HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Full Text (PDF) All Versions of this Article: 292/5/R2028    most recent 00691.2006v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via ISI Web of Science (1) Citing Articles via Google Scholar Google Scholar Articles by Green, J. A. Articles by Holder, R. L. Search for Related Content PubMed PubMed Citation Articles by Green, J. A. Articles by Holder, R. L.

COMPARATIVE AND EVOLUTIONARY PHYSIOLOGY

Estimating the rate of oxygen consumption during submersion from the heart rate of diving animals

¹Department of Zoology, La Trobe University, Bundoora, Melbourne, Australia; and ²Centre for Ornithology, School of Biosciences and ³Department of Primary Care and General Practice, University of Birmingham, Edgbaston, Birmingham, United Kingdom

Submitted 2 October 2006 ; accepted in final form 3 January 2007

	ABSTRACT

TOP ABSTRACT MATERIALS AND METHODS RESULTS DISCUSSION APPENDIX A APPENDIX B GRANTS REFERENCES

 
How animals manage their oxygen stores during diving and other breath-hold activities has been a topic of debate among physiologists for decades. Specifically, while the behavior of free-ranging diving animals suggests that metabolism during submersion must be primarily aerobic in nature, no studies have been able to determine their rate of oxygen consumption during submersion (O₂D) and hence prove that this is the case. In the present study, we combine two previously used techniques and develop a new model to estimate O₂D accurately and plausibly in a free-ranging animal and apply it to data for macaroni penguins (Eudyptes chrysolophus) as an example. For macaroni penguins at least, O₂D can be predicted by measuring heart rate during the dive cycle and the subsequent surface interval duration. Including maximum depth of the dive improves the accuracy of these predictions. This suggests that energetically demanding locomotion events within the dive combine with the differing buoyancy and locomotion costs associated with traveling to depth to influence its cost in terms of oxygen use. This will in turn effect the duration of the dive and the duration of the subsequent recovery period. In the present study, O₂D ranged from 4 to 28 ml·min^–1·kg^–1, indicating that, at least as far as aerobic metabolism was concerned, macaroni penguins were often hypometabolic, with rates of oxygen consumption usually below that for this species resting in water (25.6 ml·min^–1·kg^–1) and occasionally lower than that while resting in air (10.3 ml·min^–1·kg^–1).

penguin; model; oxygen stores; hypometabolism

As indicated above, the majority of behavioral studies investigating the time budgets of diving animals suggest that surface interval (tS) durations are not long enough to allow for recovery from anaerobic metabolism i.e., that dives are generally almost completely aerobic (49) and thus do not exceed the DLT, let alone the cADL. This implies then that, when determining cADL, either the calculation of usable oxygen stores and/or the estimate of O₂D are incorrect. There is surely a limit to the inaccuracy in estimates of total body oxygen stores (35). Certainly, it seems unlikely that total body oxygen stores could be two or three times greater than current estimates, as would be necessary for all diving to be within the cADL (40). This indicates that most of the inaccuracy lies in the estimation of O₂D. As a result, a more accurate method to estimate O₂D is required, if we are to develop a solid working knowledge of the energetic costs of diving from which we can investigate physiological adaptations more robustly.

Progress with this work has been limited since it is not possible to measure O₂D directly (14). Several experimental techniques have been employed to estimate O₂D, but the results of these studies vary substantially. Respirometry has been used to calculate O₂, while animals are at the surface between dives from man-made holes in the ice of the Antarctic (13), and/or while they swim or rest in a static water channel or flume (e.g., Ref. 17). A recent new approach utilizes body movement as a calibrated proxy for O₂ (55). A first step in measuring O₂D has been made by measuring the decline in the partial pressure of oxygen (PO₂) in the air sacs during dives of emperor penguins (Aptenodytes forsteri; 34). In free-ranging animals, isotopic techniques, including doubly labeled water, have often been used to estimate O₂ during complete foraging trips and to equate this (or a proportion of this) to O₂D (e.g., Ref. 15). Heart rate (f_H) has been recorded during free-ranging diving in a variety of species, including elephant seals (Mirounga leonina; 31), Antarctic fur seals (Arctocephalus gazella; 5), and South-Georgian shags (Phalocrocorax georgianus; 2). If a relationship has been established between f_H and O₂ for a given species, then it is possible to estimate the O₂ of a complete dive cycle [tD and the subsequent tS] from the mean f_H of that dive cycle (e.g., Refs. 20 and 24). Unfortunately, however, f_H measured while the animal is submerged or on the surface cannot be used to estimate O₂ directly, either while submerged or during the subsequent surface interval, as studies have shown that during diving the relationship between f_H and O₂ is not consistent (4, 21).

Mathematical or modeling approaches have also been developed to ascertain O₂D. Recent work has included the effects of buoyancy and metabolic rate while resting (e.g., Ref. 30). Woakes and Butler (57) developed a model to calculate mean O₂D at the mean tD and tS of tufted ducks (Aythya fuligula) diving freely in a laboratory tank. The concept used was based on a technique first applied by Scholander (48), which obtained an estimate of O₂D by partitioning the oxygen uptake between dives into the amount of oxygen consumed during the surface interval and the amount consumed during the preceding dive. While instructive, this approach could only provide an estimate of O₂D for "average" dives. With a greater experimental data set and improved mathematical model, Halsey et al. (27) refined this approach. They controlled for the effects of tS by plotting changes in oxygen uptake after 10 s (sufficient time to replenish the oxygen stores of tufted ducks) against tD. Then, by assuming that O₂ while at the surface and while descending and ascending to the foraging site (a tray at constant depth) were fixed quantities regardless of tD, the slopes of these regressions represented O₂D during the foraging part of the dive (i.e., the part of the dive varying with tD). This represented the first direct and tangible estimate of O₂D during diving. Due to a relatively small sample size, Woakes and Butler (57) and even Halsey et al. (27) were obliged to assume that O₂ at the surface was independent of tS duration, thus regressing only tD against oxygen uptake at fixed durations of tS.

In contrast to these laboratory studies, recordings of heart rate during free-ranging diving are able to include a far greater number and variety of dives, allowing some of the limitations and assumptions of previous models to be eliminated. If f_H over the dive cycle is used to estimate O₂, then it is possible to develop the models of Scholander (48), Woakes and Butler (57), and Halsey et al. (27) and not only quantify the rate of oxygen consumption during the dive cycle, but investigate how variability in different elements of the dive cycle (e.g., diving depth, tD, tS) might influence it. This development of the original model by Scholander (48) apportions total heart beats recorded over the diving cycle (_HDC) to the time spent either submerged or on the surface. In the present study, we use an example data set of over 500,000 dives by macaroni penguins (Eudyptes chrysolophus) for which heart rate has been recorded, to illustrate how statistical modeling can take advantage of such a large number of dives, and demonstrate how O₂D can be predicted from factors as straightforward as the tS and dive depth. A key finding is that the duration of a surface interval often reflects the occurrence of energy-costly activities during the previous dive, not simply the duration of that dive.

We suggest that our estimates of O₂D in macaroni penguins are the most direct, plausible, and meaningful obtained to date for a free-ranging animal. This approach could have applications in other studies where it is of interest to look at rate of oxygen consumption at a fine scale, beyond that which can be achieved using respirometry or other methods that measure oxygen uptake, which is only equivalent to oxygen consumption when an animal is in steady state. Such applications could include studies of apnea (1, 39) or short periods of exercise (e.g., Ref. 33).

	MATERIALS AND METHODS

TOP ABSTRACT MATERIALS AND METHODS RESULTS DISCUSSION APPENDIX A APPENDIX B GRANTS REFERENCES

 
Data Collection

The approach in the present study is based on the reanalysis of data collected on the diving behavior and energetics of macaroni penguins (22, 23). Briefly, the penguins were implanted with data loggers that recorded heart rate every 10 s and dive depth every 2 s. Data were analyzed from the winter migration of 18 penguins (9 male, 9 female) during 2002, since this period represented the most complete set of data for a single phase of the annual cycle (23). Data from both sexes were pooled, since, while the diving behavior and capacity varies between male and female macaroni penguins (23), there is no difference between the sexes in the relationship between f_H and O₂ (26).

Several diving species intersperse foraging dives with shallow nonforaging dives, thought to have a role in the recovery from oxygen debt (e.g., Ref. 50). This behavior is not shown by macaroni penguins so, as in previous studies (23), only foraging dives (maximum depth > 10 m) were used in the analysis. Since f_H was recorded less frequently than dive depth, dives with a tD or tS of <10 s (which would have included the majority of dives to < 10 m in any case) were ignored. Dives with a tS > 600 s were also ignored, as these were assumed to represent interbout periods rather than surface intervals for recovery from the previous dive and preparation for the next. In total, 514,106 dives were analyzed.

Conversions of f_H to mass specific rate of oxygen consumption (sO₂; ml·min^–1·kg^–1) were made using equation 14 from Green et al. (26). Conversion to sO₂ is appropriate for this species, as they have a mass exponent of one (25). As with all studies of this type, using f_H as a proxy for sO₂ assumes that the oxygen pulse (cardiac stroke volume multiplied by oxygen extraction by the tissues) varies with f_H in a predictable way under all physiological circumstances (6). It is not possible to use the heart rate method to predict O₂ accurately for individual animals (6). Therefore, in the present study, all models were constructed and calculations computed using f_H and _HDC for each individual animal. Resulting mean values of f_H and _HDC for all animals were then converted to estimates of sO₂, as described above. The standard error of the estimate (SEE) for estimates of sO₂ was calculated following the procedures established by Green et al. (25).

Calculation

Structure of models. All calculations were based on the principle that the total volume of oxygen consumed during a dive cycle (O₂DC) is a function of tD, tS, O₂D, and the rate of oxygen consumption during the subsequent surface interval (O₂s). This is summarized by Eq. 1.

(1)

An important point to note is that O₂s represents the rate at which oxygen is consumed by the tissues during the surface interval. This is not equal to total oxygen uptake during the surface interval, which has to be sufficient to match oxygen used during both the previous dive and the subsequent surface interval and will, therefore, be equal to O₂DC. As explained above, a calibration relationship between f_H and O₂ can be used to estimate mean O₂ of a complete dive cycle from the mean f_H of that dive cycle. Therefore, the same relationship can also be used to estimate the O₂DCof a dive cycle from the _HDC for that dive cycle. Thus, Eq. 1 can be converted to Eq. 2.

(2)

Note that f_HD and f_HS from Eq. 2 are theoretical quantities that can be used to predict O₂D and O₂s, but are not equal to f_H measured during submersion and the subsequent surface interval. As discussed earlier, within the dive cycle the f_H/O₂ relationship is not constant, and f_H during submersion cannot be used in any simple fashion to predict O₂D. It can be seen that for macaroni penguins (and indeed for many other diving animals) we can measure three of the components of Eq. 2 (_HDC, tD, and tS) and that with a large enough data set it should be possible to use linear regression to determine f_HD and f_HS_. The aim of the calculation procedure outlined in the present study is to determine f_HD and f_HS mathematically for each penguin, such that O₂D and O₂s can be calculated for the population. The calculation also allows f_HD and f_HS to vary with, for example, tD rather than be fixed quantities.

Four models were produced to estimate f_HD and f_HS, with each successive model including more details that may reasonably be expected to improve predictive validity. Because the statistical process is relatively involved, and possibly unfamiliar, we show the derivation of these models and how the models were used to calculate values for macaroni penguins. Model 1 is the simplest of the models, and the others add additional variables to this basic template. Each model described below incorporates between two and four constants, which are represented by Greek letters (, , , and ).

Model 1

While the physiological state of a diving animal changes unpredictably during the course of a dive as they adjust their circulation and balance the demands of foraging, locomotion, and oxygen conservation (e.g., Ref. 3), it is reasonable to suppose that changes are much more predictable during the subsequent surface interval. If so, then at any given value of tS, we assume that within an individual animal, f_HS is a constant. In other words, for all surface intervals of, for example, 30 s, f_HS will always be similar. Therefore, at a given tS, f_HS x tS will also be a constant (). Similarly, if a surface interval is a period of recovery from the previous dive, then it seems reasonable to expect that during dives with a given surface interval the rate of energy expenditure (represented by f_HD) during the previous dive will also be a constant (). While many studies show a close association between tD and tS, there will still be sufficient variability in tD within a given category of tS so that Eq. 2 can be rearranged to give Eq. 3.

(3)

Model 2

Certain studies, both in the laboratory and in the field, indicate that mean f_H of dive cycles is lower during longer dives (3, 24). It is possible that f_HD also decreases as tD increases. If this is correct, then, rather than assume f_HD is constant within a category of tS, it is replaced with a simple linear function of tD (Eq. 4). Incorporating this relationship into the model should improve the estimates of f_HD and f_HS. As before, at a given tS, f_HS x tS is assumed to be a constant (), therefore, substituting f_HD in Eq. 2 with Eq. 4 gives us Eq. 5.

(4)

(5)

Model 3

For over 35 years the diving behavior of free-ranging aquatic species has been obtained by the attachment to study animals of data loggers that record changes in hydrostatic pressure over time (36). The diving parameters tD, tS, and hence dive cycle duration, are derived from the recorded depth data. Thus when data have been obtained on tD, they are usually also available for the maximum depths of those dives (depD). It is clear that depD is an important factor when diving animals prepare themselves to dive. Changes in buoyancy with increasing depth during dives affect the energetic cost of dives and the amount of air inhaled prior to submersion (46, 51, 56). These factors will conspire together to influence tD and f_HD. For example, power costs of swimming may vary with depth (53). Alternatively, deeper dives may have a proportionately shorter bottom-feeding time that will affect the total energy requirement (32). Thus, the inclusion of depD may improve the predictive power of the model. Therefore, the potential effect of depD on f_HD was added as a simple linear relationship between f_HD, and depD (Eq. 6). As before, at a given tS, f_HS x tS is assumed to be a constant (), so substitution of Eq. 6 instead of f_HD into Eq. 2 gives Eq. 7.

(6)

(7)

Model 4

Finally, to test all logical, yet simple, combinations, depD and tD were both considered as factors that might influence f_HD. In this case, a multiple linear relationship between f_HD, tD, and depD (Eq. 8) was tested. As before, at a given tS, f_HS x tS is assumed to be a constant () and so substitution of Eq. 8 in place of f_HD in Eq. 2 gives Eq. 9.

(8)

(9)

Calculations

Each dive for each animal was classified by tS bins of 4 s. Within each bin for each animal, _HDC was regressed as a function of tD (and depD or tD², as appropriate, depending on which model was being tested). Regressions were only performed if there were > 40 dive cycles with the appropriate tS within each bin (which left a total of 506,909 dives for development of all four models). Each regression generated values for between two and four constants (depending on the model). Values of weighted means (see APPENDIX A) were then calculated for these constants across all animals for each bin of tS. Further regression analysis investigated whether these constants varied as a function of tS or whether they were fixed quantities. Polynomial functions of tS (with 95% confidence intervals) were then developed as appropriate for these constants (termed "constant functions"). Substitution of the constants with the constant functions in Eqs. 3–9 allowed calculation of f_HD and _HDC using each of the four models. The 95% confidence limits and SEEs of f_HD and _HDC were generated by substituting the constant functions with the 95% confidence limits of the constant functions, in each of the four models.

Model Selection and Validation

First, the models were evaluated and compared with each other on the basis of the regression statistics within penguins and tS bins and subsequently by examination of how the model constants (f_HS, , , and ) varied as a function of tS (constant functions). Models with better regression statistics were presumed to be more valid.

Any further validation of the model within the data set risks forming a circular argument. While a more robust test of the models would be to input data from other diving bouts, in this case, this would entail using data from another season. The seasonal variability in diving capacity shown by macaroni penguins (22, 23) means that a test of the models with data from another part of their annual cycle is not necessarily valid. Therefore, the models were tested on the data set used to derive them, in order at least to determine which model gave the most accurate estimates of _HDC and, within that, f_HD and f_HS. To limit the circularity of the validation, the data were organized in a different fashion from that for the derivation of the models. For each animal, all dives were initially classified into bins by tD (4 s). Within each 4-s bin of tD, dives were then classified into 4-s bins of tS and then mean depD was calculated for each bin for each animal. A grand mean of depD for all animals was then calculated for each bin of tD and tS and this data set was used for the validation process. Initially, 1,625 combinations of tD and tS were generated. However, combinations that were only undertaken by one penguin were removed from the data set since, while these combinations were common (15.1% of all combinations), they represented only a small fraction of all dives recorded (0.6% of all dives) and so tended to skew subsequent analyses. The resulting validation data set contained 506,607 dives in 1,381 combinations.

The final step of the validation process made an assumption that the underlying premise of the exercise was correct and that the process would derive a value (or range of values) for O₂D, which indicate that all dives would be within the cADL. As discussed in our other work on this topic, cADL should not be considered a fixed value for a particular species (22, 24). If we accept that O₂D can vary, then by definition, cADL will also vary. In previous studies, we have used mean rate of oxygen consumption for the dive cycle (O₂DC) as a proxy for O₂D, determined the cADL for dives of different duration, and found that up to 70% of dives had a tD in excess of the cADL. We concluded that O₂D must be less than O₂DC. Therefore, we have adopted a similar approach in the present study and used each model to estimate O₂D and, therefore, cADL for each combination of tD and tS. For comparison, the same approach was also applied to O₂DC estimated from measured _HDC. For each model (and measured _HDC), the proportion of dives in excess of cADL was calculated. Confidence intervals (95%) for these estimates were calculated by repeating the procedure using O₂D calculated from the lower 95% confidence limit of f_HD – SEE and O₂D calculated from the upper 95% confidence limit of f_HD + SEE (22). As in our previous work (22), macaroni penguins are assumed to have usable body oxygen stores of 58 ml/kg, although, in this case, we allowed uncertainty in this estimate of ±10%, which was incorporated into the calculation of the confidence intervals.

	RESULTS

TOP ABSTRACT MATERIALS AND METHODS RESULTS DISCUSSION APPENDIX A APPENDIX B GRANTS REFERENCES

 
Table 1 shows the summary statistics for the regressions of each model within bins of tS, within each penguin. Mean r² was significantly different between all four models (ANOVA with Tukey's post hoc testing, P < 0.001 in each case). Both the intercept and slope were significant in nearly every case in model 1. However, the mean r² of model 1 regressions was the lowest of the four. All three multiple regressions fitted the data very well, although the coefficients of the slopes were statistically significant, most frequently, and had higher partial correlation coefficients for model 3. Furthermore, model 3 had the highest mean r². Certainly, adding all of the variables into one model (model 4) did not appear to produce the most descriptive model.

View this table: [in this window] [in a new window]   Table 1. Results of four different linear regression models to predict total dive cycle heart beats (HDC) from time spent submerged and dive depth in 18 macaroni penguins

View larger version (30K): [in this window] [in a new window]   Fig. 1. Plot of predicted total heart beats during a dive cycle (HDC) as a function of measured HDC. Each point represents a unique combination of dive duration (tD) and surface interval duration (tS). Predictions were made for 4 different models (models 1–4). See text for details of models. Shades denote the number of dives observed at each combination of tS and tD in which black = 1,001–10,000, dark grey = 101–1,000, midgrey = 11–100 and light grey = 1–10. Also shown is the line of equality (solid line) and the weighted regression line between the 2 variables (dashed line).

View this table: [in this window] [in a new window]   Table 2. Regression relationships and mean percentage error between measured and predicted total HDC

View this table: [in this window] [in a new window]   Table 3. Percentage of dives that would have a duration shorter than the calculated aerobic dive limit (cADL) when the rate of oxygen consumption during diving (O2D) was calculated using 4 different models

Figure 2 shows how the different constants associated with model 3 varied as a function of tS (constant functions). In all models, f_HS was equal to divided by tS. Weighted polynomial regressions (APPENDIX A) showed that f_HS (3rd order), (4th order), and (4th order) varied significantly as a function of tS (Fig. 3). Substitution of these constant functions into Eqs. 6 and 7 generated the predictive equations for f_HD (Eq. 10) and _HDC (Eq. 11).

(10)

(11)

O₂D varies considerably as tS and depD vary. APPENDIX B contains similar derivations for the other three models. Figure 3 shows a frequency distribution of all the values of O₂D calculated for the validation data set using Eq. 10 (model 3) and converted to rate of oxygen consumption. During the winter, O₂D varied from 4 to 28 ml·min^–1·kg^–1 with a median value of 11.25 ml·min^–1·kg^–1. The 95% confidence intervals of these predictions, incorporating both the uncertainty in the model development and the error in conversion of f_H to O₂ ranged from 1 to 40 ml·min^–1·kg^–1 but were clustered tightly around the predictions (Fig. 3).

View larger version (19K): [in this window] [in a new window]   Fig. 2. A: variation in mean (open symbols) heart rate during a surface interval between two dives (fHS) as a function of the duration of that surface interval (tS). B and C: variation in the mean (open symbols) of 2 constants ( and ; dimensionless) relating to the response of heart rate during the previous dive as a function of tS. See text for details of derivation of these constants. Also shown are fitted, weighted, polynomial regression curves with 95% confidence intervals (dashed lines) and their equations. All three relationships were significant (P < 0.001, r2 = 94.8, 97.5, 89.1, respectively). Data are taken from 18 male and female free-ranging macaroni penguins diving during their winter migration.

View larger version (24K): [in this window] [in a new window]   Fig. 3. Frequency distribution (black bars) of rate of oxygen consumption during submersion (O2D) estimated from tD, tS, and maximum dive depth (depD) using model 3. Also shown are the minimum (grey shadow) and maximum (white shadow) 95% confidence intervals of this distribution.

	DISCUSSION

TOP ABSTRACT MATERIALS AND METHODS RESULTS DISCUSSION APPENDIX A APPENDIX B GRANTS REFERENCES

Factors Influencing Dive Behavior and Energetic Costs

Perhaps the most interesting aspect of the present study is the factors that proved to be significant and aided in the selection of a model and those which did not. Model 3 gave the best predictions. However, despite being the simplest of all of the models, including only the effects of tS on f_HD, model 1 also gave excellent predictions of both f_HD and _HDC. While model 3 improved upon this by adding an effect of depth, the most important factor in defining the metabolic cost of a dive appears to be the subsequent tS. To some extent this is a question of cause and effect, as it is unlikely that the rate of metabolism during a particular dive is set by the subsequent tS. Physiologically speaking, Eq. 10 should really be rearranged such that tS is dependent on f_HD. In other words, the rate at which oxygen is consumed while submerged will have a profound effect on the subsequent tS. Furthermore, the shape of the curve of cumulative oxygen uptake suggests that this relationship is not simply linear (28). That tS and f_HD are linked is perhaps not surprising, although it is gratifying that the process of model development should reveal this link.

It is also not surprising that depD is an important predictor of O₂D. Much recent work on diving physiology (with penguins in particular) has revealed that animals often appear to match aspects of their physiology to the intended depth of a dive. King penguins (Aptenodytes patagonicus), Adélie penguins (Pygoscelies adeliae), and magellanic penguins (Spheniscus magellanicus) modulate their inhaled air volume prior to submersion such that when their intended depth is reached they are neutrally buoyant (46, 56). Magellanic penguins also appear able to predict the rate of oxygen consumption in a forthcoming dive based on its duration and likelihood of prey capture (51). Macaroni penguins actively stroke their flippers during descent but during ascent are able to glide for at least half of the time (45). Mean stroke frequency during descent is independent of dive depth, but during ascent it decreases with increasing dive depth. As a result, in mechanical terms at least, O₂D will be influenced by depD (30, 53). These data show that penguins make decisions based on complex environmental parameters, suggesting a remarkably efficient use of time during foraging (51). In the case of macaroni penguins, O₂D and depD interact to define the tS.

While our models are unable to include the likelihood of occurrence of prey capture events, our findings may suggest that these events are critical in determining O₂D and tD. In other species of free-ranging penguins feeding on euphausids, prey capture events are associated with rapid acceleration and deceleration (44). Rapid acceleration and deceleration during horizontal swimming is energetically expensive and is therefore likely to limit tD and distance swum (18). Furthermore, prey pursuit by penguins may involve swimming at speeds higher than the optimal cost of transport, and the power costs of swimming increase with the cube of swim speed (54). As a further complication, the required prey capture speed may vary between prey patches or between different target prey species, all of which will introduce further variability (54). Respirometry studies of Weddell seals suggest that O₂D is greater during foraging dives than during exploratory dives (40). If prey capture events are unpredictable and energetically expensive, then they are likely to have an important effect in shortening or lengthening dives, and, as a result, the duration of a dive is not necessarily a guide to its energetic cost. In other words, a dive of 1 min duration could vary substantially in O₂D, depending on the type or prey and whether or not energetically expensive prey capture events occur. The duration of the bottom phase of dives and subsequent behavior during ascent (whether an immediate return to the surface or prey searching) vary substantially in macaroni penguins and are dependent on each other (45), suggesting that prey capture rates also vary substantially between individual dives.

While the rejection of models 2 and 4 shows that f_HD is not dependent on tD at all, tD is of course critical in determining the total energy expenditure of the dive (Eq. 1). There is little reason to assume that the duration of any given event should affect the rate at which energy is expended; in fact, the reverse is more likely. In a resource-limited environment, the rate at which energy is required for a given activity (which, in the case of O₂D, appears to be highly variable and dependent on extrinsic factors) is more likely to limit its duration than vice versa. Hence there is a lack of dependence of f_HD on tD. Figure 4 illustrates the complex nature of the interactions between tD, tS, and depD. Figure 4 shows the tremendous variation in combinations of tD and tS or depD. While the majority of dives have a tD-to-tS ratio of 5, this ratio varies from 0.25 to 18. Clearly, tS is influenced by factors other than tD alone. This variability in tS (i.e., recovery and preparation duration in terms of oxygen restocking, CO₂ elimination, and possibly lactate conversion) for a given tD presumably represents variability in the level of total oxygen stores before and after a dive (due to the shape of the curve of cumulative oxygen uptake) or energetic costs during the preceding dive, due either to differences in depD or prey capture events (see above) or other energetically costly activities. Similarly, the wide variation in tD at a given depD suggests that differing costs within a dive to a given depth may lead to different levels of O₂D, which will in turn affect its duration (tD).

View larger version (28K): [in this window] [in a new window]   Fig. 4. Frequency plot of combinations of tD and subsequent tS (A) and tD and maximum dive depth (B) recorded from 18 male and female free-ranging macaroni penguins during their winter migration. Shades denote the number of dives observed at each combination in which black = 1,001–10,000, dark grey = 101–1,000, midgrey = 11–100, and light grey = 1–10.

In the absence of detailed measurements or predictions, many studies have assumed that O₂D is constant across all dives and independent of other factors associated with the dive (e.g., Ref. 15). However, in the present study, we show that O₂D can have a wide range of values (Fig. 3). Some other studies have also shown this to be the case; for example, postdive respirometry of Weddell seals suggests that O₂D is subject to considerable variability (40), which is further supported by theoretical calculations (30). Further evidence in support of this comes from the only two studies that have measured DLT. Both emperor penguins (41), and to a lesser extent Weddell seals (37), show variation in their lactate response at a given tD. Figure 2 from Ponganis et al. (41) shows clearly how in one of four penguins (only 22 dives were analyzed) lactate production is substantial during a dive of 5.75 min, but in another dive of 6.25 min, postdive lactate levels were barely elevated above resting levels. This illustrates that even within an individual animal performing a small number of dives from a fixed location, either oxygen stores or O₂D must be subject to substantial variation, and DLT represents rather more than a fixed time threshold beyond which lactate accumulation always occurs, as many authors use it (e.g., Ref. 15).

As the limit of oxygen stores is approached, dives are likely to have a high component of anaerobic metabolism, as in Weddell seals and emperor penguins (Fig. 5). Unfortunately, the present study gives no insight as to what tD and activity might correspond to the DLT for macaroni penguins. It should be noted again that in Weddell seals < 3% of dives exceeded the DLT; so for macaroni penguins, the proportion of dives where O₂D does not account for all of the metabolism during a dive is also likely to be very low and correspond only to the longest and/or most energetically demanding dives. The ratio of tD/tS (Fig. 4) indicates that only a small percentage of dives by macaroni penguins are likely to exceed their DLT.

View larger version (11K): [in this window] [in a new window]   Fig. 5. Schematic diagram of the relative contributions of aerobic (solid line) and anaerobic (dashed line) metabolism toward ATP production during diving. ATP production/consumption is assumed to remain constant throughout the dive. Note that before the diving lactate threshold (DLT; grey zone in this example) is reached the majority of metabolism is aerobic in nature, leading to a linear decrease in oxygen stores (solid diamonds). The point at which aerobic metabolism reaches zero is when all the usable stores of oxygen have been exhausted and is the calculated aerobic dive limit (cADL). In dives beyond the DLT, with an increasing proportion of anaerobic metabolism, using heart rate will predict only the aerobic component of metabolism and hence will underestimate total ATP production.

In the absence of measurements, several authors have speculated on what O₂D might be. Some authors have noted that for all dives to be within the cADL, O₂D must be lower than that while animals rest in water, i.e., they exhibit (aerobic) hypometabolism. (8). Other studies have used O₂ while animals rest on land as a proxy for O₂D (30), which also suggests hypometabolism, since in most species studied to date, O₂ while resting on water is approximately twice that while resting on land. Other studies have used the metabolic cost of swimming in a static water canal but admit that there are limitations with this analysis, which are likely to lead to an overestimation of O₂D (16, 18). As mentioned above, in the current study, we have found that O₂D is not a fixed value but rather that it varies in association with depth and possibly with the performance of energetically costly activities. Predicted values of O₂D ranged from 4 to 28 ml·min^–1·kg^–1 (Fig. 4), although 93% of dives were in the range of 6 to 20 ml·min^–1·kg^–1, and the median was 11.25 ml·min^–1·kg^–1. By way of comparison, mean O₂ was 10.3 ml·min^–1·kg^–1 while macaroni penguins rested on land and 25.6 ml·min^–1·kg^–1 while they rested in water (26). To illustrate the effect of using different proxies for O₂D, we have recalculated cADL and the proportion of dives within this limit by using a variety of methods (Table 4). This exercise illustrates that for macaroni penguins at least, the present approach is the most plausible estimate of mean O₂D to date, but in the absence of similar data, resting O₂ in air is the most reasonable substitute.

In conclusion, we have combined two established techniques (multiple regression calculation of O₂D in the laboratory and prediction of O₂ from f_H in free-ranging animals) to show how the rate of oxygen consumption during submersion (or indeed any other breath-hold activity) can be estimated from heart rate and time-budget information. Using macaroni penguins as an example, we present the most plausible estimates to date of O₂D in a free-ranging diving animal. Many studies have reported that an unlikely proportion of dives by animals appear to exceed cADL (30). However, few studies have been prepared to examine further whether they have been overestimating O₂D, and/or underestimating total body oxygen stores. As mentioned above, it seems likely that the uncertainty lies in estimates of O₂D. Our estimates are lower than previously used values, suggesting that these animals rely on (aerobic) hypometabolism to some extent. More importantly, in contrast to many other proxies used for O₂D, our findings are consistent with the behavioral observations that suggest the majority of dives performed by macaroni penguins are essentially aerobic in nature.

	APPENDIX A

TOP ABSTRACT MATERIALS AND METHODS RESULTS DISCUSSION APPENDIX A APPENDIX B GRANTS REFERENCES

 
Calculation of Weighted Means

Weighted means of regression coefficients were calculated for each bin of tS since the strengths of the regressions varied between individual penguins. Each regression parameter estimate (E; the intercept and one or more slopes) for each penguin has a standard error (S). The weight (W) of each estimate is calculated as

The mean estimate () is calculated as

The standard error () of this weighted mean is calculated as

	APPENDIX B

TOP ABSTRACT MATERIALS AND METHODS RESULTS DISCUSSION APPENDIX A APPENDIX B GRANTS REFERENCES

 
Model 1

Total _HDC was regressed as a function of tD within each 4-s bin of tS for each penguin. Both (equivalent to f_HS x tS) and (equivalent to f_HD) were significant in most of the regressions (Table 1). A weighted mean ± SE (APPENDIX B) of f_HD and f_HS was calculated for each 4-s bin of tS using the coefficient and its mean ± SE for each penguin represented in that bin. The weighting accounted for differences between individual penguins in the variance of the mean value of the dependent variable in each tS bin. Mean f_HS and f_HD were then plotted as a function of tS and a weighted polynomial regression (maximum 4th order) was used to ascertain whether there were relationships between tS and f_HS or f_HD. Both f_HS (3rd order) and f_HD (4th order) varied significantly as polynomial functions of tS (Fig. 1). Substitution of these relationships into Eq. 2 generated our first predictive equation of _HDC (Eq. 10).

(B1)

Model 2

In a similar fashion to model 1, _HDC was regressed as a function of tD and tD². Therefore, as well as f_HS, coefficients and , respectively, were generated for each penguin in each bin of tS, most of which were statistically significant (Table 1). As before, a weighted mean of f_HS, , and was calculated for each bin of tS and plotted against tS (Fig. 2). Weighted polynomial regressions showed that f_HS (3rd order), (4th order), and (4th order) varied significantly as a function of tS. Substitution of these relationships into Eq. 5 generated our second predictive equation of _HDC (Eq. 11).

(B2)

Model 4

As with models 1–3, _HDC was regressed as a function of tD, tD², and depD. Therefore, as well as f_HS, coefficients , , and were generated for each penguin in each bin of tS, many of which were statistically significant (Table 1). As before, a weighted mean of f_HS, , , and was calculated for each bin of tS and plotted against tS (Fig. 4). Weighted polynomial regressions showed that f_HS did not vary significantly as a function of tS (Fig. 4). However, the three constants (2nd order), (2nd order), and (4th order) all did vary significantly as a function of tS. Substitution of these relationships into Eq. 7 generated our fourth and final predictive equation of _HDC (Eq. B2).

(B3)

	GRANTS

TOP ABSTRACT