Physiology on a Landscape Scale: Plant-Animal

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COMP. BIOL., 42:431–453 (2002)

Physiology on a Landscape Scale: Plant-Animal Interactions1 WARREN P. PORTER,2* JOHN L. SABO,3† CHRISTOPHER R. TRACY,* O. J. REICHMAN,†

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NAVIN RAMANKUTTY‡

*Department of Zoology, University of Wisconsin, 250 N. Mills St., Madison, Wisconsin 53706-1695 †National Center for Ecological Analysis and Synthesis, 735 State St., Suite 300, Santa Barbara, California 93101-3351 ‡Department of Atmospheric, Oceanic & Space Sciences, University of Wisconsin, 1225 W. Dayton St., Madison, Wisconsin 53706-1695

SYNOPSIS. We explore in this paper how animals can be affected by variation in climate, topography, vegetation characteristics, and body size. We utilize new spatially explicit state-of-the-art models that incorporate principles from heat and mass transfer engineering, physiology, morphology, and behavior that have been modified to provide spatially explicit hypotheses using GIS. We demonstrate how temporal and spatial changes in microclimate resulting from differences in topography and vegetation cover alter animal energetics, and behavior. We explore the impacts of these energetic predictions on elk energetics in burned and unburned stands of conifer in winter in Yellowstone National Park, chuckwalla lizard distribution limits in North America, California Beechey Ground squirrel and Dusky Footed woodrat mass and energy requirements and activity patterns on the landscape, their predator prey interactions with a rattlesnake, Crotalus viridis, and shifts in that food web structure due to topographic and vegetative variation. We illustrate how different scales of data/observation provide different pieces of information that may collectively define the real distributions of a species. We then use sensitivity analyses of energetic models to evaluate hypotheses about the effects of changes in core temperature (fever) global climate (increased air temperature under a global warming scenario) and vegetation cover (deforestation) on winter survival of elk, the geographic distribution of chuckwallas and the activity overlap of predator and prey species within a subset of commonly observed species in a terrestrial food web. Variation in slope and aspect affect the spatial variance in solar radiation incident on the ground, hence ground surface temperature, at the same elevation, same hourly 2 m air temperatures, and wind speeds. We illustrate visually how spatial effects and landscape heterogeneity make statistical descriptions of animal responses problematic, since multiple distributions of their responses to climate, topography, and vegetation on the landscape can yield the same descriptive statistics, especially at high (30 m) resolution. This preliminary analysis suggests that the model has farreaching implications for hypothesis testing in ecology at a variety of spatial and temporal scales.

INTRODUCTION Living organisms function in the context of the abiotic and biotic worlds. One of the great challenges today in biology is understanding how and when those contexts influence developmental processes and functions during the lives of organisms. One important abiotic context is the thermal environment. Spatial variation in the thermal environment provides a template on which physiological, population and community dynamics are played out. The study of biophysics (Porter and Gates, 1969; Porter et al., 2000) provides the basis for understanding this abiotic template. In this paper we apply newly developed models of animal function that are based on morphology, physiology, and behavior influenced by biophysical contexts to explore how climate, topography, and vegetation interact to determine the physiological performance, geographic distribution and strength of species interactions of both ecto- and endothermic animals. Specifically, we present examples of how vegetation alters the effect of climate on the food requirements of elk in Yellowstone National Park, the geographic range of a com-

mon desert lizard, the chuckwalla (Sauomalus obeseus), and predator-prey interactions between an ectotherm and two alternate prey species (both endotherms). In all cases, we demonstrate how a biophysical approach can be used to make testable predictions about variation in biological processes across ‘real’ landscapes at a variety of meaningful spatial scales. Until recently it has been impossible to quantitatively model, evaluate, and explore how landscape scale climate, topography, and vegetation interact with animal properties to affect animal energetics and behavior. Until recently it has been impossible to tie environmentally driven individual energetics and behavior to population dynamics and community structure. Recent work (Ives et al., 1999) shows that, ‘‘The variance in total community biomass depends only on how species respond to environmental fluctuations.’’ They show that ‘‘negative covariances (between competition and predation) are counteracted by increased species-level variances created by interspecific competition.’’ And they point out, ‘‘the same results can be shown to hold for more complex models with multiple trophic levels.’’ Our basic approach is to do calculations based on key animal properties and microclimates available to compute energetic costs, food requirements, soil degree-days and time available for activity. We compute

1 From the symposium Plant/Animal Physiology presented at the Annual Meeting of the Society for Integrative and Comparative Biology, 3–7 January 2001, at Chicago, Illinois. 2 E-mail: [email protected] 3 Present address of John Sabo: Department of Biology, P.O. Box 871501, Arizona State University, Tempe, Arizona 85287-1501.

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heat and water mass balances for required animal expenditures that are contingent on their selected/maintained core temperature and from that calculate required wet food intake that is constrained to be no greater than the body mass per day. Activity time calculations depend on light/no light availability and local microclimates that may force heat/water stress and reduce/limit activity. These models are described in detail elsewhere (Porter et al., 2000) and include several sub-models: microclimate (Porter et al., 1973; Mitchell et al., 1975), animal (Porter et al., 1973, 1994, 2000; Porter, 1989), gut, lung, and thermoregulatory models (Porter et al., 2000), and now a GIS interface that allows for prediction across real landscapes. The microclimate model that specifies available microclimates above and below ground, in sun and shade, has been tested in bogs, forests, grasslands, and desert ecosystems (Porter et al., 1973; Mitchell et al., 1975; Kingsolver, 1979; and unpublished data). The animal models have undergone extensive laboratory and field testing. Test methods included respiratory gas analysis, food and water mass balance, doubly labeled water (Porter, 1989; W.P.P., unpublished Arabian oryx data), and body temperature radiotelemetry, sometimes using all techniques at once (Porter et al., 1994). Species studied have ranged from mosquitoes to butterflies, from fish to frogs, from tiny fence lizards to giant land iguanas in Galapagos, from hummingbirds to ostriches, from mice to elephants. The animal models include 1) porous insulation for fur/feathers, 2) distributed heat generation and respiratory evaporation, 3) coupled molar balance models of respiratory and digestive systems, 4) animal posture, especially appendage effects, vital to estimates of field metabolism, especially for large animals over 20 kg, 5) steady state and transient capabilities, and 6) dynamic changes in local habitat selection by animals too hot or too cold out in the open (http:// www.wisc.edu/zoology/faculty/fac/Por/Por.html). The gut model couples heat generation requirements to maintain core temperature at user specified activity levels above resting with food ingestion requirements to meet the day’s integrated energy needs. The lung model is driven by hourly energy needs that specify required respiratory gas exchanges and consequent evaporative water loss from the respiratory system. A novel thermoregulatory model monitors core-skin temperature differences and initiates changes in microhabitat selection, including sun/shade selection, standing or lying down, climbing, or entering burrows/caves, initiation of water loss through the skin, or elevation in core temperature, depending on user specified priorities. The calculations for metabolic rates check for standing and lying down postures. The metabolism calculations bound the mouse-to-elephant and hummingbird-to-ostrich data for the experimental conditions used to collect the data. The calculations show increases in metabolism at high temperatures that are consistent with observed data. The calculated increases are emergent properties of the thermoregulatory model that monitors the core-skin temperature gradient (Por-

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ter et al., 2000). Together, these subroutines comprise an integrated basic principles model for understanding animal energetics and behavior on a landscape scale. When coupled with GIS based information on climate, topography and vegetation, they can be used to generate spatially explicit interpretations of animal energetics and behavior and species interactions. The models operate on an hourly timeframe over 24 hr with integrated results for a day, each month, and a year. On a 500 MHz laptop computer a full annual cycle of microclimate and animal simulations executes in approximately one-half second for a typical 30 m quadrat in a 30 m resolution DEM (digital elevation map). One such DEM of 176,000 quadrats will take 3 days to complete. On a 1.2 GHz PC the time is one day. The model system utilizes an early NASA concept of keeping RAM free by using database tables on the hard drive to supply GIS-based input data and receive GIS-based output data, thereby allowing for extremely rapid program execution. Recent results comparing calculations of metabolic rates for adult Arabian oryx on the Arabian Peninsula vs. doubly labeled water estimates of these rates collected in the field during the spring and summer (Williams et al., 2001) suggest that the models provide more detailed dynamical behavior of animal physiology, higher accuracy and sensitivity than traditional regression approaches to estimating physiological rates. Model calculations show 1) dynamic monthly changes in metabolism for resting and active Arabian oryxes on the Arabian Peninsula that agree better with experimental data than regression approaches, 2) predictions for changes in activity with season that agree well with behavioral observations in the field. Oryx calculations used a small sample of fur to obtain solar reflectivity, hair diameter, density, length, and pelt depth. Other data needed were body weight and morphology (available for most species online), body core temperatures, and independent measurements of climate from the area where field measurements of doubly labeled water were taken. The latter are used to groundtruth microclimate models. Calculations were done without knowledge of doubly labeled water data. Other model validation data are available for lizards (Porter, 1989) and for mammals and birds (Porter et al., 1994, 2000). In this paper we seek to illustrate how this modeling framework can be used to generate novel ecological hypotheses at a variety of levels of ecological organization. Specifically, we ask: 1) How might fires in Yellowstone National Park alter the energetic and food requirements of elk wintering in the park?, 2) How do climate and vegetation interact to determine the distribution limits of the chuckwalla (Sauromalus obesus) in the southwestern deserts of the United States and Mexico? and 3) How do topography and vegetation in a local landscape determine the strength of predatorprey interactions between an ectotherm, the western rattlesnake, Crotalus viridis, and two potential prey species, the diurnal California Beechey ground squir-

PLANT-ANIMAL LANDSCAPES rel, Spermophilus beecheyi, and the nocturnal Dusky Footed woodrat, Neotoma fuscipes in southern California? MATERIALS

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General overview of models and data sources The biophysical models use climate, vegetation, and soil type data at different spatial scales and from multiple sources. Climate data for the Yellowstone simulations in January and the simulations of soil temperature in North America and Central America come from a global climate data set (Leemans and Cramer, 1991). Soil properties and vegetation type data came from Task (1998) and Ramankutty and Foley (1999). For simulations at Sedgewick at finer spatial scales (30 3 30 m pixels), we use the microclimate model to calculate key climate variables, including air temperature and wind speed profiles (Porter et al., 1973), and a first principles model of available clear sky solar radiation on horizontal and sloping surfaces (McCullough and Porter, 1971). Air temperatures and wind speeds for the Los Olivos quadrangle are based on data from a single climate station at Sedgewick Ranch, Los Olivos quadrangle, southern California at 350 m elevation (2120809 to 212087.59W longitude, 348459 to 34837.59N latitude) and high-resolution 30 m digital elevation maps available through the USGS (http://edcwww.cr.usgs.gov/bin/maptest/cords55512 1301201265/scale524/type5dem/zoom58). Sedgewick Ranch GIS data on boundaries, streams, and vegetation came from the UCSB Biogeography lab (http:// www.biogeog.ucsb.edu). Air temperatures at 2 m height at other parts of the quadrangle were assumed to vary adiabatically from the reference location measurements at 25.5 C/km increase in elevation. For comparison purposes, we assume that wind speeds and air temperatures are the same between study sites on closely located north and south facing field sites at the same elevation at Sedgewick. Thus, differences in solar radiation incident on the ground at the same elevation but with different slope and aspect result in different ground temperatures and air temperature profiles up to the reference 2-m height. All simulations are hourly simulations for the average day for each month of the year for any locality (Porter et al., 1973). Climate data or calculations (air temperature, radiant temperatures, wind speed, etc.) then drive the animal model for either ectotherms or endotherms (Porter et al., 2000). These models require a variety of input data such as fur properties, body weight, and digestive efficiency (see appendix for complete list). Data for elk fur properties come from Steudel et al. (1994) and from reflectance measurements (W. P. Porter, unpublished data). Data for fur properties of the Dusky Footed woodrat, Neotoma fuscipes, were estimated from a similar sized animal of a closely related species, Neotoma floridana (Webb and McClure, 1988). Egg incubation requirements for the chuckwalla come from the only data available: observation of a single egg in laboratory thermal chambers (Tracy, unpublished data)

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and from field measurements of an egg nest (Johnson, 1965). For the temperature preferences of the simulated rattlesnake Crotalus viridis, we used radio telemetry measurements of non-pregnant females (Graves and Duvall, 1993) for Crotalus viridis for minimum activity temperature (218C), preferred temperature (26.58C) and maximum temperature (338C). These are consistent with data from (Wills and Beaupre, 2000) for the timber rattlesnake, Crotalus horridus, a temperate deciduous forest species. Metabolic rates of the simulated rattlesnake were based on the regression equation from another sit-and-wait rattlesnake, the black-tailed rattlesnake (Crotalus molossus; Beaupre, 1993): log10 (ml O2 /hr) 5 0.680 3 log10 (mass (g)) 1 3.105 3 log10 (Tcore ) 2 5.503

(1)

The conversion to rate of energy expenditure from oxygen consumption (ml O2/hr) assumed a protein diet for the simulated rattlesnake: J/sec 5 (ml O2 /hr) 3 (hr/3,600 sec) 3 (liter O2 /1,000 ml) 3 (4,397 cal/liter O2) 3 (4.185 J/cal) Q met(J/sec) 5 ml O2 /hr 3 0.00518

(2)

The physiological energy equivalent for protein for uric acid-producing animals was 18.4 kJ/g protein (Gessaman and Nagy, 1988). The physiological energy equivalent for protein for urea-producing animals was 21.4 kJ/g protein (Gessaman and Nagy, 1988). The respiratory quotient used was 0.8, the value with the least error in estimating energy equivalence (Gessaman and Nagy, 1988). Reflectivity data (Appendix) of the rattlesnake, chuckwalla, and mammals come from reflectance measurements of the skin or fur over the bandwidth 290– 2600 nm (98% of the solar energy reaching the earth’s surface) on a Beckman DK-2A spectroreflectometer (Porter, 1967). Reflectance measurements are made by placing animals so they cover a 1.5 cm circular opening on the back side of an integrating sphere attached to a Beckman DK-2A spectroreflectometer. A rectangular beam of light one cm tall and 0.1–0.5 mm wide strikes the animal surface. The reflected light from the animal reenters the ultra pure white barium sulfate coated sphere. At the top of the sphere a photomultiplier (visible/ultraviolet) sensor or a lead sulfide (near infrared) sensor records the reflected light. As the machine scans through the spectrum (2,600–290 nm), its electronics are continuously comparing the alternating input from the sample beam against the reference beam reflected from a reference plate coated with ultra pure white barium sulfate (99.99% reflective).

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FIG. 1. Figure 1 illustrates the differences in energetic cost to a simulated standing 200 kg elk in the northwest corner of Wyoming, where Yellowstone National Park is located. The points (dots) in the contour plots of elk metabolic rate simulations are at 0.5 degree latitude and longitude intervals. These points are approximately 30 miles apart. The top half of the figure shows calculated elk energy requirements to maintain 37.58C body temperature without needles on conifers (14,100–12,800 kJ/day, coded from red to yellow). The lower half shows simulations for the same elk under conifers with needles at night (12,000–10,900 kJ/day, coded from green to dark blue). Elk energetic costs for a full 24 hr cycle are approximately 20–25% lower when needles are present than when needles are absent.

Case studies The first question we ask is how might fires in Yellowstone National Park potentially alter the nighttime thermal environment and energetic requirements for elk overwintering in the park? Tree needles should provide protection against cold nighttime radiant temperatures from the open sky. Simulations of microclimate changes induced by forest burning in Yellowstone assume changing the sky radiant temperature from a clear night sky (Porter et al., 1973) to local 2 m air temperature for each hour of the night of a typical 24-hr day in January. Conifers with needles will have temperatures near the bottom of the tree nearly identical with the 2 m air temperature because of con-

vective warming of needles losing radiant heat to the night sky. Night skies are typically 15–208C colder than local air temperature (Swinbank, 1963; Gates, 1980, p. 156). Because both radiation exchange and convection exchange occur simultaneously, if there were no conifer needles the local radiant temperature would approach night-time clear sky temperature. However, as the needles lose heat by radiation, they are warmed by convection under cold night sky conditions. The convective warming by local air temperature, keeps their temperature high relative to night sky temperatures. This means the needles are emitting infrared radiation at temperatures close to local air temperature, instead of colder night sky temperatures. The

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FIG. 2. Soil temperatures at 10 cm depth above 328C were accumulated into a measure called ‘‘degree days.’’ The calculations were for horizontal exposed soil with no shade. Successful chuckwalla egg incubation requires approximately 1,700 degree days (Tracy, unpublished data). Almost all of the current chuckwalla distribution lies in areas where the soil temperatures meet these minimum incubation requirements. The only exception to this is in the extreme northern limits of the distribution. Calculations suggest that solely on the basis of soil temperature, the species should be able to live in Mexico and Central America.

thick boundary layer of air around large objects like elk means that they will be more sensitive to the radiation environment than small animals with thin boundary layer that are easily affected by local convection (Porter and Gates, 1969). We therefore hypothesized that fires, by removing tree needles, would expose overwintering elk to potentially stressful (cold) thermal conditions over winter nights. A significant increase in energy expenditure in winter due to a forest burn should affect potential survivorship at the very least, or increase the amount of vegetation harvested by herds the following spring or both. Either of these consequences could be measured experimentally. To estimate the magnitude of either consequence, we first need to determine the magnitude of the potential increase in elk energy requirements. To calculate added energy requirements, we simulated microclimates from global data at 0.5 degree latitude and longitude within the park (see Fig. 1 for location of sites), assuming first previously burned (no needles) for each location, then unburned forests and compared the energetic

costs and food requirements of similar sized elk under these two climate conditions. The second question we ask is how do the thermal and abiotic environments and vegetation (available resources) interact to determine the distribution of chuckwallas, Sauromalus obeseus, in the southwestern desert of the U.S. and Mexico? Specifically, we were interested in the relative effects of the thermal requirements for egg incubation and the availability of appropriate resource types (the presence of rocks and specific plants, mostly the creosote bush, Larrea tridentata, and small annual plants) on the geographic distribution of this lizard. To quantify the effects of these three variables, we used our models to calculate temperatures in the nest environments (10 cm soil depth) across the entire North American continent. We then compared calculated patterns of degree days, coupled with GIS data on soil and vegetation types to the known geographic distribution of S. obeseus. Calculations of degree-days for chuckwalla egg incubation are made as the annual sum of total hours above 328C

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FIG. 3. The most common soil type for each 30 mile by 30 mile square in the general area of the chuckwalla distribution does not correlate well with their distribution at this low resolution. There is moderate heterogeneity at this low resolution suggesting that soil type may not be a limiting constraint. However, these lizards require rocky crevices in which to hide. This spatial resolution appears to be inadequate for local smaller scale habitat selection.

at 10 cm depth below the ground surface. These estimates are based on observations of incubation of a single egg in the laboratory (Tracy, unpublished data) and a published report of a single nest of chuckwalla eggs in sand (Johnson, 1965). These observations represent all currently available data. Our third and final question is how do the thermal environment and the cover provided by vegetation from local climate conditions interact to determine the strength of species interactions between predator and prey; a rattlesnake and two small mammal prey, one diurnal, the other nocturnal? It is well known that slope and aspect affect the types of vegetation present across a landscape (Whittaker, 1956). However, there have been far fewer studies of the impact of slope and aspect on animal activity patterns. The overlap of activity patterns of a predator and its prey (Porter et al., 1973) can be used as an estimate of the time

prey are exposed to the risk of predation, and thus the potential interaction strength between a focal predator-prey pair. To answer this last question we focus on the effect of slope-aspect on the local thermal environment and how differences in radiant temperatures during the day offered by habitats with different orientations relative to the horizon may alter the activity patterns of predators and prey. Specifically, we hypothesized that climate would differentially alter the activity patterns of ectotherm predators and endotherm prey, and that this differential response would translate into spatially explicit variation in interaction strength. Calculations for predator-prey interactions on north and south facing slopes in the Los Olivos quadrangle of southern California assume the same 2 m hourly air temperatures, wind speeds, soil and vegetation types, and velocity profiles on both slopes. Weights of the


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FIG. 4. Vegetation type may be a critical variable for the western, southern, and eastern limits of the distribution of chuckwallas. The inferred northern distribution limit based solely on ‘‘desert’’ vegetation appears to be much further north than this species actually occurs. There is a confounding variable here that cannot be resolved at present. Namely, this boundary is also very close to the transition between Mojave Desert and Great Basin desert vegetation. Typical Mojave desert vegetation is dominated by creosote bush. Typical Great Basin vegetation consists of sagebrush, black brush, and shad scale. Chuckwallas at the northern limit of their distribution occur in black brush.

mammals are representative of adults for both species. Body weight assumed for the simulated snake was near observed maximum values for Crotalus viridis. Hours of potential activity time were calculated for an 848 g Crotalus viridis western rattlesnake and a 280 g diurnal California Beechey squirrel, Spermophilus beecheyi. Calculations are done for both a 30 degree north facing slope and a 30 degree south facing slope at 350 m elevation on Sedgewick Ranch. Calculations for mass absorbed by the gut assume that the rattlesnake consumes 1.5 times basal requirements (because of activity and growth/reproduction needs), and that the two mammal species consume 2.1 times basal requirements (Appendix). The food quality assumed for the mammals, grass hay, places some constraints on their intake and hence discretionary mass/energy (mass/energy absorbed—mass/energy expended). The gut model only allows for a mass intake on a daily basis that does not exceed the mass of the animal ingesting the food. We repeated both sets of predatorprey calculations under three sets of vegetative conditions: 1) no tree shade, full sun on the ground, 2)

nearly complete tree shade with small sun spots on the ground, and 3) full (100%) tree shade with no sun spots on the ground. RESULTS The effect of fire, canopy cover and nighttime radiant temperatures on food requirements of elk in Yellowstone in January Large-scale changes in vegetation cover (e.g., fires, deforestation) can affect local microclimates for animals on the landscape. Fires, such as those that occurred in Yellowstone National Park in 1988, strip needles from conifers and alter the genetic diversity of other prominent tree species, like aspen. There have been explorations of the consequences of these genetic changes (Tuskan et al., 1996), but no test of the consequences of forest destruction on local microclimates available to animals. Large animals are more sensitive to their radiant environment than small animals because of their thicker boundary layers that tend to insulate them from air temperature, thereby

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FIG. 5. A 30 m resolution digital elevation map for the Los Olivos quadrangle in southern California, which lies between 2120800.009 and 212087.509 west longitude and 34845.009 and 34837.509 north latitude. The red outline defines a University of California, Santa Barbara reserve, Sedgewick Ranch, which covers approximately 5,880 acres.

making them more vulnerable to radiant environmental conditions (Porter and Gates, 1969). Porous insulation like fur tends to make boundary layers even thicker when considering the animal skin as the system boundary (Stewart et al., 1993; Budaraju et al., 1994, 1997). We simulated the energetic costs of a 200 kg elk under pre- and post-burn conditions in Yellowstone National Park located in the northwest corner of Wyoming (Fig. 1). Climate and elk simulations were carried out at half degree latitude and longitude intervals (ca. 48 km apart). These low spatial resolution calculations show a general trend of warmer climates going from east to west in January in this area of Wyoming and Idaho. We also found that elk save 20–25% in metabolic expenditures when occupying habitats with needle-bearing conifers that provide nighttime shade from cold skies (compare no needles/needles, Fig. 1 top and bottom, respectively). Both simulations assume that elk maintain a standing posture in the open during daylight hours where they can absorb sunlight from a clear sky and standing at night under trees

with or without needles. Additional simulations were run assuming a low 0.5 m/sec wind speed all night in the forest, rather than the usual 24 hr wind speed cycle that reaches minimum velocity at sunrise and maximum velocity at one hour past solar noon (Table A1, Appendix, where minimum wind speed varies from 4.2–6.9 m/sec depending on the month). Reduced stress brought on by a constant low wind speed throughout the night led to only a 1% additional lower energy requirement for a 200 kg elk in both a burned and intact forest. Thus, these calculations suggest that infrared radiation, not wind, is the major factor influencing calculated differences in energy requirements for elk in January in Yellowstone. These results are consistent with a more comprehensive sensitivity analysis of the effects of climate on the energy requirements of fur-bearing mammals (McClure and Porter, 1983; Porter et al., 1994). These analyses showed that core, air and radiant temperatures are the dominant variables for small mammals, followed by fur depth, other fur properties and other variables.


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The elevation map calculated from the digital elevation map of the Los Olivos quadrangle.

Effects of climate, vegetation and soil type on the distribution limits of a lizard The geographic distribution of the chuckwalla, Sauromalus obesus, in North America appears to be consistent with the distribution of soil types and vegetation in North America (Figs. 2–4). We use energetic models to understand which hypothesis or combination of hypotheses best explains the current distribution of this species. We first examined the effect of soil temperature on the number of degree-days accumulated by chuckwalla eggs incubating in the soil as an explanation for the distribution of this lizard. Figure 2 shows microclimate effects on soil temperature calculations for every 0.58 latitude and longitude in North and Central America. Soil temperatures were calculated at the surface, 2.5, 5.0, 10, 15, 20, 30, 40, 50, and 60 cm depth. Only soil temperatures at 10 cm depth were accumulated into a measure called ‘‘degree days.’’ The calculations were for horizontally oriented soil in full sun exposure (no shade). Successful chuckwalla egg incubation requires approximately 1,700 degree days (C. R. Tracy, unpublished data). Almost all of the current chuckwalla distribution lies in areas where the soil

temperatures meet these minimum incubation requirements. The only exception to this is in the extreme northern limits of the distribution, where south facing slopes, rather than horizontal ground, could provide the warmer temperatures needed to complete incubation. Calculations suggest that solely on the basis of soil temperature, the species should be able to live throughout Mexico and Central America. This suggested that factors other than soil temperature may be important in determining the southern limit of the geographic range of this species. Spatial information on soil characteristics provides a proxy for the quality of microhabitat available to adult chuckwallas. These lizards rely on rock crevices for cover from predators and harsh climate and these crevices are found only in boulder fields or lava flows. This suggests that the type of soil itself may limit the distribution of chuckwallas. Figure 3 shows the most common soil type for each 46 3 46 km pixel in the general area of the distribution of the species. In general there is little consistency at this low resolution between the distribution of various soil types and the presence of chuckwallas, suggesting that soil

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FIG. 7. The slope/aspect map calculated from a digital elevation map of the Los Olivos quadrangle. Notice the sharp differences in slope/ aspect over small spatial scales, suggesting important microclimate differences at small spatial scales.

type may not be as important as the thermal environment. However, this extremely low level of spatial resolution may be inadequate for predicting local microhabitat selection. For example, boulders or lava may be present within large pixels but not a dominant enough feature to influence the mean soil characteristic represented in Figure 3. Much higher resolution is needed for a test of substrate as a limiting constraint on distribution. Finally, vegetation could present an important constraint on the current distribution of chuckwallas. These animals are herbivores, specializing on the flowers of creosote bushes. Further, vegetation can alter local climate (above) and thus soil surface temperatures and the degree-days available to chuckwalla eggs. Figure 4 shows vegetation distributions. The distributions suggest that vegetation type may be a critical variable for the western, southern, and eastern limits of the distribution of the species. The inferred northern distribution limit based solely on ‘‘desert’’ vegetation appears to be much further north than this species actually occurs. There is a confounding variable here that cannot be resolved at present. Namely, this boundary is also very close to the transition between Mojave

Desert and Great Basin desert vegetation. Typical Mojave desert vegetation is dominated by creosote bush, while Great Basin vegetation consists of sagebrush, black brush, and shad scale. Chuckwallas at the northern limit of their distribution occur in black brush. There is a pocket of Great Basin sagebrush in the Mojave Desert near the Granite Mountains farther south. It would be interesting to determine whether chuckwallas occur in sagebrush there, since they occur in the nearby Granite Mountains. Taken together, Figures 1 and 3 combined at this low resolution of 0.5 degree latitude and longitude suggest that the northern limit of distribution may be constrained by egg incubation requirements and that the other boundaries of the distribution may be constrained by vegetation/food requirements. At higher resolution the additional constraints of a rocky substrate would show chuckwalla actual distribution to be patchier and apparently disconnected when areas of sand or other analogous substrates intervene between rock patches. Thus, as is well-known, different scales of data/observation provide different pieces of information that may collectively define the real distributions of species (O’Neill et al., 1992).


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FIG. 8. The vegetation composition of the Sedgewick Reserve in the Los Olivos quadrangle and its implications for sun/shade microhabitats and oak acorn availability for woodrat babies.

The effects of topography and vegetation on ectotherm-endotherm predator-prey interactions Local climate varies strongly with topographic relief across a landscape. Local inputs of radiant energy depend on both slope and the orientation of this slope (aspect). By contrast, local air temperature declines with elevation as a function of air pressure. Topographic relief is a prominent feature of the landscape at the Sedgewick Reserve in southern California’s Santa Ynez River Valley. Figure 5 shows the digital elevation map for the Los Olivos quadrangle, which lies between 2120800.009 and 212087.509 west longitude and 34845.009 and 34837.509 north latitude. The red outline defines the reserve, Sedgewick Ranch, which covers approximately 5,880 acres. These digital elevation data can be used to calculate elevation (Fig. 6), slope, and aspect (Fig. 7). Strong variation in slopeaspect at Sedgewick suggests that local microclimates should also differ significantly over small spatial scales as a result of topography. In addition, the species composition of the vegetation varies spatially across the reserve (Fig. 8). By superimposing elevation, slope, aspect, and vegetation type, microclimate calculations for each 30 m by 30 m segment of the landscape in Sedgewick Ranch can be done for the entire year.

These calculations comprise input data for animal models used to calculate potential activity time. Sensitivity to key variables To give the reader a measure of metabolic sensitivity at the landscape scale (Los Olivos quadrangle) Figures 9 and 10 illustrate the sensitivity of model calculations of basal metabolic rates to two degree shifts in either core or air temperature. These temperatures represent two of the most important variables affecting animal energetics (Porter and Gates, 1969; McClure and Porter, 1983). In Figures 9 and 10 we compare the impact of a 2-degree increase in core temperature (e.g., via fever) with a similar increase in air temperature (e.g., via global warming) on metabolic demand of a 280 g Dusky footed woodrat on every 30 3 30 m quadrat within the Los Olivos quadrangle in Southern California during the month of July. We chose a two-degree warming scenario because it is the current anticipated climate warming for this region over the next 20–30 yr. The elevation of the quadrangle is lowest (179 m) in the lower left region and highest (1,049 m) in the upper right region. Fever (right half of Fig. 9) elevates metabolic costs from the ;475–600 kJ/day range across the quadrangle to the ;775–975 kJ/day range.

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FIG. 9. Sensitivity analysis of the effect of a 2 degree fever (or a 28C too high error in estimated/measured core temperature) on calculated daily metabolic costs on the Los Olivos quadrangle for a 250 g nocturnal Dusky Footed Woodrat.

However, a 2 degree warming of the air across the landscape (right side of Fig. 10 with normal 37.5 core temperature) lowers metabolic costs from the ;475– 600 kJ/day range to the 425–575 kJ/day range (Fig. 10). Water requirements are elevated both for fever and climate warming in both cases (not shown). In the case of fever this is due to enhanced respiratory water loss needed to maintain the higher metabolic rate. In the case of climate warming, it is due to more time spent at higher temperatures (lower humidities) that enhance evaporation. Life history parameter variation on the landscape Climate influences two variables that are likely very strongly related to animal fitness: activity and the energy available for growth and reproduction (‘‘discretionary energy’’). For a diurnal 280 g Beechey Ground Squirrel both variables appear to vary strongly with topography on the Los Olivos quadrangle in Southern California (Figs. 7, 11). Note particularly locations where maximum and minimum values are in close spatial proximity. Frequently, these locations occur along ridges where slope aspect is highly divergent among

adjacent pixels. Also note that minimum values may occur at multiple elevations due to topographic effects. The topographic relief shows up sharply, especially in the discretionary energy estimates (Fig. 12). Interestingly, maximum (aqua colored regions) and minimum (red colored regions) potential activity times and discretionary energy are often closely allied or even adjacent on this landscape, thereby underscoring the importance of vegetation and topographic variation across the landscape (Figs. 11, 12). Statistical descriptions will not easily highlight these kinds of important differences in how animals may utilize the landscape. Topography, climate and time available for activity—a first principles approach to quantifying variation in interaction strength Both topography and vegetation alter the activity patterns of rattlesnakes and their potential overlap in activity time with small mammal diurnal and nocturnal prey (Figs. 13, 14). Nocturnal activity predicted for rattlesnakes changes with season and is more prevalent on south facing slopes (Fig. 13). In general, rattlesnakes should become less diurnal and more nocturnal


443

FIG. 10. Sensitivity analysis of the effect of a 2 degree warming of air temperature (or a 2 degree measurement error in air temperature) on calculated daily metabolic costs on the Los Olivos quadrangle for a 250 g nocturnal Dusky Footed Woodrat.

during hot summer months (June–September for north facing slopes, May–November for south facing slopes). Total annual activity times change with slope and aspect for both species (range 5 3,535–3,744 hr for the rattlesnake on sunny 308 north vs. 308 south facing slopes; 4,255–4,379 hr for the ground squirrel on sunny 308 north vs. 308 south facing slopes; Tables 1 and 2). However, as a result of shifts in the activity period predicted for rattlesnakes, the overlap of annual activity periods changes substantially across this landscape gradient especially for rattlesnake predators and nocturnal prey (Dusky footed woodrats, Fig. 14). Annual activity overlap for rattlesnakes and ground squirrels decreases from 2,503 to 2,381 hr (122 hr) (Table 4) compared to an increase from 524 to 887 hr (363 hr) in annual activity overlap for rattlesnakes and Dusky footed woodrats (Fig. 14) in habitats with 308 north vs. 308 south facing slopes. Our models thus predict that slope-aspect should have strong effects on the diet composition and the relative per capita effects of rattlesnakes on alternate prey species. Shade cast by vegetation has an even stronger effect than slope-aspect on calculated annual activity time of

rattlesnakes and alters the effects of climate variability at larger scales on predator-prey interactions (Fig. 15). To facilitate comparisons, the same daily 2 m shade air temperature is assumed for both north and south facing slopes, although ground surface temperatures and local air temperatures near the ground will vary due to differences in solar radiation from slope, aspect, and vegetative shade. With full ground shade, there is no difference between north and south facing slope microclimates. Full canopy shade (no sun spots on the ground) reduces the potential activity time of rattlesnakes by between 73–75% (2,584–2,793 hr) while increasing the potential activity time for ground squirrels by 0–3% (0–84 hr). By contrast, when basking sites in sunspots are available between the canopy ground shade, reductions in potential activity time of rattlesnakes are much more minimal (2–7% or 66–275 hr total activity time). In both shade cases, simulated rattlesnakes cease all nocturnal activity and become completely diurnal (Fig. 15). Sunspot basking has an even stronger effect on the potential overlap in annual activity time between rattlesnakes and diurnal ground squirrels as a result of

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W. P. PORTER

ET AL.

FIG. 11. Sensitivity analysis of how topographic variation in elevation, slope, and aspect modify calculated available activity hours for a diurnal 280 g Beechey Ground Squirrel on the Los Olivos quadrangle on an average July day.

increased diurnal activity of rattlesnakes (Fig. 16). Overlap in annual activity time in patchy vs. closed canopy vegetation is completely constrained by hours available for the rattlesnake (3,469–951 hr), since the diurnal ground squirrel can be active during the entire diurnal period. For a community ecologist interested in the effect of rattlesnakes on the relative abundance of various prey species, our results suggest that the relative per capita effects of rattlesnakes on small mammal species will change along gradients of slopeaspect across the landscape. Our models thus offer a mechanistic framework for quantifying spatial variation in the strength (Paine, 1980) of species interactions across landscapes as a function of regional climate and interactions between vegetation structure and local climate. It is important to point out that the specific calculations presented above are for only one single geographic location—southern California—and may not be applicable in other regions, especially at different latitudes. The relative importance of radiant vs. air temperature to animals, even the same species, may vary as a function of latitude. For example, adult Cro-

talus horridus can be found in summer in open southern exposures in Wisconsin, but avoid these habitats in Arkansas (S. J. Beaupre, personal communication). In Arkansas they appear to be active whenever thermal cues indicate an opportunity for activity and they avoid open (sunny) habitat during the summer in forests. The observed behavior differences illustrate how these models can be used as null models (all individuals have the same behavior/physiology in space) to highlight differences between observed and predicted behavior across spatial scales. It is the failure of the null model that often points out an important difference in behavior or physiology (or underlying genetic bases) in a given locality (Adolph and Porter, 1996). Mass and energy requirements—an alternate approach to interaction strength Above, we describe a method to quantify potential changes in interaction strength between rattlesnakes and small mammal prey based on the relationship between local climate and the relative activity time of predator and prey species. However, to use activity overlap as a surrogate for interaction strength, we must


445

FIG. 12. Sensitivity analysis of how topographic variation in elevation, slope, and aspect modify calculated discretionary energy (kJ/day) for growth or reproduction for a diurnal 280 g Beechey Ground Squirrel on the Los Olivos quadrangle on an average July day.

assume that predators are limited by encounters rather than handling time (Schoener, 1971) and that increases in activity time are linearly related to increases in encounters. A second, complementary approach is to examine the annual energy requirements of the predator in different environments and make predictions about prey consumption based on minimum estimates of energy required to sustain the predator in each environment (Kitchell et al., 1974; Yodzis and Innes, 1992). In this way, activity- and energy-based calculations represent upper and lower bounds (respectively) on changes in predicted interaction strength between a predator and a prey species across the Sedgewick landscape. Tables 1, 2 and 3 summarize calculations to estimate bounds on predator and prey metabolic and water expenditures on north and south facing slopes with no tree cover, with trees that comprise a nearly closed canopy (sun spots for basking) and a completely closed canopy. Canopy closure alters not only activity patterns, but also energetic costs and gain for rattlesnakes. Metabolic costs decline by more than 50% for the rattlesnake in closed canopy environments

compared with habitats with patchy vegetation and sun spots. Metabolic costs increase by approximately 16% for the diurnal ground squirrel, but less than 4% for the nocturnal woodrat. Evaporative water loss declines with increasing shade and is lower on exposed north facing slopes than southern exposed slopes. Calculations of discretionary energy available (Dnrg) to rattlesnakes for growth and reproduction show a precipitous drop from approximately 609–518 3 103 J/yr to 86 3 103 J/yr when available sunlight goes from full sun to no sun. Reduction in radiant energy imposed by full canopy conditions reduces activity time, time at active body temperature, cumulative metabolism, and discretionary energy, since we assume the snake consumes 1.5 times basal metabolic costs. The biomass a rattlesnake would need to process to meet metabolic demands would decrease from approximately 324 g wet weight of prey/year to only 80 g wet weight of prey/year, a difference of .240 g. This reduction in mass is consistent with a reduction in prey intake of nearly one average sized animal (250–280 g) per rattlesnake per year. Hence, by changing local mi-

446

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ET AL.

FIG. 13. Calculated times of activity overlap between a simulated western rattlesnake and a simulated diurnal California Beechey squirrel on exposed 308 north and south facing slopes.

croclimate and the energetic demand of predators, vegetation may alter per capita consumption rates of prey by these predators. These per capita predation rates represent our best estimate of the lower bound to interaction strength between rattlesnakes and their small mammal prey.

Food web structure Food web structure can be defined in a variety of ways depending on the question of interest (Paine, 1980; Pimm, 1982; Polis and Winemiller, 1995). One simple approach is to quantify the number of trophic links within a given food web (Pimm, 1982). This


447

FIG. 14. Calculated times of activity overlap between a simulated western rattlesnake and a simulated nocturnal Dusky Footed woodrat on exposed 308 north and south facing slopes.

quantity divided by the number of species in a web gives a standard measure of food web ‘‘connectance.’’ Our simulations suggest that ground shading can have an effect not only on the duration of activity overlap between a predator and its alternate prey species, but also whether overlap occurs at all. Ground shading, and more specifically, heteroge-

neity in ground shading (microclimate) allows an animal to behaviorally thermoregulate by shuttling between warmer and cooler microenvironments. This behavioral thermoregulation in turn, affects energetic costs (prey/food requirements) and potential activity time for feeding. The effects are different for ectotherms and endotherms. Our calculations show that in

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ET AL.

TABLE 1. Calculated annual summaries of 848 g Crotalus viridis rattlesnake energetics and activity potential at Sedgewick Ranch, CA, 350 m elevation.*

Slope

30, 30, 30, 30,

N S N&S N&S

% Shade

Sun spots

Met (J/y) 3104

Evap (g/y)

Qavail (J/y) 3104

Wavail (g/y)

Dnrg (J/y) 3103

Dwtr (g/y)

Gwet/y

Hr/y

0 0 100 100

N/A N/A Yes No

104 122 104 29.9

406 699 266 266

155 183 156 38.4

216 254 216 53.4

518 609 519 85.8

2190 2445 249.8 2213

324 381 325 80.1

3,535 3,744 3,469 951

* Column headings with abbreviations are Metabolism (Met); Evaporation (Evap); Available absorbed energy from the gut (Qavail); Available water from food, (Wavail); Discretionary energy (Dnrg) 5 absorbed energy from the gut 2 metabolic energy expanded; Discretionary water (Dwtr) 5 water intake from food 2 water lost from cutaneous, respiratory and fecal material; g wet food consumed per year for the specified food type (Gwet/y); and total potential hours of activity per year (Hr/y).

habitats with canopy vegetation, sunspot basking greatly increases the diurnal activity time of rattlesnakes, but not ground squirrels, their diurnal prey. By contrast, our models predict that regardless of whether sunspots are present for basking during the day, canopy vegetation should preclude nocturnal activity by rattlesnakes (Fig. 15), most likely as a result of reduced daytime soil temperatures and lower radiant energy from the soil during the night. Potential overlap in annual activity time between rattlesnakes and nocturnal prey, such as wood rats, is minimized or eliminated altogether by canopy vegetation. Thus, the presence of vegetation can interact with the thermal environment to determine which predator-prey interactions occur across the landscape. Climate and vegetation determine connectance within this small subweb of organisms present at Sedgewick. DISCUSSION Understanding the factors that determine the abundance and distribution of species has been one of the primary foci of modern ecology. Temperature and climate are inextricably tied to many of the processes known to cause variation in abundance and species diversity at a variety of temporal and spatial scales. However, until recently there have been very few models capable of coupling complex climate data with a mechanistic understanding of animal physiology to make predictions about how these fundamental ecological variables may vary in space or time. In this paper, we show how a model based on first principles of heat and mass transfer (borrowed from the studies of physics and engineering) and animal physiology (Porter et al., 2000) can provide quantitative estimates about the effects of climate on a range of ecological processes. Specifically we have shown how regional climate can interact with local vegetative cover to de-

termine the overwintering survival of elk, the geographic distribution of chuckwallas and spatial variation in predator prey interactions between rattlesnakes and two small mammals. In some cases, our models simply quantify what we know well about the physiology of an organism (e.g., ectotherms require more food in warmer microclimates to meet metabolic demands). These examples serve as much-needed validation of our first-principles approach. In many other cases our approach reveals novel hypotheses for observed patterns. For example, the major benefit of canopy cover for elk in Yellowstone is not cover from high temperatures during summer days, but rather from low sky temperatures during winter nights. Finally, our analysis reveals how climate effects may propagate from the individual to the level of whole food webs. Our models predict that canopy cover is capable of both reducing per capita prey consumption by rattlesnakes and causing shifts in the relative consumption of various alternate prey species. Below we briefly discuss how these insights may be coupled with field studies to better understand the mechanisms of climate related shifts in animal performance, behavior, abundance and distribution. Behavioral shifts by elk in response to canopy removal Elk wintering in burned areas of Yellowstone National Park would be expected to take advantage of local topographic features, like south facing slopes and other landscape features that improve their infrared and solar radiation balance both during daylight hours and at night. More importantly, these animals may also take advantage of heat saving postures that could potentially lower radiant heat losses to cold night skies. For example, elk conserve more energy lying down than standing up at night. Prior calculations illustrated

TABLE 2. Calculated annual summaries of 280 g California Beechey ground squirrel energetics and activity potential at Sedgewick Ranch, CA, 350 m elevation.

Slope

% Shade

Sun spots

Metab. (J/y) 3 105

Evap (g/y)

Qavail (J/y) 3 105

Wavail (g/y)

Dnrg (J/y) 3 105

Dwtr (g/y)

Gwet/y

Hr/y

30, N 30, S 30, N&S

0 0 100

N/A N/A No

1,289 1,165 1,367

581 757 590

2,713 2,471 2,967

18,120 16,390 1,980

1,425 1,307 1,600

17,540 15,630 19,200

81,840 76,740 89,850

4,379 4,255 4,379


449

in Figures 11 and 12 (Porter et al., 2000), suggest that elk would benefit from up to a ten-fold increase in energy savings by lying in the snow during the night, assuming equal energy intake. However, lying down precludes feeding and may lead to higher depletion of fat reserves. Moreover, elk are most likely less vigilant and more susceptible to attack by wolves when lying down. Paradoxically, movement to higher elevation in winter may be more advantageous to elk than staying at lower elevation in the presence of predators that force relatively frequent movement (standing/moving in snow vs. more time lying down). These hypotheses remain to be tested in the field. Multifactor control on the geographic distribution of animals Distribution limits of animals may be constrained by many different factors. In the case of ectotherms ground temperatures are an important factor for successful incubation (Overall, 1996; Porter and Tracy, 1983; Muth, 1977; Lesham and Ackerman, 1991). Here we have demonstrated that the northern limit of the geographic range of chuckwallas is limited by the effect of local and regional climate on soil temperatures. However, the southern, western and eastern limits to the distribution of this lizard cannot be explained by climate. Chuckwallas are only found on rocky substrates. They are also typically found in association with creosote bush, Larrea tridentata, in desert environments, though the presence of creosote bush is not a requirement for their distribution (C. R. Tracy, unpublished data). Our comparison of vegetation and soil types at a fairly large grain size suggests that the eastern limits to the geographic range of this lizard may be related to the availability of creosote whereas the southern and western limits may be more strongly related to the availability of rocky crevices available only in rocky habitats and lava flows. These hypotheses however, await confirmation with more detailed field studies. FIG. 15. Vegetation density changes alter calculated activity times of a simulated rattlesnake and ground squirrel. Shade from overhead vegetation allows either only small sun spots for basking or no sun on the ground at all. The rattlesnake would have no nocturnal activity in either case. A subtle change in vegetation to a completely closed canopy from one nearly closed reduces activity time by half and food requirements by one fourth. The Beechey Ground squirrel could be active whenever the sun was up, in contrast to lack of midday activity in midsummer on exposed slopes (Fig. 13).

Spatial variation in predator-prey dynamics and food web structure Biophysical models predict that slope-aspect should have strong effects on the diet composition and relative effects of rattlesnakes on alternate prey bases. Predicted overlap in annual activity time decreases for rattlesnakes and diurnal prey and increases for rattlesnakes and nocturnal prey along north-south facing slope gradients as a result of a shift towards increasing nocturnal behavior by rattlesnakes in warmer south

TABLE 3. Calculated annual summaries of 250 g Dusky footed woodrat energetics and activity potential at Sedgewick Ranch, CA, 350 m elevation.

Slope

% Shade

Sun spots

Metab. (J/y) 3 105

Evap (g/y)

Qavail (J/y) 3 105

Wavail (g/y)

Dnrg (J/y) 3 105

Dwtr (g/y)

Gwet/y

Hr/y

30, N 30, S 30, N&S

0 0 100

N/A N/A No

1,606 1,502 1,565

920 993 894

3,023 2,978 3,048

20,470 20,170 20,700

1,418 1,476 1,483

19,550 19,180 19,810

86,200 84,770 85,840

5,475 4,381 5,475

450

W. P. PORTER

ET AL.

late into significant differences in the attack rate of rattlesnakes on small mammal prey over small spatial scales corresponding to differences in topography. Despite the strong effects of slope-aspect on calculated activity times, metabolic demand and diet composition of predators, local climates are determined not only by topography, but also by the composition and density of local vegetation. Closed canopy vegetation dramatically diminishes the overlap in annual activity time between rattlesnakes and nocturnal mammals and reduces the metabolic demand of rattlesnakes by a factor of four regardless of topography. These effects are reversed if small amounts of sun are available for sunspot basking as a result of a patchier canopy. These results suggest that topography and vegetation together, may provide a framework for understanding spatial and temporal variation in both the number of trophic links as well as the relative importance of these links within larger food webs. The generality of these conclusions could be tested in the future by examining the relative activity times and metabolic demands of our focal predator-prey trio across real landscapes at a larger spatial scale. For example, differences in snake body temperature and prey requirements between north- and south-facing slopes and in canopy versus no canopy environments would be more pronounced with increasing elevation. At higher elevation, lower prevailing air temperatures would increase the reliance of these animals on radiant heat sources leading to dramatically lower activity times and energetic demand in north-facing habitats. Similar increases in the relative importance of radiant heat should occur with increasing latitude. These observations suggest that spatial variability in food web dynamics at the scale of tens of meters to several kilometers, should increase both with elevation and latitude. In the future, this hypothesis could be tested using controlled experiments (e.g., predator exclusion) performed along elevational or latitudinal gradients in climate. FIG. 16. Vegetation density changes alter calculated overlap of activity times between the western rattlesnake and the California Beechey squirrel on heavily vegetated slopes with canopies that are nearly closed vs. canopies with complete closure. These changes would also completely free woodrats from rattlesnake predation, since rattlesnakes would no longer be active at night.

CONCLUSIONS Interactions between topography, vegetation and climate have both direct and indirect effects on animal food and water requirements, growth and reproduction potential, activity patterns, population dynamics, and the structure of food webs. The structure and diversity of vegetation also affect animals at many hierarchical levels. Vegetative diversity and associated variation in food digestibility appear to affect optimal body size and modify the degree of clumping of species’ body sizes on a landscape (Porter et al., 2000). The loss of

facing habitats. Assuming that the thermal environment does not alter activity rates (e.g., sprint speeds) of predators and prey in a countervailing manner, our calculated changes in activity overlap of between 2,503 to 2,381 hr (N vs. S exposed slopes) could transTABLE 4. Predator/Prey

R/Squirrel R/Squirrel R/Squirrel R/Woodrat R/Woodrat

Monthly hours of predator-prey overlap on 308 slopes.

Month

% Shade

1

2

3

4

5

6

7

8

9

10

11

12

To

North South North North South

0 0 100 0 0

186 167 0 0 0

196 224 0 0 0

279 310 0 0 0

300 300 0 0 0

341 341 62 0 31

120 120 120 30 90

124 124 248 217 217

124 93 248 217 217

300 90 180 60 270

248 186 93 0 62

180 240 0 0 0

155 186 0 0 0

25 23 10 52 88

PLANT-ANIMAL LANDSCAPES vegetation alters the diversity and quantity of food supplies, but it also alters local microclimates. In particular, the loss of canopy cover leads to greater temperature extremes and higher radiation loads. These changes in microclimate alter activity times, metabolic rates, water loss rates, and potential for growth and reproduction of both ectothermic and endothermic animals. The loss of vegetative cover in cold climates can be particularly challenging for large animals that depend on intact evergreen vegetation for radiant protection during the cold winter nights that they must endure. These challenges in turn, may reduce the allowable number of species (functional types) that can coexist in a given habitat (Porter et al., 2000). Interactions between predator and prey species can also be affected in substantial ways by the topography and the presence of canopy. Slope, aspect and elevation combined with the degree of canopy shading interact to determine activity time overlap of predators and prey and alter the metabolic requirements and water loss associated with maintenance, growth, reproduction and survivorship. Vegetation thus interacts with the morphology, physiology, and behavior of animals at local scales, which have ramifications for how they utilize microscale, mesoscale, and macroscale features of the landscape. ACKNOWLEDGMENTS We thank NCEAS and the NCEAS Bodysize workshop for support to the senior author. We thank Math Heinzel for early assistance in developing the GIS images in Arcview. We thank Dr. Steve Beaupre for review comments. We thank Dr. Frank Davis for information and access to resources about Sedgewick reserve. We thank Felisa Smith for natural history information on Dusky Footed woodrats. We thank Dr. Curt Walker for samples of fresh elk pelt for making reflectance measurements. We thank NSF grant IBN0097876 for symposium presentation support and NSF grant DBI-0137836 support to the senior author. REFERENCES Adolph, S. C. and W. P. Porter. 1996. Growth, seasonality, and lizard life histories: Age and size at maturity. Oikos 77:267–278. Beaupre, S. J. 1993. An ecological study of oxygen consumption in the mottled rattlesnake, Crotalus lepidus lepidus, and the blacktailed rattlesnake, Crotalus molossus molossus, from two populations. Physiol. Zool. 66:437–454. Budaraju, S., W. E. Stewart, and W. P. Porter. 1994. Prediction of forced ventilation in animal fur from a measured pressure distribution. Proc. Roy. Soc. London B 245:41–46. Budaraju, S., W. E. Stewart, and W. P. Porter. 1997. Mixed convective heat and moisture transfer from a horizontal furry cylinder in transverse flow. Int. J. Heat & Mass Transfer 40:2273–2281. Gates, D. M. 1980. Biophysical ecology. Springer-Verlag, New York. Gessaman, J. A. and K. A. Nagy. 1988. Energy metabolism: Errors in gas-exchange conversion factors. Physiol. Zool. 61:507–513. Graves, B. M. and D. Duvall. 1993. Reproduction, rookery use, and thermoregulation in free-ranging, pregnant Crotalus v. viridis. J. Herpetol. 27:33–41. Ives, A. R., K. Gross, and J. L. Klug. 1999. Stability and variability in competitive communities. Science 286:542–544. Johnson, S. R. 1965. An ecological study of the chuckwalla, Sau-

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romalus obesus Baird in the western Mojave Desert. Am. Midl. Natur. 73:1–29. Kingsolver, J. G. 1979. Thermal and hydric aspects of environmental heterogeneity in the pitcher plant mosquito. Ecol. Monog. 49: 357–376. Kitchell, J. F., J. F. Koonce, R. V. O’Neill, H. H. Shugart, J. J. Magnuson, and R. S. Booth. 1974. Model of Fish Biomass Dynamics. Trans. Am. Fish. Soc. 1974:786–798. Leemans, R. and W. P. Cramer. 1991. The IIASA database for mean monthly values of temperature, precipitation, and cloudiness on a global terrestrial grid. Laxenburg, Austria, International Institute for Applied Systems Analysis. Lesham, A. and R. A. Ackerman. 1991. Growth, water, and energy metabolism of the soft-shelled turtle (Trionyx triunguis) embryo: Effects of temperature. Physiol. Zool. 64:568–594. McClure, P. A. and W. P. Porter. 1983. Development of insulation in neonatal cotton rats (Sigmodon hispidus). Physiol. Zool. 56:18–32. McCullough, E. M. and W. P. Porter. 1971. Computing clear day solar spectra for the terrestrial ecological environment. Ecology 52:1000, 1008–1015. Mitchell, J. W., W. A. Beckman, R. T. Bailey, and W. P. Porter. 1975. Microclimatic modeling of the desert. In D. A. d. a. N. H. Afgan (ed.), Heat and mass transfer in the biosphere, Part I, Transfer processes in the plant environment, pp. 275–286. Scripta Book Co., Washington D.C. Muth, A. F. 1977. Eggs and hatchlings of captive Dipsosaurus dorsalis. Copeia 1977:189–190. Overall, K. L. 1996. Integrating behavioral, demographic, and biophysical environments: Potential roles for mate choice and nest environments on eggs and hatchlings of the lizard Sceloporus merriami. Univ. of Wisconsin, Madison. Paine, R. T. 1980. Food webs: Linkage, interaction strength and community infrastructure. J. An. Ecol. 49:667–685. Pimm, S. L. 1982. Food webs. Chapman & Hall, New York. Polis, G. A. and K. O. Winemiller. 1995. Food webs: Integration of patterns and dynamics. Chapman and Hall, New York. Porter, W. P. 1967. Solar radiation through the living body walls of vertebrates with emphasis on desert reptiles. Ecol. Monog. 37: 273–296. Porter, W. P. 1989. New animal models and experiments for calculating growth potential at different elevations. Physiol. Zool. 62: 286–313. Porter, W. P., S. Budaraju, W. E. Stewart, and N. Ramankutty. 2000. Calculating climate effects on birds and mammals: Impacts on biodiversity, conservation, population parameters, and global community structure. Amer. Zool. 40:597–630. Porter, W. P. and D. M. Gates. 1969. Thermodynamic equilibria of animals with environment. Ecol. Monog. 39:227–244. Porter, W. P., J. W. Mitchell, W. A. Beckman, and C. B. DeWitt. 1973. Behavioral implications of mechanistic ecology: Thermal and behavioral modeling of desert ectotherms and their microenvironment. Oecologia 13:1–54. Porter, W. P., J. C. Munger, W. E. Stewart, S. Budaraju, and J. Jaeger. 1994. Endotherm energetics: From a scalable individual-based model to ecological applications. Austral. J. Zool. 42:125–162. Porter, W. P. and C. R. Tracy. 1983. Biophysical analyses of energetics, time-space utilization, and distributional limits. In R. B. Huey, E. Pianka, and T. Schoener (eds.), Lizard ecology: Studies of a model organism, pp. 55–83. Harvard Univ. Press, Cambridge. Ramankutty, N. and J. A. Foley. 1999. Estimating historical changes in global land cover: Croplands from 1700 to 1992. Global Biogeochem. Cycles 13:997–1027. Schoener, T. W. 1971. Theory of feeding strategies. Ann. Rev. Ecol. Systematics 2:369–404. Steudel, K., W. P. Porter, and D. Sher. 1994. The biophysics of Bergmann’s rule: A comparison of the effects of pelage and body size variation on metabolic rate. Can. J. Zool. 72:70–77. Stewart, W. S., S. Budaraju, W. P. Porter, and J. Jaeger. 1993. Prediction of forced ventilation in animal fur under ideal pressure distribution. Funct. Ecol. 7:487–492. Swinbank, W. N. C. 1963. Long-wave radiation from clear skies. Q. J. World Meteor. Soc. 89:339. Task, I. G. S. D. 1998. A program for creating global soil-property databases, version IGBP-DIS, SoilData (V. 0), Paris, France.

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APPENDIX TABLE A1. Microclimate input data for Sedgewick Ranch, Los Olivos quadrangle. Sample data for a south facing slope at 350 m elevation are formatted as one or more rows of description of the data, followed by a separation row of dashes, followed by the appropriate data under each label. Site label ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Sedgewick Ranch, California 350 m elevation South slope Roughness height (m)

Soil therm cond (W/mC)

Sub. refl (dec%)

Sub. dens (kg/m3)

Sub. specific heat (J/m3-K)

Sub. long IR emiss (dec%)

Integrator error

Animal ave. height (cm)

0.0001

0.35

0.26

2650.

837.

0.90

2.

1.

Start month #

End month #

Percent of bare ground in shade (0% 5 full sun)

1

12

100.0

Hemisphere

Latitude

W Longitude

Longit. time

Slope Azimuth

Elev. Cm H2O

N 5 1., S 5 2.

(deg)

(min)

(deg)

(min)

zone (deg)

(deg)

S 5 0 (deg)

(m)

in air col.

1.

34.

41.5

120.

2.5

120.

30.0

0.0

350.0

0.2

Time of MAXIMUMS (top row), Minimums (bottom row): Air, Wind max’s ; solar noon (integer hours relative to sunrise or solar noon) Rel. Hum, Clouds max’s ; sunrise Air temp.

Wind speed

Rel. hum.

Cloud cover

1.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 Max’s (top row), min’s (bottom row) REL HUMIDITIES (%) for ave. day each month ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

40. 40. 40. 40. 40. 40. 40. 40. 40. 40. 40. 40. 20. 20. 20. 20. 20. 20. 20. 20. 20. 20. 20. 20. Max’s (top row), min’s (bottom row) CLOUD COVER (%) for ave. day each month ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

25. 25. 25. 25. 25. 25. 25. 25. 25. 25. 25. 25. 25. 25. 25. 25. 25. 25. 25. 25. 25. 25. 25. 25. Max’s (top row), min’s (bottom row) WIND SPEED (m/s) for ave. day each month ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

7.3 9.4 8.5 9.5 9.5 8.7 7.7 7.7 7.9 8.6 7.5 7.9 4.2 4.2 4.2 6.9 4.2 6.9 6.9 6.9 4.2 6.9 5.6 4.2 Max’s (top row), min’s (bottom row) AIR TEMPERATURE (C) for ave. day each month ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

17.6 5.2

17.3 5.0

19.0 5.0

20.1 5.1

23.6 7.4

25.4 8.6

30.7 11.5

32.5 12.7

29.4 11.8

26.0 8.6

22.0 7.1

18.0 4.4


453

Animal input data used in the simulations. TABLE A2.

Mass (kg) Hair diameter (dorsal/ventral) (mm) Hair length (dorsal/ventral) (mm) Fur depth (dorsal/ventral) (mm) Hair density (dorsal/ventral) (#/cm2) Fur/skin reflectivity (%) Fur transmissivity (%) % ventral area in contact with substrate

Morphological properties.

Elk

Beechey squirrel

Dusky footed Woodrat

Western rattlesnake

200.0 15/15 53/53 29/29 4,742/4,742 20 14 0.5

0.280 24/17 8.5/8.5 7/7 10,000/10,000 20 14 0.5

0.250 24.5/24.5 13.5/13.5 10.7/10.7 19,900/19,900 20 14 0.5

0.848 N/A N/A N/A N/A 24.9 N/A 60

0.4 0.4

0.4 0.4

0.5 0.2

Long wavelength infrared configuration factors (F) Fanimal-sky Fanimal-ground

0.4 0.4

TABLE A3.

Physiological properties.

Elk

Core temperature (C) Body density (kg/m3) Body specific heat (J/kg-K) Body thermal conductivity (W/m-C) Digestive efficiency (%) Fecal water (%) Urea in urine or uric acid (%) Basal metabolism multiplier for activity/growth/reproduction

TABLE A4.

Species type of food

% % % %

protein fat carbohydrate dry matter

TABLE A5.

Food properties.

Elk (Grass hay)

Beechey squirrel (Grass hay)

8.6 2.3 42 50

8.6 2.3 42 50

Western Dusky footed rattlesnake (Squirrel/ woodrat Woodrat) (Grass hay)

8.6 2.3 42 50

80 8 12 25

Behavioral properties/options specified by user.

Species

Diurnal? Burrow? Climb to cool? Ground shade seeking OK? Crepuscular? Night shade? (Cold protection) Fossorial exclusively/primarily? Dig burrows? Arboreal exclusively/primarily?

Elk

Y N N Y N Y N N N

Dusky Beechey footed squirrel woodrat

Y Y N Y N Y N Y N

N N Y Y Y Y N N N

Western rattlesnake

Y Y N Y Y N N N N

Beechey squirrel

Dusky footed woodrat

37.5

37.5

37.5

1,000 4,185 0.9 61 10 20 3.5

1,000 4,185 0.9 61 10 20 2.1

1,000 4,185 0.9 61 10 20 2.1

Western rattlesnake

21.0/26.5/33.0 Tmin/Tpref/Tmax 1,000 4,185 0.9 90 25 20 1.5