Climate change is causing more frequent and intense storms, and climate models predict this trend will continue, potentially affecting wildlife populations. Since 1960 the number of days with >20 mm of rain increased near Punta Tombo, Argentina. Between 1983 and 2010 we followed 3496 known-age Magellanic penguin (Spheniscus magellanicus) chicks at Punta Tombo to determine how weather impacted their survival. In two years, rain was the most common cause of death killing 50% and 43% of chicks. In 26 years starvation killed the most chicks. Starvation and predation were present in all years. Chicks died in storms in 13 of 28 years and in 16 of 233 storms. Storm mortality was additive; there was no relationship between the number of chicks killed in storms and the numbers that starved (P = 0.75) or that were eaten (P = 0.39). However, when more chicks died in storms, fewer chicks fledged (P = 0.05, R 2 = 0.14). More chicks died when rainfall was higher and air temperature lower. Most chicks died from storms when they were 9–23 days old; the oldest chick killed in a storm was 41 days old. Storms with heavier rainfall killed older chicks as well as more chicks. Chicks up to 70 days old were killed by heat. Burrow nests mitigated storm mortality (N = 1063). The age span of chicks in the colony at any given time increased because the synchrony of egg laying decreased since 1983, lengthening the time when chicks are vulnerable to storms. Climate change that increases the frequency and intensity of storms results in more reproductive failure of Magellanic penguins, a pattern likely to apply to many species breeding in the region. Climate variability has already lowered reproductive success of Magellanic penguins and is likely undermining the resilience of many other species.

Competing interests: The authors received donations of field equipment from two commercial sources (Trimble and Canon), but neither company (nor any of our funders) has any claims or restrictions on the data or results. Therefore, this does not alter the authors′ adherence to all the PLOS ONE policies on sharing data and materials.

Funding: The Penguin Project has been funded by: WCS, Exxonmobil Foundation, The Pew Fellows Program in Marine Conservation, The Disney Worldwide Conservation Fund, The National Geographic Society, The Chase Foundation, The Cunningham Foundation, The MKCG Foundation, The Offield Foundation, The Peach Foundation, The Thorne Foundation, The Tortuga Foundation, The Kellogg Foundation, The Wadsworth Endowed Chair in Conservation Science, and Friends of the Penguins. Trimble provided six rugged tablets. Canon provided two pairs of binoculars. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Synchrony of breeding in the colony may affect the proportion of chicks that is vulnerable to a storm and how long a proportion of chicks is vulnerable. Breeding synchrony does not affect an individual chick’s probability of dying, but does affect the proportion of chicks in the colony that is vulnerable to death in a storm. As synchrony decreases, the age span of chicks in the colony increases and the period when some chicks are of a vulnerable age is longer. Magellanic penguins at Punta Tombo bred synchronously with most first eggs laid within about a two week period in the 1980s [47] . We tested whether breeding synchrony decreased and whether laying was less synchronous when laying dates were later. We modeled the consequences of breeding synchrony on the proportion of chicks likely to die in storms on a given day.

A storm is likely to kill a chick if the chick’s down and skin get wet. A very young chick is likely to be protected by a brooding parent in a well-protected nest and not get wet. Once juvenile plumage covers the skin, it protects an older chick’s skin from getting wet, even if the down is wet. We therefore predicted that chicks of intermediate ages would be more likely to die in a storm than younger or older chicks. We also expected increasing rain and decreasing air temperature to increase chick mortality. We expected burrow nests, nests with more cover, and nests that face north to provide more protection to chicks during storms than bush nests, nests with less cover, and south-facing nests. Nests in burrows maintain a more constant temperature [49] , [50] and tend to be better protected from the weather [24] than nests under bushes.

Chick growth and survival to fledging are strongly linked to food availability in many seabird species [45] and starvation was a major cause of chick mortality at Punta Tombo [46] , [47] . Predation on eggs and chicks is an important driver of productivity in many seabirds [48] , including Magellanic penguins [24] . We report the major causes of chick mortality at Punta Tombo from 1983 to 2010, including starvation, predation, storms, and heat. We used our field data on chick age and mortality, weather, and nest characteristics to test predictions about chick mortality from storms and to find the best predictor variables of mortality. We used the model to predict mortality rates for chicks for a range of ages and rainfall amounts. We then simulated the effects of decreasing breeding synchrony in the colony on the proportion of chicks vulnerable to a 40-mm rainstorm.

For most birds, nests help protect eggs and chicks against storms by blocking wind and precipitation and retaining heat [39] . For example, rocks above or around European shag (Phalacrocorax aristotelis) nests protected chicks from rain and spray [40] . Fork-tailed Storm-Petrel (Oceanodroma furcata) nests in soil were warmer than nests in rocks, leading to higher chick survival [41] and shallow burrows were more likely to flood than deeper burrows [42] . Nests also protect eggs and chicks from overheating in the sun [24] , [43] . Ground-nesting passerines in the northern hemisphere orient their nests towards the north at lower latitudes to protect against the heat of the sun and towards the south at higher latitudes to take advantage of the sun’s heat [44] .

Climate models predict that extreme precipitation in the region will increase in the austral summer by 40%–70% in 2076–2100 compared to 1951–1976 [8] and precipitation events that occurred every 20 years in the late twentieth century are predicted to occur every 10–15 years by 2046 and every 7–15 years by 2081 [7] . Precipitation extremes are expected to increase regardless of whether atmospheric circulation patterns change because warmer air holds more moisture; any particular storm can therefore carry more water [36] . Air temperatures are predicted to increase by 1.5 to 2.5°C in the region over the next century [37] , [38] .

Near Punta Tombo, Argentina, site of the world’s largest breeding colony of Magellanic penguins [31] , rainfall increased and temperature patterns changed between 1960 and 2000 during the austral summer, the penguins’ breeding season. At the Trelew airport weather station (43° 12′ S, 65° 16′ W), about 90 km north of Punta Tombo, precipitation in storms became heavier: the amount of precipitation from wet days (days with at least 1 mm of precipitation), the number of consecutive wet days, the number of days with at least 20 mm of precipitation, and the percentage of total precipitation from days with more than the 99 th percentile of rain all increased [32] . Wetter weather was associated with a large-scale spatial pattern of sea-surface temperatures similar to El Niño patterns. Independent of El Niño-Southern Oscillation (ENSO) patterns, storm tracks shifted southward, bringing more precipitation [32] . An increased flow of warmer, moister air from the north accompanied enhanced El Niño-like conditions since 1977 and increased precipitation in the area [33] . At the Trelew airport the daily temperature range increased but there was no significant increase in air temperature. The lowest daily minimum temperature decreased by up to 3°C and the percentage of days with a minimum temperature below the 10 th percentile increased [34] , [35] .

Many penguin species breed in arid and semi-arid coastal areas, including Antarctica, southern Africa, Peru, the Galapagos Islands, Argentina, and Western Australia. Adult Spheniscus penguins have behavioral adaptations to heat [23] , [26] , [27] and temperate penguins usually nest in shaded sites such as burrows or crevices or under vegetation [28] . Heavy rainfall was infrequent historically in arid areas, and seabird species have not had time to adapt to increasing storm frequency and intensity in the 20 th century. On the Antarctic Peninsula, a desert environment that is getting more rain [29] , penguin chicks die when their down gets wet and they cannot maintain their body temperature [30] .

Increased frequency of extreme events, such as storms, drought, temperature extremes, and wildfires, associated with climate change, affect many species [1] , [2] , [3] , [4] , [5] . Over the past 50 years, more precipitation is coming from heavy rainfall in many areas, and climate models predict the trend will continue [6] , [7] , even in places where mean precipitation is not predicted to increase [8] . Intense storms kill birds [9] , [10] , [11] , [12] and may affect colonial species more than others [4] . Single storms kill enough seabird chicks to affect reproductive output of colonies [9] , [10] , [13] , [14] , recruitment, and population size [15] . Increasing frequency of extreme heat [6] also reduces reproductive success [9] , [14] , [16] , [17] , causes adult mortality in birds [5] , [9] and increases stress from lack of water [18] . In addition to direct mortality from hyperthermia and hypothermia, extreme weather increases starvation and predation in chicks. Storms may reduce adult foraging efficiency, decreasing the amount, quality, or frequency of food brought to chicks [19] , [20] , [21] , [22] . Storms and heat may also decrease nest attendance by adults, increasing predation on chicks [14] , [19] , [23] , [24] . Seabirds are challenged by indirect effects of climate change, including reduced marine productivity and range shifts of prey species [25] . We investigated whether direct factors, increased storminess and heat, reduce reproductive success in a long-lived seabird, the Magellanic penguin (Spheniscus magellanicus).

Materials and Methods

We began a long-term study at Punta Tombo, Argentina (44° 03′ S, 65° 13′ W) in 1982, following individual Magellanic penguins and nests [30], [47]. The climate of Punta Tombo is arid, with mean annual precipitation low (<200 mm) but variable [51]. The sparse precipitation when penguins are nesting falls as rain. Penguins arrive at Punta Tombo in September or early October. Females lay two eggs in October, rarely in late September or in November [47], [52], although before egg laying, they usually have 3–4 well-developed follicles with yolk (Boersma unpubl. data). Most eggs hatch between early November and mid-December. Adults take turns foraging, with one parent brooding or guarding the chicks for the first 3–4 weeks [47], [53], after which the parents forage simultaneously and leave the chicks alone. Chicks left alone usually remain in or near their nests rather than forming crèches [53]. Chicks fledge in January or February at 50–100 days of age [53]. We refer to a season by the calendar year that it starts in; 1983 refers to the 1983–1984 season.

We checked all penguin nests from 1983 to 2010 in an area of approximately 7200 m2 once or twice a day from mid-September (before eggs are laid) until late February (after most chicks have left the colony). We found all nests used in this study before chicks hatched. On each visit, we recorded the identity of adults, eggs, and chicks in the nest and every 10 days, we weighed and measured chicks. When chicks died or disappeared we recorded the date and determined their cause of death when possible. The number of chicks visited daily ranged from 39 in 2002 to 213 in 1996 (mean = 125, SD = 43, total = 3496). We used subsets of these chicks, where we had relevant data, for the analyses described below.

Magellanic penguins at Punta Tombo nest in burrows that they dig or in shallow depressions under shrubs [24], [54]. We classified 2785 nests as burrow or bush. We classified the quality of each nest according to the percentage of the nest cup covered by earth or foliage: 1 = good (>80% cover), 2 = average (60–79% cover), 3 = poor (<60% cover). Burrow nests usually have more cover than bush nests; 25% of nests were in burrows and 85% of the burrow nests were good (>80% cover) compared to 33% of bush nests. In 1983–1991, 1999, and 2007–2010, we measured the orientation of the main entrance in degrees by pointing a compass from the nest cup toward the entrance (N = 1600). The orientation is the direction from which wind or rain enters the nest. For example, if the orientation is 180°, wind from the south blows directly into the nest. We classified orientation, a circular variable, into four cardinal directions: North = 316°–45°, East = 46°–135°, South = 136°–225°, and West = 226°–315°.

Breeding adults and the quality of a nest can change between years (Boersma unpubl. data) even though adults often return and use the same nest in subsequent years [50]. Some nests had one chick and some nests had two chicks. Sibling chicks in the same nest were two days apart in age on average and often did not have the same body condition because parents fed one chick more than the other [55]. Siblings in the same nest did not necessarily share the same fate; in 126 nests where a chick died of exposure and had a sibling, 59 of the siblings died of exposure, 47 died of other causes, and 20 fledged. This is not surprising because a few chicks move from their nests to seek more shelter, the company of other chicks, or to beg for food. Even within a nest, microclimates can vary. Nests and siblings were neither completely independent nor the same. We did not use a repeated-measures analysis; each chick was used once. To account for the lack of independence when chicks shared parents or nests, we grouped on nest ID in logistic regressions on chicks. This procedure reduced the degrees of freedom to the number of individual nest IDs and accounted for the lack of independence among the chicks from the same nest. We also used robust standard errors in the regressions [56].

At Punta Tombo, we collected weather data daily, usually before 0800 h. We recorded precipitation (±0.1 mm) using a manually-emptied plastic rain gauge, and minimum and maximum temperatures (±1°C) using a minimum-maximum recording thermometer for the previous 24 hours. We defined a storm as a period of consecutive days with measurable rain, ranging from one to six days (165 of 233 storms lasted one day and 50 lasted 2 days; only 18 lasted more than 2 days). For storms lasting more than one day, we added the rain for the consecutive days. We defined the low temperature as the lowest daily minimum temperature from the start of the storm to the day following the end of the rain. The temperature often dropped after the rain ended.

We did necropsies on dead adults and chicks opportunistically [57]. When we found a dead chick we determined a cause of death when possible. We assigned predation as the cause of death if we found a dead chick with bite marks or saw signs that a predator had gotten into the nest, such as fresh digging by an armadillo (Chaetophractus villosus). If a chick disappeared before 10 January, we assumed a predator took it. Kelp gulls (Larus dominicanus) and Antarctic skuas (Stercorarius antarcticus antarcticus) are the main predators of young chicks and they usually do not leave evidence of their predation. Other predators include skunks (Conepatus humboldti), foxes (Lycalopex culpaeus, L. griseus), weasels (Galictis cuja), and cats (Leopardus geoffroyi). Predators may not leave tracks or their tracks may be covered by penguin tracks. Because the chick may have died and been scavenged, we likely overestimated predation and underestimated other causes of death, especially starvation. Chicks that lost weight between measurements, or were very small or skinny for their age, or had empty stomachs and no body fat when found dead, we classified as starved. Additional evidence for starvation was the failure of adults to changeover at the nest for several days when chicks were less than two weeks old. We assumed that a chick found dead following a storm died of exposure during the storm if it had no sign of injury and was a healthy weight. We also assumed that a dead chick with wet down died of exposure even if its weight was low. We found many wet chicks outside of the nest cup and we classified them as storm deaths. We did necropsies on 15 chicks that died in a storm on 17 December 2009 and six chicks that died in a storm on 24 December 2012 to determine whether their stomachs contained food, so we could rule out starvation as the cause of death. Similarly, we assigned heat as the cause of death if a chick looked healthy but died on a hot day (>37°C). Penguins that are overheated often lie with both legs extended to dissipate body heat through unfeathered skin, and we sometimes found dead chicks in that posture following hot days (>30°C) or dead in the shade next to their nests with their bills open indicating they were panting when they died. We likely underestimated deaths from heat because they are hard to determine unless the chick was seen panting before it died. We lumped several minor causes of death into an “other” category, including crushed or pecked by an adult, burrow cave-in, chick died hatching, and possible toxic algae blooms (chicks fed toxic fish or squid). In some cases, we could not determine cause of death and assigned a code of “unknown”. If a chick was not found dead and weighed at least 1800 g after 10 January, we assumed it fledged.

If we found a chick on the first check of the day, we assumed the chick hatched the previous day; if we found the chick on the second check of the day, we assumed it hatched that day. In a few cases (N = 151 of 3496) there was an interval of two or three days between nest checks. In those cases, we assumed the chick hatched on the day of the last check before we found it. We included these chicks because they increased the sample size of chicks that died of exposure during storms by 9% but we may have overestimated their ages by a few days. Including or excluding them did not alter our conclusions.

We assigned a storm and an age to each chick that was alive during a storm (N = 2482; no storms occurred while 1014 chicks were alive). If a chick died during or immediately after a storm, the chick’s age was its age on the date of the storm that killed it (the first date of a multi-day storm). If the chick did not die in a storm, we used the chick’s age on the date of a randomly-assigned storm that occurred when the chick was known to be alive. These chicks may have fledged or died later of a cause other than a storm. The randomly-assigned storm may or may not have killed other chicks. The number of storms per year ranged from zero in 1988 to 18 in 2005 (mean = 8.3 storms/yr, total storms = 233). Each chick, whether it survived or died in a storm, had an age on a storm date, and a rainfall amount and low temperature from that storm. There were no storms during the chick-rearing period in 1988 so we excluded that season from storm analyses. For each chick that did not die in a storm, we calculated the age when it died, or its age when we last saw it, and whether it disappeared or likely fledged.

Statistical Analyses We tested whether chick age, amount of rain, or low temperature affected a chick’s probability of dying during a storm using our 28 years of data with multiple logistic regressions. The response variable was whether each chick died or survived a storm and we standardized age, precipitation, and low temperature so that each had a mean of zero and a standard deviation of one. When the explanatory variables are standardized, the regression coefficients reflect their relative importance [58]. To allow a peak in mortality at intermediate ages in the regression, we included chick-age squared but also tested models without age squared. We included all 2-way interactions except age × age squared because we did not want to include a cubic fit for age which is unlikely to have biological meaning. We also tested the two 3-way interactions that did not include age and age squared: age × rain × low temperature and age squared × rain × low temperature. We excluded the 4-way interaction because it included both age and age squared. We knew age, precipitation, and temperature for 2482 chicks (590 nests) for 1983–2010. We used AIC [59] to select the best regression model. All regressions were run in Stata 9.2 (StataCorp LP, College Station, Texas, USA). After selecting the best model using chick age and weather variables, we added nest characteristics (type, quality, and orientation) for the subset of 377 nests with 1063 chicks where we had all data on nest characteristics. We again selected the best regression model using AIC. To determine if storm deaths were additive to other sources of mortality, we regressed the number of chicks that fledged each year on the number of chicks that died in storms. A negative relationship would indicate additive mortality. We also regressed the number of chicks that starved and the number eaten on the number that died in storms. Negative relationships would indicate that storm deaths were compensated by lower starvation or predation rates. We calculated the 5th and 95th percentile of laying dates of first eggs (when 90% of first eggs were laid) for each year from 1983 to 2010 (N = 8033 clutches). We used the number of days between the dates of the 5th and 95th percentiles as an index of laying synchrony. We regressed this index on year to determine if laying synchrony had increased (smaller range of days) or decreased (larger range of days) over time. We removed the trends from the time series of lay dates and laying interval by calculating the residuals from their regressions on year. We regressed the residuals on each other to determine if laying is less synchronous in late years independent of trends over time. We weighed 13 to 213 females (mean = 47, total = 1033) in the second half of September (near arrival dates to the colony) in 22 years and regressed median lay date on the mean weight to determine if females were in poorer body condition when eggs were laid late. There was no trend in mean weight over time (F 1,20 = 1.3, P = 0.27).

Predicted Probability of Death We estimated a chick’s probability of dying during a storm using the best regression model for chicks in burrow nests and for chicks in bush nests. We calculated the predicted probabilities for chicks in each nest type from 0 to 50 days of age for seven values of precipitation (10, 15, 20, 25, 40, 45, and 55 mm). We calculated age squared and the age-precipitation and age-squared-precipitation interaction terms using the 51 ages and seven precipitation values. We held low temperature and all interactions with low temperature at their mean values (zero because low temperature was standardized). We simulated the effects of breeding synchrony on chick mortality in storms. We simulated the proportion of chicks likely to die in a storm on a given day by the hatching spread: for 13 days (the mean for 1983–1986) and 27 days (the predicted value for the early 2080s, based on an increase of 0.15 days per year; see results). We assumed a normal distribution of chick hatching dates for each breeding-synchrony group, with the midpoint as the mean. We drew 10,000 random numbers representing chicks for each hatching interval. We binned the numbers (chicks) into 13 categories (days) for the 13-day interval and 27 categories (days) for the 27-day interval. The number in each bin represented the number of chicks hatched on that day, e.g., if the first bin contained 44 numbers then 44 chicks hatched on the first day. Each day for the first 10 days, 3% of chicks were removed from the matrix, and 0.5% of chicks were removed each day thereafter, representing mortality from all non-storm sources based on the mortality we found in the field [46]. We multiplied the number of chicks remaining in each hatch-day bin by the probability that a chick of that age in a bush nest and in a burrow nest would die in a storm with 40 mm of rain. We calculated the number of chicks likely to be killed on each day as the sum of the number of chicks likely to die in each cohort that had hatched by that day (i.e., chicks hatched on day 2 or later were not counted on day 1, etc.). Finally, we converted the number of chicks likely to be killed to a percent.