeding phenology, consistently higher breeding success, and consistently higher subsequent annual survival.
• Prediction: The magnitude of the COE of foraging efficiency in winter on spring body condition, breeding propensity and phenology, breeding success, and subsequent annual survival varies amongst years.
• Prediction: Individuals of higher BQ are better able to buffer changes in the magnitude of COE amongst years, exhibiting consistently higher body condition, breeding success, and subsequent annual survival.
• Prediction: Within individuals, better body condition on arrival leads to higher parental investment and higher breeding success.

Methods related to Goal 2:
To test the predictions of H5 and H7, we will use random regression models (van de Pol & Verhulst 2006, Nussey et al. 2007) in a Bayesian framework (Hadfield 2010 ) to simultaneously estimate the effects of extrinsic (latitudinal position relative to the SbACC, daylight duration, SIC, year) and intrinsic (index of breeding quality, individual ID) variation on the dependent variables (foraging efficiency, body condition, timing of arrival, parental investment, breeding success, depending on the prediction tested), as well as to partition within- and between-individual variation. This method also specifically accounts for the fact that measurements from the same individual might be autocorrelated.

To test the predictions of H5, we will use GLS-TDR data collected on the same individuals over 5 years to calculate locations and foraging efficiency (see goal 1 for description of devices, attachment methods, and sample size). Latitudinal position relative to the SbACC (the daily position of birds equipped with GLS-TDR tags) will be derived from the light recordings following Thiébot & Pinaud (2010); see Goal 1. We will compute average values by weeks and/or months. Daylight duration will be calculated from the light recordings of the GLS-TDR tags and daily SIC in winter (25 km resolution) will be derived from passive microwave imagery available from the National Snow & Ice Data Center.

While we are developing new BQ indexes for this project, we will also take the opportunity to better understand the different lifetime reproductive strategies of Adélie penguins by looking at the evolution of these indexes through life depending on an individual’s age and age at first reproduction.

Foraging efficiency will be assessed from the GLS-TDR data in summer and winter (see Goal 1). We will compute average values by weeks and/or months, as well as coefficients of variation and anomalies (foraging efficiency of an individual minus mean foraging efficiency across years). Timing of arrival on the breeding grounds will be assessed from the GLS-TDR data, using both the position derived from the light recordings and the pressure and temperature profiles to determine when birds arrive on land. We will arrive at our study sites in time (late October) to find nests of known-age and WB birds soon after their arrival, measure body condition on arrival and record their laying dates (see Goal 1 for body condition methods). Known-age individuals equipped over multiple years will only be handled once a year (in late October) for less than 6 min from catch to release in order to retrieve the GLS-TDR tag attached the previous year, put on a new tag, weigh and measure the birds and sample feathers.

To test the predictions of H6, we will calculate average foraging efficiency during key life-stages (incubation, chick-rearing, pre-molt, outgoing migratory phase, returning migratory phase) from GLS-TDR data. Variation in average foraging efficiency for each of these stages over the multiple-year deployments will be modelled using generalized linear mixed models including year (as a factor) and the interaction between age class and time since deployment (in years, from 1 to 5 as a continuous variable) as fixed effects, as well as individual identity as a random effect.

To test the predictions of H7, we will determine breeding propensity, phenology and success for all known-age birds, including those wearing GLS tags, by monitoring throughout the season (see Methods for H5 and Goal 1). Breeding success will be quantified as the number of chicks (0, 1 or 2) raised to the crèching stage. Parental investment will be evaluated by monitoring the relative mass changes (as a proportion of initial body mass) of WB individuals throughout the season from WB measurements as an index of parental investment (Ballard et al. 2010a). We will also consider chick feeding frequency and provisioning efficiency (i.e., weight of food brought back to the colony by a given individual divided by previous trip duration; see Lescroël et al. 2010). Annual survival, as well as breeding propensity of instrumented birds will be estimated using multi-state open population models and Program MARK (White et al. 2006) for the 5 years birds will receive instruments (see Methods for Goal 1 above). Indices to individual quality can be estimated for every bird receiving a GLS tag, but foraging behavior during winter and body condition upon arrival can only be determined for those birds that return. However, if as predicted a link can be made between varying levels of individual quality and body condition, we can link annual survival to individual quality (e.g., Lescroel et al. 2009) and infer the effects of winter COE on annual survival based on winter foraging behavior/body condition/individual quality linkages. In addition, using lagged effects of winter or migration conditions we will attempt to determine whether annual survival is most strongly affected by direct mortality during winter/migration vs summer (i.e., conditions within current survival interval) or COEs from previous winter/migration conditions (i.e., conditions associated with previous survival interval).

Goal 3: Determine how climate change may affect population change at colonies via impacts to nesting habitat, interacting with individual quality and COEs. Despite an understanding of changes in the large-scale millennial pattern in distribution of Adélie penguin colonies in the Ross Sea region (Emslie et al. 2003, 2007), information on the mechanisms of Adélie penguin response to terrestrial habitat change at the local breeding colony scale remains sparse (Bricher et al. 2008, Fraser et al. 2013). Whether or not habitat exposed by retreating glaciers and snowfields is colonized may depend on the quality of the new habitat relative to existing habitat. Habitat quality can also be measured in relation to its ability to mitigate the effects of climate change, as extreme weather may affect some parts of the colony more than others, turning some nesting locations into localized population sinks (Bricher et al. 2008, Ferrer et al. 2014, Moreno & Moller 2012). For example, summer snowfall, projected to increase in coastal Antarctica, and specifically in the Ross Sea (Ainley et al. 2010, Ballerini et al. 2015), buries nesting penguins, covers suitable nesting habitat, and increases meltwater (Ducklow et al 2007, Bricher et al. 2008, Fraser et al. 2013, Fig. 9). But snowfall may not affect all colonies or subcolonies (discrete patches within a colony) equally and some locations may be more susceptible to increased snowfall than others (Bricher et al. 2008, Fraser et al. 2013). At the scale of subcolonies and individual territories, nests in smaller subcolonies or on the periphery of subcolonies may be more at risk from exposure, disturbance, and predation (Ferrer et al. 2014, Taylor 1962, Tenaza 1971, Ainley et al. 1983), and these factors can result in population change (La Rue et al. 2013, Fraser et al. 2013). At the finest scale, habitat selection theory predicts that individuals should attempt to settle in the highest quality habitat available to maximize their fitness (Fretwell 1972). However, in part because of COE’s from the previous winter, individuals may differ in their ability to compete for the highest quality nest sites and poorer quality individuals may end up settling in suboptimal habitat. Our aim is to establish whether habitat characteristics on Ross Island affect breeding success and recruitment at the scale of colonies, patches within a colony (subcolonies), and individual nests, and how these habitats may be affected by rapid climate change. Understanding how individuals of different age and intrinsic quality interact with changing habitat will provide valuable insight into the mechanisms of population change. We will also investigate whether conditions from the previous winter and migration periods can affect the quality of nest site occupied by individuals the subsequent breeding season.

Research hypotheses & predictions related to Goal 3:
H9: Habitat characteristics affect breeding success and differ within colonies.

• Prediction: within a colony, some subcolonies have consistently higher breeding success than others and they are distinguishable (in terms of physical habitat characteristics) from subcolonies with lower success.

H10: The habitat characteristics determining breeding success are different than those driving growth/recruitment.

Prediction: sheltered areas and alluvial areas are habitat traps—recruiting individuals are attracted there by open space and lots of pebbles and the number of nests will grow faster than average. But, nests there are at higher risk of snowdrifts or late season flooding from melt water and will have lower than average breeding success and/or higher variation in breeding success (Fig. 9).

H11. Individuals of high intrinsic quality nest in subcolonies with physical habitat and nest characteristics that are distinguishable from subcolonies occupied by lower quality individuals.

• Prediction: subcolonies with higher reproductive rates have a high proportion of high quality individuals.
• Prediction: Nests of high quality individuals are distinguishable (in terms of physical characteristics) from nests of low quality individuals

H12: Wintering location has COEs on where individuals nest.

• Prediction: individuals arriving late and in poor condition acquire low quality nesting habitat
• Prediction: penguins in poor condition and in poor quality nest sites stand up more and thus have lower incubation temperature, increased risk of predation, and lower hatching success.

Fig. 9. Examples of habitat traps: what seems to be suitable habitat in the early season turns out to not be true later in the season owing to climate change-induced increased snow drifts and meltwater streams.

Methods related to Goal 3:
To test the habitat predictions (under H9 and H10), we will use the 19 years of historic data on subcolony breeding success (numbers of chicks crèched per breeding pair) at all three study colonies (90+ subcolonies with 30+ each at the three Ross Island colonies, Cape Royds, Cape Crozier, and Cape Bird) to model (using a GLM) differences in breeding success as a function area, shape, risk from snowdrift and physical characteristics of the subcolony habitat. Subcolony area and shape over time will be digitized using historic aerial photos (available from NZ collaborators from 1996-2015). The subcolony habitat will be quantified by using a high resolution Digital Elevation Model (DEM; to be provided by the Polar Geospatial Center) to characterize the elevation, slope, aspect, isolation, and position (relative to edge of colony, snowfields and, glaciers) for each subcolony. Risk of flooding and snowdrift within and between colonies will be modeled using the DEM and a GIS model (as per. Bricher et al. 2008) with ground-truthing by field crews. From this model we will create a map of the spatial distribution of predicted breeding success, based on the physical variables selected in the model, for each colony and each year. The predictions of the model will be validated using the breeding success data on known individuals nesting throughout the colony.

The second step will be to use historic aerial photos to characterized the rate of growth (number of new nests) for each subcolony then model the rate of change as a function of the physical characteristics of the subcolony (also using a GLM and the same characteristics as for the nests). We will then create a map of the areas of predicted highest growth and compare to the spatial distribution of predicted breeding success and assess the overlap. If the areas of highest growth do not correspond to the areas of highest breeding success, this would indicate a habitat trap (i.e., individuals are attracted to and settling in poor quality habitat). Areas of highest growth and in newly exposed habitat will be targeted over the 5 seasons (2016-2020) to measure breeding success relative to the historic study subcolonies and validate the model predictions. We will also characterize the proportion of each colony (Royds, Crozier, and Bird) that is susceptible to increased snowdrift and meltwater to establish the relative risk to each colony from climate-driven change in nesting habitat.

We will evaluate the predictions of the individual quality and carry-over effects hypotheses (H11 and H12) by first using the historic data on the nesting locations of known-age, known-history individuals (~7500 nests with GPS locations and position relative to subcolony edge) to establish whether the high quality subcolonies (as determined from the overall chicks per pair data described above) consistently have a higher proportion of high BQ individuals. We will also measure nest volume (De Neve et al. 2006) and nest position (relative to subcolony edge) to characterize the nests of GLS-TDR-tagged birds each year over each field season to establish whether the nests of individuals arriving in better condition are distinguishable from those of individuals arriving in poor condition. We will use GLMs to determine whether nest characteristics (volume and position) vary as a function of individual quality, arrival date and body condition (with interactions). Finally, the GLS-TDR tags include a temperature sensor that will also allow us to quantify incubation behavior (e.g. incubation shift length, standing frequency); we will use GLMs to model incubation behavior (percent of time standing, frequency of standing) as a function of arrival condition and environmental conditions (e.g., air temperature as determined from local weather stations) and to model hatching success as a function of incubation behavior.

FIELD PLAN AND ROLE OF COLLABORATORS

Please see Logistical Requirements and Field Plan in Supplementary Documents for details on how we will undertake our project. As in the current and previous grants that funded this project, it is a collaboration among the senior PIs (D Ainley, G Ballard, K Dugger), and their respective home institutions, along with a series of other collaborators including French colleagues (Co-PI A Lescroël, leading the studies of quality- and age-related behaviors) and those from Landcare Research New Zealand (see Results of Prior NSF Support). Ainley brings an overall connection to other Antarctic issues and extensive insights into the Ross Sea ecosystem spanning 5 decades; Ballard brings extensive experience with large data sets and spatial analyses, oversees the intern program and one of the proposed post-doctoral scholars (A Schmidt); and Dugger brings expertise in demographic modeling and a university connection that allows incorporation of MSc and PhD students and oversight of the other post-doctoral scholar. The first of two post-docs, A Schmidt, already has 3 year of Antarctic penguin experience. In addition to the annual aerial photographs of the penguin colonies, Landcare Research NZ (led by P Lyver) has provided an effort at Cape Bird complementary to ours at Royds, Crozier and Beaufort Island, and thus some of the retrospective analyses described herein also include Cape Bird data. We have benefitted tremendously with the work of J Pennycook, an educator with wide experience in reaching schools and the public, and this will continue (see Broader Impacts, below). N Pollish helped to design our original WB and will volunteer his services again.

BROADER IMPACTS OF PROPOSED PROJECT

The education and public outreach (EPO) aspect of the project will continue to be promoted through the Penguin Science website (see Results of Prior Support ;> 1M hits/mo). Our most popular project,”NestCheck,” is followed by over 300 classrooms annually as students adopt then follow a penguin family throughout the breeding season. Participants keep their own field book of observations as individual nest pictures are uploaded daily and students download weather data from the penguin cam located in the colony. Students combine the topics of science, art and geography as they first design, create, then send a penguin postcard or flag to the research station to be returned after receiving the official Antarctic postmark or certificate of ‘flying at the station’. Personal connection to the field was made available through the recent enhancements of WIFI at Cape Royds capable of reaching the penguin colony. In season 2014-15, 33 classrooms (~1000 students) were able to watch live penguin behavior as students asked questions. As a mechanism to encourage students in pursuing educational and career pathways in the STEM (Science Technology Engineering and Math) fields, we plan to continue these programs as well as provide daily stories from the field in the Penguin Journal, continue to develop classroom-ready NGSS aligned activities, increase our library of instructional PowerPoints and short behavior videos, and provide outreach through local, state and national speaking engagements about penguins, Antarctica and climate change. Our anticipated outreach for each research season is >400 classrooms and >15 000 students through the website portal and activities, plus 300 teachers and >500 persons in the general public through EPO presentations and workshops.

We will also include two post-docs for 2.5 yr each. The first will be supervised by Dr. Grant Ballard at Point Blue and will start in 2016, focusing on the retrospective analysis. The second will be supervised by Dr. Katie Dugger at OSU and will start in 2018, focusing on the wintering ecology and demographic analysis. We will also include 5 interns (1 per field season). We propose also to include a science writer/photographer (Chris Linder, at no cost) for two months during Year I at Cape Crozier. He’ll produce magazine articles and a webisode about our research to be placed on the PenguinScience website.

Literature Cited

Citations denoted by an asterisk (*) resulted from this project, with those since 2009 (most recent funding) in bold (see Results of Prior NSF Support):
Ådahl E., P. Lundberg & N. Jonzén. 2006. From climate change to population change: the need to consider annual life cycles. Global Change Biology 12:1627–1633.

*Ainley, D.G. 2002. The Adélie Penguin: Bellwether of Climate Change. Columbia University Press, New York, New York.

*Ainley, D.G. & G. Ballard. 2011. Non-consumptive factors affecting patterns in Antarctic penguins: a review and synthesis. Polar Biology, doi: 10.1007/s00300-011-1042-x.

*Ainley, D.G. & G. Ballard. 2012. Trophic interactions and population trends of killer whales (Orcinus orca) in the Southern Ross Sea. Aquatic Mammals 38: 153-160.

*Ainley, D.G., G. Ballard, S. Ackley, L.K. Blight, J.T. Eastman, S.D. Emslie, A. Lescroël, S. Olmastroni, S.E. Townsend, C.T. Tynan, P. Wilson & E. Woehler. 2007. Paradigm lost, or is top-down forcing no longer significant in the Antarctic marine ecosystem? Antarctic Science 19: 283–290.

*Ainley, D.G., G. Ballard, K. Barton, B. Karl, G. Rau, C. Ribic & P. Wilson. 2003. Spatial and temporal variation of diet within a presumed metapopulation of Adélie penguins. Condor 105: 95-106.

*Ainley, D.G., G. Ballard, L.K. Blight, S. Ackley, S.D. Emslie, A. Lescroël, S. Olmastroni, S.E. Townsend, C.T. Tynan, P. Wilson & E. Woehler. 2009a. Impacts of cetaceans on the structure of southern ocean food webs. Marine Mammal Science 26: 482-489.
*Ainley, D.G., G. Ballard & K.M. Dugger. 2006. Competition among penguins and cetaceans reveals trophic cascades in the western Ross Sea, Antarctica. Ecology 87: 2080–2093.
*Ainley D.G., G. Ballard & S. Olmastroni. 2009b. An apparent decrease in the prevalence of “Ross Sea Killer Whales” in the southern Ross Sea. Aquatic Mammals 35: 335-347.
*Ainley, D.G., G. Ballard, R.M. Jones, D. Jongsomjit, S.D. Pierce, W.O. Smith, Jr. & S. Veloz. MS1. Trophic cascades in the western Ross Sea, Antarctica: revisited. Marine Ecology Progress Series, submitted (10 March 2015).
*Ainley, D.G., E.D. Clarke, K. Arrigo, W.R. Fraser, A. Kato, K.J. Barton & P.R. Wilson. 2005. Decadal-scale changes in the climate and biota of the Pacific sector of the Southern Ocean, 1950s to the 1990s. Antarctic Science 17: 171-182.
Ainley, D.G. & D.P. DeMaster. 1980. Survival and mortality in a population of Adélie Penguins. Ecology 61: 522-530.
Ainley, D.G., R.E. LeResche & W.J.L. Sladen. 1983. Breeding biology of the Adélie penguin. University of California Press, Berkeley CA.
*Ainley, D.G., N. Nur, J.T. Eastman, G. Ballard, C.L. Parkinson, C.W. Evans & A.L. Devries. 2012. Decadal trends in abundance, size and condition of Antarctic toothfish in McMurdo Sound, Antarctica, 1972-2011. Fish and Fisheries. doi: 10.1111/j.1467-2979.2012.00474.x.
*Ainley, D. & D. Pauly. 2013. Fishing down the food web of the Antarctic continental shelf and slope. Antarctic Record, doi: 10.1017/S0032247412000757.
*Ainley, D.G., C.A. Ribic, G. Ballard, S. Heath, I. Gaffney, B.J. Karl, K.R. Barton, P.R. Wilson & S. Webb. 2004. Geographic structure of Adélie penguin populations: size, overlap and use of adjacent colony-specific foraging areas. Ecological Monographs 74: 159-178.
*Ainley, D.G., J. Russell, S. Jenouvrier, E. Woehler E, P. O’B. Lyver, W.R. Fraser & G.L. Kooyman. 2010. Antarctic penguin response to habitat change as earth’s troposphere reaches 2° C above preindustrial levels. Ecological Monographs 80: 49–66.
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