ECOLOGY OF THE EASTERN FENCE LIZARD, SCELOPORUS …



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These data are rate limiting to constructing models of bioenergetics (Grant ???), and we relate here how we solved some in our lab of the major challenges to collecting these data.

Metabolic rate is one of the most frequently measured physiological variables (Withers 1992). The metabolic rate of an animal is the rate at which it converts chemical energy to heat and external work. Heat is liberated by all the energy consuming processes in the body and therefore the metabolic rate reflects the sum of all such reactions. Since energy is being transformed from chemical energy in food to heat and external work this energy is said to be consumed. Therefore, the metabolic rate of an animal represents the rate of its energy consumption. In ectotherms, the 3 factors that influence metabolic rate are 1) ambient temperature 2) activity and 3) feeding status (fed vs. unfed). At any body temperature when an ectothermic animal is resting and fasting its metabolic rate is termed to be standard metabolic rate (SMR). Since activity has such a dramatic effect its level must be specified when metaolic rate is measured. The only level of activity that can be specified quantitatively across species is rest. The problem is that it is not a small challenge to get an experimental animal to rest without restraining it during measurements of metabolism. Restraint is not a viable option since it stresses the animal causing a change in its metabolic rate. We have devised a method to account for different level of activity during measurements of metabolism and therefor allows us to accurately estimate SMR.

Feeding after a previous fast cause an increase in metabolic rate of animals even though in all other aspects except the ingestion of food all conditions are kept constant. Many terms are used to describe this increase in metabolic rate. Rubner (1929) coined the term specific dynamic effect (SDE) sometimes called specific dynamic action (SDA) due to a mistranslation from German. Other terms used are “the work of digestion”, calorigenic effect of feeding, diet induced thermogenesis (DIT) and many others. The specific dynamic effect varies in magnitude and duration among animals and in many instances is proportional to the size of the meal and its greater for protein than fat or carbohydrates (Hill and Wyse 1989). There is not a single universally accepted explanation for SDE. The measurement of SDE is not trivial. In this poster we describe how we overcome some of the problems in the methodology of measuring as well as calculating SDE. Although we study fence lizards the techniques described in this poster are applicable to measurement of SDE in other ectothermic animals.

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computer mouse to a digitizer. of a monitor placed in a box facing up connected to a VCR in which the tape is played. While playing the tape one moves the mouse on the surface of the Plexiglas to follow exactly the lizard in the metabolic chamber. The output is a file that contain graphics as well as numerical data of the distance traveled and the total number of moves of an animal in the metabolic chamber during a 24 minute run.

DESCRIPTION OF HOW IT IS DONE ???

The cost of digestion is estimated as the integrated difference between digesting and standard metabolic rates of adjusted files. Figure ??? shows the output from the JAVA program that calculates the distance traveled and the total number of moves of an animal in the metabolic chamber during a 24 minute run. Figure 2 shows the uncorrected and corrected 24 hour record of the same animal at 35 C during unfed and fed runs. The corrections were performed according to the procedure described above.

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Our first attempts of measuring activity in the metabolic chamber involved viewing all tapes and counting the number of movements the animal made during each data collection run. This method was fairly low tech (videotaping and counting), BUT very labor intensive, and provided only a rough estimate of the true cost of activity. In order to reduce the labor involved and improved the accuracy we tried to use the newly developed motion activity detector (MAD1) by Sable Systems(. This system could have provided us with an easier and more accurate way of monitoring activity simultaneously with metabolic measurements. Unfortunately, the MAD1 did not work in our system. Our double wall water filled chambers may have been too stable to register vibrations in the chamber caused by the lizards’ activity. Bruce Grant then developed the JAVA mouse digitizing program to quantify the activity from the videotapes records. This system provides a repeatable and quite accurate estimate of the lizards’ activity in the chamber. Its main advantage is that the output is a data file that can be directly used using any spreadsheet program without any further intermediate steps.

Lizard metabolic rates undergo an endogenous diurnal rhythm (photophase - scotophase) that is superimposed on both the fed and unfed data. This does not pose a problem as long as the metabolic measurements for fed and unfed lizards are paired based on the clock time at which each measurement was made and the runs last for at least a 24-hour period.

We have collected fed and unfed data on at least 6 lizards at 25 0C, 30 0C and 35 0C. These data has not been analyzed yet (we have our work cut up for us). Hopefully when these data are analyzed we will be able to estimates the cost of digestion at various temperatures.

(was the animal's MR high because of simple activity or because of a physiological process?).

Secor, S. M.; Phillips, J. A. 1997.

Future Studies.

( Expand laboratory physiological studies. We plan to intensify our investigations of the costs of digestion by lizards using a repeated measures fractional factorial experiment to assess the effects of sex, age (size), and temperature on the rate and total cost of digestion, food passage rate, digestive efficiency, and maximal rate of food consumption. These data will be used to build our bioenergetic model of Sceloporus physiological performance to assess how variations in microclimatic conditions constrain individual energy budgets.

( Begin modeling the optimal allocation of resources. We will develop individual-based models of the phenotypic responses by these lizards to variation in their thermal environment to assess the evolutionary consequences of the unusual life history characteristics these lizards exhibit. Such models simulate how variation in an individual’s allocation to growth, storage, or reproduction affects current plus expected future reproduction. This is an important way to assess the fitness consequences of alternate life history strategies and predict the optimal phenotype. For example, are large yearling females more fit who delay reproduction until their third summer? Are adult males more fit who stop growing at a smaller terminal body size than their sisters seem to be capable of doing?

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Figure 1 - The Eastern Fence Lizard, Sceloporus undulatus.

Figure 2 - Field site in the New Jersey Pine Barrens.

Figure 7 - Representative movement data in the chamber for three runs (A, B, and C from Figure 6) for a lizard (female #4, unfed runs, 35oC, on 6/17/98).

Figure 1 - The Eastern Fence Lizard, Sceloporus undulatus.

Figure 2 - Field site in the New Jersey Pine Barrens.

Figure 3A - Schematic of the apparatus used to estimate lizard metabolic rates showing a double walled glass chamber containing a lizard, the water bath controlling the chamber temperature, and the Sable Systems Oxygen Analyzer and associated gas flow controllers we used to measure O2 consumption (ml O2/hr).

Figure 3B – Closeup of the double walled glass chambers and lizards inside.

Figure 3C – Panasonic infra-red video camera connected to time lapse VCR (not shown).

Figure 3D - Neslab circulating water bath.

Figure 3F - Sable Systems Oxygen Analyzer S-3A/1

Figure 3E - Gas multiplexor arrangement.

Figure 4 – Photo of TV monitor, image of lizards during a metabolic run, and standard PC mouse functioning as a “digitizer” to record the position of the lizard on the screen.

Figure 5 – see ESA Vatnick 898 fig5.doc

Figure 6 – Representative data showing the relationship between the total distance of movements in the chamber and metabolic rate (for lizard #4, unfed runs, 35oC, on 6/17/98). Each datum stems from a 24 minute O2 consumption run and analysis of the corresponding segment of the video tape movement record. The actual movement traces for points marked A, B, and C appear in Figure 7.

Figure 9 – Graphical result of removing the effect of elevated metabolism due to movements in the chamber. “Raw data are from Figure 6 (lizard #4), and “adjusted data” are plotted from Table 1 and were found from “adjusted MR” = MR - 0.0252 * distance, using the slope of the regression of raw MR vs. distance

Figure 10 – “Adjusted” metabolic rates for the fed and unfed runs versus time over the duration from feeding to defecation for lizard #4 (6/17-19/98, 35oC).

|Lizard |Temp. |Feeding |O2 Cons. |O2 Cons. ml/24hr/g |Adj O2 Cons |Cost of |Cost of |% Cost of Digestion |

|Id # |(o C) |Status |ml/hr/g | |ml/24hr/g |Activity |Digestion | |

| | | | | | |% |ml/24hr/g | |

| | |Digesting |1.10 |26.5 |22.8 |13.9% | | |

|0-1-5-0 |33o | | | | | |1.21 |4.57 % |

| | |Not |1.05 |25.1 |21.7 |13.6% | | |

| | |Digesting | | | | | | |

| | |Digesting |1.58 |38.0 |36.8 |3.00% | | |

|0-5-5-3 |33o | | | | | |2.40 |6.3% |

| | |Not |1.42 |34.0 |34.4 |-1.2% | | |

| | |Digesting | | | | | | |

| | |Digesting |0.73 |17.5 |12.1 |30.9% | | |

|0-1-5-0 |30o | | | | | |2.13 |12.2% |

| | |Not |0.63 |15.2 |9.92 |34.7% | | |

| | |Digesting | | | | | | |

| | |Digesting |0.68 |16.4 |16.4 |* | | |

|0-1-5-0 |25o | | | | | |2.24 |13.6% |

| | |Not Digesting |0.59 |14.2 |14.2 |* | | |

| | |Digesting |1.59 |38.1 |39.4 |-3.4% | | |

|1-2-2-3 |25o | | | | | |7.86 |20.6% |

| | |Not |1.03 |24.8 |22.2 |10.6% | | |

| | |Digesting | | | | | | |

| | |Digesting |1.91 |46.0 |36.2 |21.2% | | |

|1-1-2-3 |25o | | | | | |4.00 |8.70% |

| | |Not |1.83 |43.9 |32.2 |26.6% | | |

| | |Digesting | | | | | | |

| | |Digesting |1.83 |43.9 |38.3 |12.9% | | |

|1-5-1-5 |25o | | | | | |10.36 |23.60% |

| | |Not |1.16 |27.9 |27.9 |* | | |

| | |Digesting | | | | | | |

| | |Digesting |1.47 |35.3 |29.7 |15.7% | | |

|1-2-2-5 |25o | | | | | |7.57 |21.44% |

| | |Not |1.03 |24.8 |22.2 |10.6% | | |

| | |Digesting | | | | | | |

|L200807 |36o |Not |0.54 |12.83 |12.05 |6.1% | | |

| | |Digesting | | | | | | |

|L49 |36o |Not |0.46 |11.1 |11.37 |-2.4 | | |

| | |Digesting | | | | | | |

|L431016 |36o |Not |1.02 |24.55 |18.31 |25.4% | | |

| | |Digesting | | | | | | |

Table 1. Temperature, feeding status, and activity dependent metabolic rate (ml/g/h) and the calculated cost of digestion for S. undulatus. All runs lasted approximately 24 hours. Lizards were lab acclimated and varied in age, sex, and physiological state. The “Adjusted O2 Consumption” is the estimated 24 hr O2 consumption based on summing only the data runs (28 minute samples) for which the lizard moved less than 30 times. “Cost of Activity” was calculated by dividing the difference between the unadjusted and adjusted 24 hour average oxygen consumption with and without the high movement runs. “Cost of Digestion” was calculated by subtracting the adjusted average 24 hour oxygen consumption of the fed and unfed runs. The field “% Cost of Digestion” was calculated by subtracting the adjusted average 24 hour oxygen consumption of the original and “Adjusted” O2 consumption fields and dividing by the unadjusted.

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Our research is designed to integrate field and laboratory data to understand how individual Sceloporus undulatus amass resources into an ecological energy budget, and how they then allocate these resources to either growth, maintenance, storage, or reproduction in their life histories. The field component reported in Grant et al. is designed to elucidate the population and biophysical ecology of these lizards. The laboratory component we report here is designed to construct a bioenergetics model of individual resource budgets based upon modeling rates of metabolism and digestive performance as a function of body temperature, sex, age, etc. Such a model will enable us to better understand the bioenergetic constraints on the resource allocation decisions to growth, storage, and reproduction that determine the life history characteristics observed among years in our field studies and among sites comparing with other field studies

Figure 9 - Correlation of activity rate in the chamber and metabolic rate for the lizard “Yellow 5” measured at 36oC.

Figure 6 - Summary of the steps involved in bioenergetic modeling of lizard energy budgets and life histories. The top and middle equations show how the short term physiology (metabolism and digestion) determine the amount of energy a lizard may assimilate per meal. This energy is then integrated over a breeding season to provide the energy budget for optimal allocation to growth (G), storage (S), or reproduction (R) in a life history. See text.

Year Ave. Ta (+1se) t-test

1995 22.7 +0.5

1948-1993 21.7 +0.4

1996 20.4 +0.5

Table 1. Air temperature data from the National Weather Service station at an airport near the study site for May-July in 1995, 1996 and for a 46 year run from 1948-1993 (station and years selected based on availability from the NWS internet site).

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t = 2.1, P < 0.02

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t = 1.9, P < 0.03

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Abstract

Research on individual energy budgets has contributed greatly to our understanding of a wide range of ecological phenomena from life history evolution to ecological energetics. Modeling energy budgets of animals requires a detailed understanding of the physiological processes of energy assimilation during digestion, which includes estimating the cost of digestion. However, the standard method to measure the cost of digestion (by integrating the difference between digesting and standard metabolic rates over the time between feeding and defecation) assumes that the animals are resting or at least equally active while fed and fasted, which is not always true. Thus, activity metabolism can lead to inaccurate estimate of digestive costs.

Reptiles are model animals to study

environmentally constrained bioenergetics since, being ectothermic, their ecologies are closely tied to their physical environment. Important advances in technology have yielded notable advances in modeling energy budgets in reptiles (e.g. Dunham 1993), however, the salient details of many of the physiological processes critical to energy acquisition by reptiles remain obscure. One such example is digestion.

During food digestion the metabolism of an animal increases to pay for costs in wide variety of physiological processes – including peristaltic contractions of the gut muscle, synthesis and secretory costs of digestive enzymes, transport costs of mobilized nutrients across the gut lining into the body, increases in circulation to translocate these

circulation to translocate these nutrients to elsewhere in the body, and processing costs of these nutrients once they arrive. The result of these metabolic costs can be considerable – e.g. 22% elevation of an animal’s resting and fasting metabolic rate (=standard metabolic rate, SMR) (e.g. Karasov and Anderson, 1998). The total increase in metabolism integrated over the time to digest a meal has been given a variety of terms – such as the “cost of digestion,” the specific dynamic effect (SDE) (Rubner 1929) or the specific dynamic action (SDA) (due to a mistranslation from German), calorigenic effect of feeding, diet induced thermogenesis (DIT) and many others. This effect varies in magnitude and duration among animals, is often proportional to the size of the meal, and varies with meal composition (Hill

We have developed a novel method to measure the cost of digestion of the Eastern Fence Lizard (Sceloporus undulatus) that removes the effects of lizard activity during metabolic measurements. We used infrared time-lapse video imaging and a Sable Systems oxygen analyzer to simultaneously measure the rates of activity and metabolism. Using a Scalex Plan Wheel and our own device driver, we analyzed the videos to assess lizard movement rates during their metabolic rate measurements. Data indicate that even slight chamber activity (e.g. moving 1m/24 min) can significantly elevate metabolism. Our results over a range of temperatures show significant improvement in estimating the cost of digestion by removing the effects of activity metabolism.

and Wyse 1989). Regardless of which term one prefers (we will use SDE, as defined in Figure1 ), and despite that no clear understanding exists of what accounts for it, there are major logistic impediments to estimating it that are essential to solve in order to estimate energy budgets from data on the amount of food consumed and thermal opportunities for feeding and digestion.

Perhaps the most important challenge to estimating SDE results from the fact that the study individual’s metabolism must be monitored continuously during the entire time required to digest its meal. As mentioned above, the integrated difference between this trajectory and a “resting and fasted” SMR equals the SDE. The problem is that for many animals, such as reptiles, the time

Introduction

Environmental changes at the local, landscape, and global scales will undoubtedly affect rates of species extinction as well as the functioning of the ecosystems that sustain those who remain. To predict how climate change affects populations, communities, and ecosystems requires an understanding of how individuals physiologically and behaviorally respond to their changing environments and how these responses affect population- and ecosystem-level dynamics. Modeling environmentally constrained bioenergetics is at the core of constructing this understanding (Dunham et al. 1989, Grant and Porter 1992).

Reptiles are model animals to study

Fence Lizards (Sceloporus undulatus). In our method, we simultaneously measure metabolic rate under controlled temperature conditions while also videotaping lizard movements in the chamber using an infra-red time lapse camera and VCR. We present representative data showing the large increases in metabolism due to individual activity in the chamber, and the “corrections” for activity.

To our knowledge, there has not been a previously reported attempt to estimate the effect of individual activity in the chamber while estimating SDE – consequently, others have relied on the assumption that individual activity levels between fed and unfed runs are equal and therefore offset. We present data that refute this assumption.

needed for digestion is at least a day, even under optimum conditions. However, in our experience with freshly captured lizards from our NJ Pine Barrens site, lizards are never resting and inactive for more than a few hours at a time. During much of the day, our lizards are very active as they move back and forth in the metabolic chamber (e.g. trying to escape). These movements in the chamber cause major spikes in the metabolic rate (see below), and unless one watches the lizards while measuring their metabolism one cannot see these effects. Restraint is not a viable option since it stresses the animal causing a change in its metabolic rate.

In this poster, we describe a novel method we have devised to study the cost of digestion (SDE) of Eastern

Results

Representative data showing the relationship between the total distance of movements in the chamber and metabolic rate for a lizard (#4, unfed, 6/17/98, 35oC) appear in Figure 7. Each datum stems from a 24 minute O2 consumption run and analysis of the corresponding segment of the video tape of its movements. Representative movement patterns for points marked A, B, and C appear in Figure 8. Clearly, as this lizard moved more in the chamber its metabolic rate was elevated. The regression line (y = 0.0252 x + 4.8863) has an R2 = 0.84 and is significant. Metabolic rates for runs during which the lizards were actually immobile were about 40%

actually immobile were about 40% lower than for runs with a meter or more total movement.

Figure 9 (A-D) shows additional movement vs. metabolic rate relationships for two additional lizards (#5 and #9, 6/17-19/98, unfed and fed trials, 35oC) and for the fed trial at 35oC for the same lizard as in Figure 7 (Figure 9E).

Table 1 shows the calculated results of adjusting the raw metabolic rate data to remove the effects of activity for our representative lizard #4 (see Methods and Figures 5 and 6).

Finally, putting it all together comparing fed and unfed runs for our representative lizard (#4), Figure 10 shows the “adjusted” metabolic rates for the fed and unfed runs versus time

for the fed and unfed runs versus time over the duration from feeding to defecation. Note that the trajectory for the fed runs is above that of the unfed runs - this is the cost of digestion. Numerical integration of the difference between these curves reveals a total expenditure of 26.56 mlO2 , or 533.3 J, which is the SDE.

Since this lizard consumed a cricket that weighed 0.304 gm wet weight (or 0.076 gm dry weight assuming 25% dry weight) and using a conversion of 25,600 J/ gm dry weight of cricket (Waldschmidt et al. 1986), we estimate that the lizard consumed 1945.6 J. Thus, the SDE was 27.4% of the food consumed for lizard #4 at 35oC.

Parallel calculations for lizards #5 and #9 reveal similar numbers.

and #9 reveal similar numbers. Among all three lizards, the average SDE = 513 J which represents an average of 24% of the energy of the food they consumed. These numbers are certainly reasonable according to previously published literature (e.g. Karasov and Anderson 1998, Secor and Phillips 1997).

What If One Ignores Activity in Finding the Cost of Digestion?

Table 2 shows estimates of (1) the Specific Dynamic Effect (( SDE), (2) the % of ingested food energy spent on digestion, and (3) the % rise in metabolism due to digestion using the adjusted metabolic rates (after removing the effects of activity) and using the raw data on metabolic rates (without correcting for activity). Note that the unadjusted estimates of SDE, etc., generally underestimate the adjusted values. This is due to the activity of the lizards in a complex way. Importantly, lizards differed in total movement rate, and in their relationship between movement and metabolism. For example, lizard #9 had significantly higher movement in

had significantly higher movement in the chamber during its unfed runs than during its fed (t-test, P < 0.05). In fact, its metabolism was so elevated that showed an SDE less than zero!

Figure 11 shows average adjusted and unadjusted metabolic rates for four lizards in fed and unfed states at 300C. There was a significant difference between the means of adjusted fed and unfed metabolic rates (t-test, t = 3.38, P ................
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