Fathers matter: male body mass affects life-history traits in a size-dimorphic seabird

One of the predicted consequences of climate change is a shift in body mass distributions within animal populations. Yet body mass, an important component of the physiological state of an organism, can affect key life-history traits and consequently population dynamics. Over the past decades, the wandering albatross—a pelagic seabird providing bi-parental care with marked sexual size dimorphism—has exhibited an increase in average body mass and breeding success in parallel with experiencing increasing wind speeds. To assess the impact of these changes, we examined how body mass affects five key life-history traits at the individual level: adult survival, breeding probability, breeding success, chick mass and juvenile survival. We found that male mass impacted all traits examined except breeding probability, whereas female mass affected none. Adult male survival increased with increasing mass. Increasing adult male mass increased breeding success and mass of sons but not of daughters. Juvenile male survival increased with their chick mass. These results suggest that a higher investment in sons by fathers can increase their inclusive fitness, which is not the case for daughters. Our study highlights sex-specific differences in the effect of body mass on the life history of a monogamous species with bi-parental care

Mapping and assessing variability in the Antarctic marginal ice zone, pack ice and coastal polynyas in two sea ice algorithms with implications on breeding success of snow petrels

© The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in The Cryosphere 10 (2016): 1823-1843, doi:10.5194/tc-10-1823-2016.Sea ice variability within the marginal ice zone (MIZ) and polynyas plays an important role for phytoplankton productivity and krill abundance. Therefore, mapping their spatial extent as well as seasonal and interannual variability is essential for understanding how current and future changes in these biologically active regions may impact the Antarctic marine ecosystem. Knowledge of the distribution of MIZ, consolidated pack ice and coastal polynyas in the total Antarctic sea ice cover may also help to shed light on the factors contributing towards recent expansion of the Antarctic ice cover in some regions and contraction in others. The long-term passive microwave satellite data record provides the longest and most consistent record for assessing the proportion of the sea ice cover that is covered by each of these ice categories. However, estimates of the amount of MIZ, consolidated pack ice and polynyas depend strongly on which sea ice algorithm is used. This study uses two popular passive microwave sea ice algorithms, the NASA Team and Bootstrap, and applies the same thresholds to the sea ice concentrations to evaluate the distribution and variability in the MIZ, the consolidated pack ice and coastal polynyas. Results reveal that the seasonal cycle in the MIZ and pack ice is generally similar between both algorithms, yet the NASA Team algorithm has on average twice the MIZ and half the consolidated pack ice area as the Bootstrap algorithm. Trends also differ, with the Bootstrap algorithm suggesting statistically significant trends towards increased pack ice area and no statistically significant trends in the MIZ. The NASA Team algorithm on the other hand indicates statistically significant positive trends in the MIZ during spring. Potential coastal polynya area and amount of broken ice within the consolidated ice pack are also larger in the NASA Team algorithm. The timing of maximum polynya area may differ by as much as 5 months between algorithms. These differences lead to different relationships between sea ice characteristics and biological processes, as illustrated here with the breeding success of an Antarctic seabird.This work is funded under NASA grant NNX14AH74G and NSF grant PLR 1341548

Influence of dispersal processes on the global dynamics of Emperor penguin, a species threatened by climate change

© The Author(s), 2017. This is the author's version of the work. It is posted here under a nonexclusive, irrevocable, paid-up, worldwide license granted to WHOI. It is made available for personal use, not for redistribution. The definitive version was published in Biological Conservation 212 (2017): 63-73, doi:10.1016/j.biocon.2017.05.017.Species endangered by rapid climate change may persist by tracking their optimal habitat; this depends on their dispersal characteristics. The Emperor Penguin (EP) is an Antarctic seabird threatened by future sea ice change, currently under consideration for listing under the US Endangered Species Act. Indeed, a climate-dependent-demographic model without dispersion projects that many EP colonies will decline by more than 50% from their current size by 2100, resulting in a dramatic global population decline. Here we assess whether or not dispersion could act as an ecological rescue, i.e. reverse the anticipated global population decline projected by a model without dispersion. To do so, we integrate de22 tailed dispersal processes in a metapopulation model|specifically, dispersal stages, dispersal distance, habitat structure, informed dispersal behaviors, and density-dependent dispersion rates. For EP, relative to a scenario without dispersion, dispersal can either offset or accelerate climate driven population declines; dispersal may increase the global population by up to 31% or decrease it by 65%, depending on the rate of emigration and distance individuals disperse. By developing simpler theoretical models, we demonstrate that the global population dynamic depends on the global landscape quality. In addition, the interaction among dispersal processes - dispersion rates, dispersal distance, and dispersal decisions - that influence landscape occupancy, impacts the global population dynamics. Our analyses bound the impact of between-colony emigration on global population size, and provides intuition as to the direction of population change depending on the EP dispersal characteristics. Our general model is flexible such that multiple dispersal scenarios could be implemented for a wide range of species to improve our understanding and predictions of species persistence under future global change.S. Jenouvrier acknowledges support from WHOI Unrestricted funds and Mission Blue / Biotherm; J. Garnier and L. Desvillettes acknowledge respectively the NONLOCAL project (ANR-14-CE25-0013) and the Kibord project (ANR-13-BS01-0004) from the French National Research Agency

Strong sea surface cooling in the eastern equatorial Pacific and implications for Galápagos Penguin conservation

Author Posting. © American Geophysical Union, 2015. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 42 (2015): 6432–6437, doi:10.1002/2015GL064456.The Galápagos is a flourishing yet fragile ecosystem whose health is particularly sensitive to regional and global climate variations. The distribution of several species, including the Galápagos Penguin, is intimately tied to upwelling of cold, nutrient-rich water along the western shores of the archipelago. Here we show, using reliable, high-resolution sea surface temperature observations, that the Galápagos cold pool has been intensifying and expanding northward since 1982. The linear cooling trend of 0.8°C/33 yr is likely the result of long-term changes in equatorial ocean circulation previously identified. Moreover, the northward expansion of the cold pool is dynamically consistent with a slackening of the cross-equatorial component of the regional trade winds—leading to an equatorward shift of the mean position of the Equatorial Undercurrent. The implied change in strength and distribution of upwelling has important implications for ongoing and future conservation measures in the Galápagos.K.B.K. acknowledges support from the Alfred P. Sloan Foundation, the James E. and Barbara V. Moltz Fellowship administered by the Woods Hole Oceanographic Institution (WHOI) Ocean and Climate Change Institute (OCCI), and the National Science Foundation (NSF) Physical Oceanography program (grant OCE–1233282). S.J. acknowledges support from WHOI. C.W.B. was supported by the NOAA Center for Satellite Applications and Research.2016-02-0

Semiparametric Regression in Capture-Recapture Modelling

Capture-recapture models were developed to estimate survival using data arising from marking and monitoring wild animals over time. Variation in the survival process may be explained by incorporating relevant covariates. We develop nonparametric and semiparametric regression models for estimating survival in capture-recapture models. A fully Bayesian approach using MCMC simulations was employed to estimate the model parameters. The work is illustrated by a study of Snow petrels, in which survival probabilities are expressed as nonlinear functions of a climate covariate, using data from a 40-year study on marked individuals, nesting at Petrels Island, Terre Adelie

Methane observations from the Greenhouse Gases Observing SATellite: Comparison to ground‐based TCCON data and model calculations

We report new short-wave infrared (SWIR) column retrievals of atmospheric methane (X_(CH4)) from the Japanese Greenhouse Gases Observing SATellite (GOSAT) and compare observed spatial and temporal variations with correlative ground-based measurements from the Total Carbon Column Observing Network (TCCON) and with the global 3-D GEOS-Chem chemistry transport model. GOSAT X_(CH4) retrievals are compared with daily TCCON observations at six sites between April 2009 and July 2010 (Bialystok, Park Falls, Lamont, Orleans, Darwin and Wollongong). GOSAT reproduces the site-dependent seasonal cycles as observed by TCCON with correlations typically between 0.5 and 0.7 with an estimated single-sounding precision between 0.4–0.8%. We find a latitudinal-dependent difference between the X_(CH4) retrievals from GOSAT and TCCON which ranges from 17.9 ppb at the most northerly site (Bialystok) to −14.6 ppb at the site with the lowest latitude (Darwin). We estimate that the mean smoothing error difference included in the GOSAT to TCCON comparisons can account for 15.7 to 17.4 ppb for the northerly sites and for 1.1 ppb at the lowest latitude site. The GOSAT X_(CH4) retrievals agree well with the GEOS-Chem model on annual (August 2009 – July 2010) and monthly timescales, capturing over 80% of the zonal variability. Differences between model and observed X_(CH4) are found over key source regions such as Southeast Asia and central Africa which will be further investigated using a formal inverse model analysis

Impacts of climate change on avian populations

Author Posting. © The Author(s), 2013. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Global Change Biology 19 (2013): 2036-2057, doi:10.1111/gcb.12195.This review focuses on the impacts of climate change on population dynamics. I introduce the MUP (Measuring, Understanding and Predicting) approach, which provides a general framework where an enhanced understanding of climate-population processes, along with improved long-term data, are merged into coherent projections of future population responses to climate change. This approach can be applied to any species, but this review illustrates its bene t using birds as examples. Birds are one of the best-studied groups and a large number of studies have de- tected climate impacts on vital rates (i.e. life history traits, such as survival, matura- tion, or breeding, a ecting changes in population size and composition) and population abundance. These studies reveal multifaceted e ects of climate with direct, indirect, time- lagged and non-linear e ects. However, few studies integrate these e ects into a climate-dependent population model to understand the respective role of climate vari- ables and their components (mean state, variability, extreme) on population dynamics. To quantify how populations cope with climate change impacts, I introduce a new universal variable: the \population robustness to climate change." The comparison of such robustness, along with prospective and retrospective analysis may help to identify the major climate threats and characteristics of threatened avian species. Finally, studies projecting avian population responses to future climate change predicted by IPCC-class climate models are rare. Population projections hinge on selecting a multi-climate model ensemble at the appropriate temporal and spatial scales and integrating both radiative forcing and internal variability in climate with fully speci ed uncertainties in both demographic and climate processes.This research was supported by the Grayce B. Kerr Fund and the Penzance Endowed Fund in Support of Assistant Scientists, as well as by a grant from the Ocean Life Institute at Woods Hole Oceanographic Institution

Water vapour line assignments in the 9250-26 000 cm (-1) frequency range

Line parameters for water vapour in natural abundance have recently been determined for the 9250-13 000 cm(-1) region [M.-F. Wrienne, A. Jenouvrier, C. Hermans, A.C. Vandaele, M. Carleer, C. Clerbaux, P.-F. Coheur, R. Colin, S. Fally, M. Bach, J. Quant. Spectrosc. Radiat. Transfer 82 (2003) 99] and the 13 000-26 000 cm(-1) region [P.-F. Coheur, S. Fally, M. Carleer, C. Clerbaux, R. Colin, A. Jenouvrier, M.-F. Wrienne, C. Hermans, A.C. Vandaele, J. Quant. Spectrosc. Radial. Transfer 74 (2002) 493] using a high-resolution Fourier-transform spectrometer with a long-path absorption cell. These spectra are analysed using several techniques including variational line lists and assignments made. In total, over 15 000 lines were assigned to transitions involving more than 150 exited vibrational states of (H2O)-O-16. Twelve new vibrational band origins are determined and estimates for a further 16 are presented. (c) 2005 Elsevier Inc. All rights reserved

Pan-Antarctic analysis aggregating spatial estimates of Adélie penguin abundance reveals robust dynamics despite stochastic noise

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Nature Communications 8 (2017): 832, doi:10.1038/s41467-017-00890-0.Colonially-breeding seabirds have long served as indicator species for the health of the oceans on which they depend. Abundance and breeding data are repeatedly collected at fixed study sites in the hopes that changes in abundance and productivity may be useful for adaptive management of marine resources, but their suitability for this purpose is often unknown. To address this, we fit a Bayesian population dynamics model that includes process and observation error to all known Adélie penguin abundance data (1982–2015) in the Antarctic, covering >95% of their population globally. We find that process error exceeds observation error in this system, and that continent-wide “year effects” strongly influence population growth rates. Our findings have important implications for the use of Adélie penguins in Southern Ocean feedback management, and suggest that aggregating abundance across space provides the fastest reliable signal of true population change for species whose dynamics are driven by stochastic processes.H.J.L., C.C.-C., G.H., C.Y., and K.T.S. gratefully acknowledge funding provided by US National Aeronautics and Space Administration Award No. NNX14AC32G and U.S. National Science Foundation Office of Polar Programs Award No. NSF/OPP-1255058. S.J., L.L., M.M.H., Y.L., and R.J. gratefully acknowledge funding provided by US National Aeronautics and Space Administration Award No. NNX14AH74G. H.J.L., C.Y., S.J., Y.L., and R.J. gratefully acknowledge funding provided by U.S. National Science Foundation Office of Polar Programs Award No. NSF/PLR-1341548. S.J. gratefully acknowledges support from the Dalio Explore Fund

Multi-modal survey of Adélie penguin mega-colonies reveals the Danger Islands as a seabird hotspot

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Scientific Reports 8 (2018): 3926, doi:10.1038/s41598-018-22313-w.Despite concerted international effort to track and interpret shifts in the abundance and distribution of Adélie penguins, large populations continue to be identified. Here we report on a major hotspot of Adélie penguin abundance identified in the Danger Islands off the northern tip of the Antarctic Peninsula (AP). We present the first complete census of Pygoscelis spp. penguins in the Danger Islands, estimated from a multi-modal survey consisting of direct ground counts and computer-automated counts of unmanned aerial vehicle (UAV) imagery. Our survey reveals that the Danger Islands host 751,527 pairs of Adélie penguins, more than the rest of AP region combined, and include the third and fourth largest Adélie penguin colonies in the world. Our results validate the use of Landsat medium-resolution satellite imagery for the detection of new or unknown penguin colonies and highlight the utility of combining satellite imagery with ground and UAV surveys. The Danger Islands appear to have avoided recent declines documented on the Western AP and, because they are large and likely to remain an important hotspot for avian abundance under projected climate change, deserve special consideration in the negotiation and design of Marine Protected Areas in the region.We gratefully acknowledge the financial support of the Dalio Foundation, Inc. through the Dalio Explore Fund, which provided all the financing for the Danger Island Expedition. We would like to thank additional support for analysis from the National Science Foundation (NSF PLR&GSS 1255058 - H.J.L. and P.M.; NSF PLR 1443585 – M.J.P.) and the National Aeronautical and Space Administration (NNX14AC32G; H.J.L. and M.S.). Geospatial support for the analysis of high resolution satellite imagery provided by the Polar Geospatial Center under NSF PLR awards 1043681 & 1559691