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It has been proposed that predators searching for prey acquire food according to a probabilistic framework, where success is based on ‘luck’ and the odds of success vary with prey abundance. If true, this has major ramifications for variation in the rates of energy acquisition within animal populations, which is particularly pertinent in offspring provisioning and breeding success, because smaller animals (the young) cannot starve for as long as the adults. However, despite much general speculation about rates of food acquisition, no study has measured whether food encounter is probabilistic in wild animals. We used animal-mounted cameras to document all prey captures by wild imperial shags Leucocarbo atriceps as they hunted underwater and show that, although they mostly do not have inter-prey acquisition time distributions that accord with a ‘luck-based’ framework assuming a constant probability of finding prey over time, there is no difference in the predicted amount of food captured between models that use the empirical data or theoretical Poisson-based fits of the data. We also noted considerable inter-individual differences in foraging success that far exceeded any differences between empirical and theoretical inter-prey acquisition time distributions. The data were used in a probabilistic foraging model that made explicit the mechanistic link between random prey encounters and food-dependent breeding success, indicating that ‘less lucky’ individuals could not provision their broods at rates commensurate with normal growth while the ‘lucky’ birds could do so easily. Given the nature of food encounter in these birds, coupled with substantial inter-individual variation in foraging success, we suggest that more successful individuals are particularly choosey about when, how and where to forage, which results in them operating with higher odds of success.

In the British Isles, the European badger (Meles meles) is thought to be the primary wildlife reservoir of bovine tuberculosis (bTB), an endemic disease in cattle. Test, vaccinate or remove (‘TVR’) of bTB test-positive badgers, has been suggested to be a potentially useful protocol to reduce bTB incidence in cattle. However, the practice of removing or culling badgers is controversial both for ethical reasons and because there is no consistent observed effect on bTB levels in cattle. While removing badgers reduces population density, it may also result in disruption of their social behaviour, increase their ranging, and lead to greater intra- and inter-species bTB transmission. This effect has been recorded in high badger density areas, such as in southwest England. However, little is known about how TVR affects the behaviour and movement of badgers within a medium density population, such as those that occur in Northern Ireland (NI), which the current study aimed to examine. During 2014–2017, badger ranging behaviours were examined prior to and during a TVR protocol in NI. Nightly distances travelled by 38 individuals were determined using Global Positioning System (GPS) measurements of animal tracks and GPS-enhanced dead-reckoned tracks. The latter was calculated using GPS, tri-axial accelerometer and tri-axial magnetometer data loggers attached to animals. Home range and core home range size were measured using 95% and 50% autocorrelated kernel density estimates, respectively, based on location fixes. TVR was not associated with measured increases in either distances travelled per night (mean = 3.31 ± 2.64 km) or home range size (95% mean = 1.56 ± 0.62 km2, 50% mean = 0.39 ± 0.62 km2) over the four years of study. However, following trapping, mean distances travelled per night increased by up to 44% eight days post capture. Findings differ from those observed in higher density badger populations in England, in which badger ranging increased following culling. Whilst we did not assess behaviours of individual badgers, possible reasons why no differences in home range size were observed include higher inherent ‘social fluidity’ in Irish populations whereby movements are less restricted by habitat saturation and/or that the numbers removed did not reach a threshold that might induce increases in ranging behaviour. Nevertheless, short-term behavioural disruption from trapping was observed, which led to significant increases in the movements of individual animals within their home range. Whether or not TVR may alter badger behaviours remains to be seen, but it would be better to utilise solutions such as oral vaccination of badgers and/or cattle as well as increased biosecurity to limit bTB transmission, which may be less likely to cause interference and thereby reduce the likelihood of bTB transmission.

Although shark teeth are abundant in the fossil record, their bodies are rarely preserved. Thus, our understanding of the anatomy of the extinctOtodus megalodonremains rudimentary. We used an exceptionally well-preserved fossil to create the first three-dimensional model of the body of this giant shark and used it to infer its movement and feeding ecology. We estimate that an adultO. megalodoncould cruise at faster absolute speeds than any shark species today and fully consume prey the size of modern apex predators. A dietary preference for large prey potentially enabledO. megalodonto minimize competition and provided a constant source of energy to fuel prolonged migrations without further feeding. Together, our results suggest thatO. megalodonplayed an important ecological role as a transoceanic superpredator. Hence, its extinction likely had large impacts on global nutrient transfer and trophic food webs.

BACKGROUND: Studies on the relationship between renal function and the human plasma proteome have identified several potential biomarkers. However, investigations have been conducted largely in European populations, and causality of the associations between plasma proteins and kidney function has never been addressed. METHODS: A cross-sectional study of 993 plasma proteins among 2882 participants in four studies of European and admixed ancestries (KORA, INTERVAL, HUNT, QMDiab) identified transethnic associations between eGFR/CKD and proteomic biomarkers. For the replicated associations, two-sample bidirectional Mendelian randomization (MR) was used to investigate potential causal relationships. Publicly available datasets and transcriptomic data from independent studies were used to examine the association between gene expression in kidney tissue and eGFR. RESULTS: In total, 57 plasma proteins were associated with eGFR, including one novel protein. Of these, 23 were additionally associated with CKD. The strongest inferred causal effect was the positive effect of eGFR on testican-2, in line with the known biological role of this protein and the expression of its protein-coding gene (SPOCK2) in renal tissue. We also observed suggestive evidence of an effect of melanoma inhibitory activity (MIA), carbonic anhydrase III, and cystatin-M on eGFR. CONCLUSIONS: In a discovery-replication setting, we identified 57 proteins transethnically associated with eGFR. The revealed causal relationships are an important stepping stone in establishing testican-2 as a clinically relevant physiological marker of kidney disease progression, and point to additional proteins warranting further investigation.

Animal-attached devices have transformed our understanding of vertebrate ecology. To minimize any associated harm, researchers have long advocated that tag masses should not exceed 3% of carrier body mass. However, this ignores tag forces resulting from animal movement. Using data from collar-attached accelerometers on 10 diverse free-ranging terrestrial species from koalas to cheetahs, we detail a tag-based acceleration method to clarify acceptable tag mass limits. We quantify animal athleticism in terms of fractions of animal movement time devoted to different collar-recorded accelerations and convert those accelerations to forces (acceleration × tag mass) to allow derivation of any defined force limits for specified fractions of any animal's active time. Specifying that tags should exert forces that are less than 3% of the gravitational force exerted on the animal's body for 95% of the time led to corrected tag masses that should constitute between 1.6% and 2.98% of carrier mass, depending on athleticism. Strikingly, in four carnivore species encompassing two orders of magnitude in mass (

BACKGROUND: Epigenomic (e.g., DNA methylation [DNAm]) changes have been hypothesized as intermediate step linking environmental exposures with allergic disease. Associations between individual DNAm at CpGs and allergic diseases have been reported, but their joint predictive capability is unknown. METHODS: Data were obtained from 240 children of the German LISA cohort. DNAm was measured in blood clots at six (N=234) and ten years (N=227) using the Illumina EPIC chip. Presence of aeroallergen sensitization, was measured in blood at six, ten and 15 years. We calculated six methylation risk scores (MRS) for allergy-related phenotypes, like total and specific IgE, asthma or any allergies, based on available publications and assessed their performances both cross-sectionally (biomarker) and prospectively (predictor of the disease). Dose-response associations between aeroallergen sensitization and MRS were evaluated. RESULTS: All six allergy-related MRS were highly correlated (r>0.86) and seven CpGs were included in more than one MRS. Cross-sectionally, we observed an 81% increased risk for aeroallergen sensitization at six years with an increased MRS by one standard deviation (best-performing MRS, 95% confidence interval=[43%; 227%]). Significant associations were also seen cross-sectionally at ten years and prospectively, though the effect of the latter was attenuated when restricted to participants not sensitized at baseline. A clear dose-response relationship with levels of aeroallergen sensitization could be established cross-sectionally, but not prospectively. CONCLUSION: We found good classification and prediction capabilities of calculated allergy-related MRS cross-sectionally, underlining the relevance of altered gene-regulation in allergic diseases and providing insights into potential DNAm biomarkers of aeroallergen sensitization.

Additional file2. Methods

Additional file1: Table S1. Characteristics of the participating cohorts in analyses of DNAm in childhood and child sleep outcomes. Table S2. Overlap between cord blood and peripheral blood in childhood DNA methylation analyses. Table S3. Site-specific results for the 25 CpGs that came closest to statistical significance (p

Additional file 1. S1. Uncorrected and BACON corrected Quantile-quantile plot. Additional file S2. Enrollment flowchart for FELICITy study.

Additional file 4: Supplemental Results and Methods. In the Supplemental Results section a detailed comparison of the results obtained across cohorts can be found. In the Supplemental Methods section a detailed description of data processing for each of the population based cohorts can be found, as well as detailed specification on statistical and multi-omics analyses.

Additional file 2. Input and output of the random forest models used to classify behaviour: error rates including Out-of-bag error estimate, mean Gini decrease of each predicting variable, confusion matrix of predicted and observed behaviours, and precision and recall of each behaviour.

Further information pertaining to the study design (study area, collar set up and capture protocol), and the mathematical procedures and descriptive statistics used to derive and compare MVF results

Additional file 1: S1. The model selection results for the best candidate models investigating the number of dives per night among nine beavers in a Eurasian beaver population in south-eastern Norway. S2. The model selection results for the best candidate models investigating the diving probability among nine beavers in a Eurasian beaver population in south-eastern Norway. S3. List of aquatic species abundance in Saua river in south-eastern Norway. S4. The model selection results for the best candidate models investigating the diving habitat selection among nine beavers in a Eurasian beaver population in south-eastern Norway. S5. The model selection results for the best candidate models investigating the individual context-dependent diving selection for distance to riverbank among nine beavers in a Eurasian beaver population in south-eastern Norway. S6. The model selection results for the best candidate models investigating the individual context-dependent diving selection for clay sediment among nine beavers in a Eurasian beaver population in south-eastern Norway. S7. The model selection results for the best candidate models investigating the individual context-dependent diving selection for mud sediment among nine beavers in a Eurasian beaver population in south-eastern Norway. S8. The model selection results for the best candidate models investigating the individual context-dependent diving selection for sand sediment among nine beavers in a Eurasian beaver population in south-eastern Norway. S9. The model selection results for the best candidate models investigating the individual context-dependent diving selection for rock sediment among nine beavers in a Eurasian beaver population in south-eastern Norway. S10. The model selection results for the best candidate models investigating the individual context-dependent diving selection for species richness of focal species among nine beavers in a Eurasian beaver population in south-eastern Norway. S11. The model selection results for the best candidate models investigating the diving distance to riverbank among nine beavers in a Eurasian beaver population in south-eastern Norway. S12. The model selection results for the best candidate models investigating the diving distance to beaver lodge among nine beavers in a Eurasian beaver population in south-eastern Norway. S13. The model selection results for the best candidate models investigating the diving distance to beaver lodge among dives within 150 m of the lodge of nine beavers in a Eurasian beaver population in south-eastern Norway.

Additional file 1. Supplementary figures and dead-reckoning formulae.

Additional file 1. Supplemental Figures.

Additional file 18: Figure S4. ROC curves for significant CpG-outcome associations: Presented are the receiver operating characteristic (ROC) curves for each outcome-CpG pair for which there exists a statistically significant association for either M1 or M2 (Table 3). The red line is the ROC curve for the model without the CpG, and the green line is the model with the CpG. Presented are also the areas under the curve (AUC) for the respective ROCs, and a p value for the null hypothesis that the addition of the CpG to the model has no effect on the predictive performance of the model. The p value was determined using the R package pROC [87], command roc.test, method “bootstrap”. The analysis was run in the KORA F4 dataset, and, to ensure comparability of the results, the ROC curves were generated using individuals with no missing values in any of the outcomes or covariates, and the methylation data were mean imputed.

Additional file 1: Supplementary methods. Comprising the study cohort characteristics.

Figure S1: Comparison of the rate of change of altitude and speed of an ultralight (A and C respectively) and a pigeon (B and D), flying at the same time, and with the same loggers. Violin plots show the distribution of the data during the flight; the upper and lower quartiles are represented by the upper and lower extremes of the box and the median by the thick black line.

Contains details on species-specific tagging procedures and supplementary figures and tables

Additional file 4: Figures S1–S5. Figure S1 shows the results for heavy drinkers vs. light and non-drinkers; Figure S2 shows the results for heavy drinkers vs. at-risk drinkers; Figure S3 shows the results for at-risk drinkers vs. non-drinkers; Figure S4 shows the results for at-risk drinkers vs. light drinkers; and Figure S5 shows the results for light drinkers vs. non-drinkers.

Additional file 5: Tables S3–S11. Table S3 shows the results for heavy drinkers vs. non-drinkers; Table S4 shows the results for heavy drinkers vs. light drinkers; Table S5 shows the results for heavy drinkers vs. light and non-drinkers; Table S6 shows the results for heavy drinkers vs. at-risk drinkers; Table S7 shows the results for at-risk drinkers vs. non-drinkers; Table S8 shows the results for at-risk drinkers vs. light drinkers; Table S9 shows the results for light drinkers vs. non-drinkers; Table S10 shows the results for heavy and at-risk drinkers vs. light and non-drinkers; and Table S11 shows the results for heavy, at-risk and light drinkers vs. non-drinker.

Additional file 4: Figure S6. The importance of each variable ordered by mean Gini decrease for the model predicting behaviours including slope of terrain; (a Pygmy goats with ‘Pitch’ as the most important variable and (b) Alpine ibex with ‘Static X’ as the most important variable. Table S5. The median and 1st and 3rd quantile of acceleration, for each behaviour and species, for the three variables that are in the top 5 most important variables for predicting behaviour of both pygmy goats and Alpine ibex. Table S6. Confusion matrix showing the observed behaviours and predicted behaviours when using a random forest model built using pygmy goat training dataset and tested on the Alpine ibex training data set. Italicised cells are the true positives where the behaviour has been correctly predicted. Table S7. Confusion matrix showing the observed behaviours and predicted behaviours, including the gradient of terrain for locomotion behaviours, when using a random forest model built using pygmy goat training dataset and tested on the Alpine ibex training data set. Italicised cells are the true positives where the behaviour has been correctly predicted. (Downhill = D, Flat = F, Uphill = U).

Additional file 1: Table S1. Details of the individuals and training the male Alpine ibex to have collars put on and taken off. Table S2. Description of training protocol. Figure S1. A male ibex being rewarded standing in the protective feeding station (step 3). Figure S3. Three target male ibex rewarded in their designated protective stations (step 4, only two were successfully trained beyond this step). Figure S4. Holding the collar around a male ibex neck while he stands in the protective station, one trainer holds the collar while the second provides the reward (step 7). Table S3. Total time observed of each behaviour for each individual pygmy goat (G) or Alpine ibex (IB) in seconds.

Additional file 3: Notes including additional information on sensitivity analyses, the derivation of the DNAm-based predictors, cohort-specific details (background and study design, as well as data collection and pre-processing), and all legends to supplemental tables and figures.

Additional file 1 Supplementary information. This document includes the following supplementary information: supplementary methods, supplementary figures, supplementary tables, supplementary reference list.

Additional file 2: Figure S1. Sensitivity analysis of control probes principal components analysis. The first five principal components were included in regression models.

Additional file 7: Figure S2. Explained variance of the first 30 principal components of the metabolite measure principal component analysis, performed in the discovery cohort KORA F4.

Background The difference between an individual's chronological and DNA methylation predicted age (DNAmAge), termed DNAmAge acceleration (DNAmAA), can capture life-long environmental exposures and age-related physiological changes reflected in methylation status. Several studies have linked DNAmAA to morbidity and mortality, yet its relationship with kidney function has not been assessed. We evaluated the associations between seven DNAm aging and lifespan predictors (as well as GrimAge components) and five kidney traits (estimated glomerular filtration rate [eGFR], urine albumin-to-creatinine ratio [uACR], serum urate, microalbuminuria and chronic kidney disease [CKD]) in up to 9688 European, African American and Hispanic/Latino individuals from seven population-based studies. Results We identified 23 significant associations in our large trans-ethnic meta-analysis (p

Additional file 3 Review history.

Additional file 1. Information for each individual goat included in the study including their body weight and age, the respirometry measurements taken in the study, and the values predicted or measured for each individual in each enclosure. An ethogram of all the behaviours with additional details.

Additional file 2 Methods for building and refining random forest models to predict the behaviour of Alpine ibex and pygmy goats. Table S4. A list of the accelerometry and magnetometry variables that are used or calculated for the random forest model. Including the name, and label, the description of the variable and its calculation. Figure S4. Recursive feature elimination plots showing the cross-validated model accuracy when a different number of acceleration and magnetometry variables are included in the random forest models for classifying the behaviours of (a) Alpine ibex and (b) pygmy goat. Figure S5. Random forest error plots across 500 trees for classifying each of the nine behavioural states (Aggression, Browsing (pygmy goats only), Climbing (Alpine ibex only), Grazing, Grooming, Lying down, Running, Shaking, Standing, Trotting and Walking) and Out-of-bag (OOB) error estimates for each different model at 10 Hz for both species (a,b) including the models with: (c,d) balanced observations and (e,f) reduced behaviour classes. Figure S6. Random forest error plots across 500 trees for classifying each of the nine behavioural states including terrain slope for locomotion behaviours (Aggression, Browsing (pygmy goats only), Climbing (Alpine ibex only), Grazing, Grooming, Lying down, Running, Shaking, Standing, Trotting and Walking) and Out-of-bag (OOB) error estimates, for (A) Alpine ibex and (B) pygmy goats. Table S5. The variable reduction process to reach the final selected model.

Additional file 1: Table S1. Criteria for model selection during stepwise removal of insignificant terms in linear mixed models. Models with and without the fixed-effect term ‘mean temperature’ were compared by ANOVA with maximum likelihood to produce the final models shown in Table 2.

Additional file 6. We provide the data, methods, and results regarding the replication of the Liu et al. approach; including accompanying supplementary Tables S12–S20, and supplementary Figures S6–S14. Table S12 shows the results for heavy drinkers vs. non-drinkers; Table S13 shows the results for heavy drinkers vs. light drinkers; Table S14 shows the results for heavy drinkers vs. light and non-drinkers; Table S15 shows the results for heavy drinkers vs. at-risk drinkers; Table S16 shows the results for at-risk drinkers vs. non-drinkers; Table S17 shows the results for at-risk drinkers vs. light drinkers; Table S18 shows the results for light drinkers vs. non-drinkers; Table S19 shows the results for heavy and at-risk drinkers vs. light and non-drinkers; and Table S20 shows the results for heavy, at-risk and light drinkers vs. non-drinker. Figure S6 shows the results for heavy drinkers vs. non-drinkers; Figure S7 shows the results for heavy drinkers vs. light drinkers; Figure S8 shows the results for heavy drinkers vs. light and non-drinkers; Figure S9 shows the results for heavy drinkers vs. at-risk drinkers; Figure S10 shows the results for at-risk drinkers vs. non-drinkers; Figure S11 shows the results for at-risk drinkers vs. light drinkers; Figure S12 shows the results for light drinkers vs. non-drinkers; Figure S13 shows the results for heavy and at-risk drinkers vs. light and non-drinkers; and Figure S14 shows the results for heavy, at-risk and light drinkers vs. non-drinker.

Additional file 8. List of all authors included in the BIOS consortium.

Additional file 1. Methods expanded. Text S1. Device set up and capture protocol. Text S2. The importance of having the correct coordinate system and axis alignment. Text S3. Magnetometer calibration, rotation correction and deriving yaw (heading)—Gundog.Compass() explained. Text S4. Step counts as a distance estimate—Gundog.Peaks() explained. Text S5. Time Data in R (POSIXct). Text S6. VPC dead-reckoning—Gundog.Tracks()—explained.

Additional file 8: Figure S3. Presented are the first 8 principal components (PCs) of the metabolite data in KORA F4 (after scaling of the individual metabolites) coloured according to sex, intake of lipid-lowering drugs, and smoking habits. “Expl. Var” is the variance explained by the given PC. Some clustering is observed for sex within the first 2 PCs, but no other obvious clusters emerge for any of the other phenotypes or PCs.

Additional file 1: Figure S1. Pearson’s correlation plot of replicated proteins in: A) KORA; B) HUNT. Figure S2. ROC curve comparing the bootstrap ranking LASSO selected protein model with age and sex model predicting incident MetS in KORA, showing the AUCs, their 95% CI and the difference (delta AUC) and p-value of the DeLong test comparing both models. Figure S3. ROC curve comparing the bootstrap ranking LASSO selected protein model with age and sex model predicting prevalent MetS in KORA (A) and HUNT (B), showing the AUCs, their 95% CI and the difference (delta AUC) and p-value of the DeLong test comparing both models. Figure S4. Calibration plots of the bootstrap ranking LASSO-selected MetS diagnostic model in: A) KORA; B) HUNT. Figure S5. STRING protein-protein interaction network constructed using the prevalent or incident MetS associated proteins in KORA without adding additional interactor proteins.

Additional file 3: Random forest model results. Figure S6. The importance of each variable retained in the models predicting behaviour and behaviours including terrain slope. Table S5. The median and 1st and 3rd quantile of acceleration, for each behaviour and species, for three variables. Table S6. Confusion matrix showing the observed behaviours and predicted behaviours (in seconds) when training the random forest model built using the pygmy goat training dataset. Table S7. Confusion matrix showing the observed behaviours and predicted behaviours (in seconds) when using a random forest model built using pygmy goat training dataset and tested on the Alpine ibex training data set. Table S8. Confusion matrix showing the observed behaviours and predicted behaviours, including the gradient of terrain for locomotion behaviours, when training the random forest model built using the pygmy goat training dataset. Table S9. Confusion matrix showing the observed behaviours and predicted behaviours, including the gradient of terrain for locomotion behaviours, when using a random forest model built using pygmy goat training dataset and tested on the Alpine ibex training data set.

Additional file 2: Figures S1���S7 showing forest plots for all significant associations not presented in the main figures, as well as secondary traits (categorical MRS), comparison of MRS and DNAmAA effect sizes across renal traits.

For a given research question, there are usually a large variety of possible analysis strategies acceptable according to the scientific standards of the field, and there are concerns that this multiplicity of analysis strategies plays an important role in the non-replicability of research findings. Here, we define a general framework on common sources of uncertainty arising in computational analyses that lead to this multiplicity, and apply this framework within an overview of approaches proposed across disciplines to address the issue. Armed with this framework, and a set of recommendations derived therefrom, researchers will be able to recognize strategies applicable to their field and use them to generate findings more likely to be replicated in future studies, ultimately improving the credibility of the scientific process.

Additional file 1. The estimated coefficients of variables for each general linear model used: (1) effect of collar size and weight on VeDBA, (2) VeDBA measured by the harness and (3) difference between collar and harness. Bold font indicates that 95% confidence intervals for coefficients did not overlap zero.

Four kinds of magic-size CdS clusters and two different regular CdS quantum dots have been studied by x-ray total scattering technique and pair distribution function method. Results for the regular CdS quantum dots could be modelled as a mixed phase of atomic structures based on the two bulk crystalline phases, which is interpreted as representing the effects of stacking disorder. However, the results for the magic-size clusters were significantly different. On one hand, the short-range features in the pair distribution function reflect the bulk, indicating that these structures are based on the same tetrahedral coordination found in the bulk phases (and therefore excluding new types of structures such as cage-like arrangements of atoms). But on the other hand, the longer- range atomic structure clearly does not reflect the layer structures found in the bulk and the regular quantum dots. We compare the effects of two ligands, phenylacetic acid and oleic acid, showing that in two cases the ligand has little effect on the atomic structure of the magic-size nanocluster and in another it has a significant effect., Comment: 8 pages in the main paper, 6 figures in the main paper, 7 pages in the electronic supporting information, 4 figures in the electronic supporting information

Boxplots of the methylation values for the CS-CS individuals (current smokers at both baseline and follow-up) and the NS-NS individuals (never smokers at both baseline and follow-up). Figure S4a shows the technically adjusted beta values and Figure S4b shows the methylation beta values after residualization to account for confounding (see Statistical Methods). (PDF 23 kb)

Highly specialized diving birds display substantial dichotomy in neck length with, for example, cormorants and anhingas having extreme necks, while penguins and auks have minimized necks. We attached acceleration loggers to Imperial cormorants Phalacrocorax atriceps and Magellanic penguins Spheniscus magellanicus, both foraging in waters over the Patagonian Shelf, to examine the difference in movement between their respective heads and bodies in an attempt to explain this dichotomy. The penguins had head and body attitudes and movements that broadly concurred throughout all phases of their dives. By contrast, although the cormorants followed this pattern during the descent and ascent phases of dives, during the bottom (foraging) phase of the dive, the head angle differed widely from that of the body and its dynamism (measured using vectorial dynamic acceleration) was over four times greater. A simple model indicated that having the head on an extended neck would allow these cormorants to half the energy expenditure that they would expend if their body moved in the way their heads did. This apparently energy-saving solution is likely to lead to greater heat loss though and would seem tenable in slow-swimming species because the loss of streamlining that it engenders would make it detrimental for fast-swimming taxa such as penguins.

Methods. Changing shapes for frequency distributions. Figure S1. A 3-d scatter plot (g-sphere) of static (orthogonal) tri-axial acceleration data. Figure S2. A spherical coordinateâ s visualization of (a) postural state plotted onto the surface of a sphere in three-dimensional space, (b) points joined together in chronological order, (c) projecting the data outwards from the sphere according to other parameters. Figure S3. A spherical histogram (Dubai plot) visualization to depict frequent postural states. Figure S4. Histogram, Frequency shape (stacked), fixed shape (skittle) from urchin plots. Figure S5. G-urchin of skittle shape and stacked frequency urchins emitted from the centre of each facet of the sphere. Figure S6. Overview of user interface for a program in which spherical plots can be created. Figure S7. G-spheres and comparable g-urchins derived from a rod-mounted tri-axial accelerometer showing fly-fishing visualisations. (DOCX 5289 kb)

Extensive morphological and physiological adjustments are assumed to underpin the adaptations of diving birds to high thermoregulatory costs. However, the role of behavioral adaptations has received little consideration. We have assessed the relative importance of physiological and behavioral adjustments in aquatic endotherms by studying the case of the poorly insulated great cormorant (Phalacrocorax carbo) in two contrasting thermal environments: Normandy (water temperature 12°C) and Greenland (water temperature 5°C). Major differences were found in the feeding behavior of birds breeding in the two regions. Greenland birds showed a 70% reduction in time spent swimming relative to those in Normandy. Reduction in Greenland was achieved first by reducing time spent on the surface between dives and secondly by returning to land in between intensive bouts of diving. Total daily energy intake of cormorants was similar in both areas but prey capture rates in Greenland were 150% higher than those in Normandy. Our study shows that in a cold foraging environment, poorly insulated great cormorants significantly increase their foraging efficiency. To do this they rely on ecological adaptive patterns (minimization of time spent swimming in cold water and increased prey capture rates) far more than physiological adaptations (minimizing instantaneous costs). This finding supports predictions by Grémillet and Wilson (1999) that great cormorants can cope with a wide range of abiotic parameters despite their morphological handicaps, provided they can adjust their distribution to exploit dense prey patches.

In some seabirds, foraging trips have been defined as either long or short, with the length of time spent traveling to the foraging area apparently a critical feature in determining foraging trip length. Using logger technology, together with complimentary data from published studies, we investigated traveling and foraging times in 18 free-living Adélie Penguins Pygoscelis adeliae, which were foraging for chicks. Most deep, foraging dives were distributed around the center of the foraging trip. This central tendency was particularly apparent if the cumulative amount of undulations in the depth profile (indicative of prey capture) was considered during deep dives; values started to increase before 20.9% and ceased after 67.2% of the dives had occurred. This concentration of the feeding activity in the middle of the foraging trip indicates that birds traveled to and from a prey patch whose location varied little over the birds' trips. These data form the basis for a simple model that uses traveling and foraging times together with projected rates of prey ingestion and chick and adult gastric emptying to determine that there are occasions when, to optimize rates of prey ingestion while at sea for both adults and chicks, birds should conduct foraging trips of bimodal lengths.