Challenges and opportunities in integrating ecological knowledge across scales

Forest Ecology and Management 181 (2003) 223–238

Challenges and opportunities in integrating ecological knowledge across scales N. Thompson Hobbs* Natural Resource Ecology Laboratory, Colorado Division of Wildlife, Colorado State University, Fort Collins, CO 80523-1499, USA Received 1 November 2001; received in revised form 10 June 2002; accepted 10 June 2002

Abstract Choices of the spatial and temporal dimensions of ecological investigations define their scale. In this paper, I identify some of the ways that issues of scale challenge ecologists in developing an understanding of natural and human-dominated systems, with particular reference to understanding interactions between ungulates and landscapes. I also point out opportunities to rise to those challenges. Ecologists often study areas of land that represent only a tiny fraction of the area that is managed for natural resources or other human uses. This mismatch between scales of investigation and scales of management is problematic because observations of many phenomena depend on the scale at which those observations are made. Conducting traditional experiments at ever-larger scales would appear to offer a logical solution to this problem, but the ‘‘tyranny of power’’ means that such investigations are frequently infeasible. Moreover, because human perception is based on limited scales of experience, it is often difficult to apply understanding of ecological processes occurring over long time periods and large areas. The ability of ecologists to integrate knowledge across scales in a way that is useful to management has improved dramatically as a result of technological advances, innovations in statistical analysis and study design, and a shift in the philosophy of science favoring model selection over traditional hypothesis testing. Multi-scale understanding is fostered by adaptive management, which uses fine-scale, mechanistic understanding to screen hypotheses to be tested at large-scales. Issues of scale reveal that applying ecological understanding to complex environmental problems requires two kinds of science—developing an understanding of properties and processes and assembling that understanding reliably across scales of time and space. # 2003 Elsevier Science B.V. All rights reserved. Keywords: Scale; Resource management; Ungulate; Adaptive management; Heterogeneity; Model selection

1. Introduction One of the defining characteristics of ungulate herbivores is mobility—they can traverse large areas of space over relatively brief intervals of time and, consequently, are able to respond to heterogeneity in landscapes expressed across a broad range of scales. * Tel.: þ1-970-491-5738; fax: þ1-970-491-1965. E-mail address: [email protected] (N.T. Hobbs).

The implications of these multi-scale responses were first made clear in the seminal paper of Senft et al. (1987) who applied Robert O’Neill’s newly cast hierarchy theory (O’Neill et al., 1986) to the problem of large herbivore foraging. The Senft et al. paper developed the idea that foraging consisted of a series of decisions nested in a hierarchy, with each decision occurring at a different temporal frequency and at a different spatial scale. This paper has been exceedingly influential, eliciting more than 160 citations and

0378-1127/$ – see front matter # 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0378-1127(03)00135-X

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stimulating several other reviews elaborating on its central ideas (Coughenour, 1991; Laca and Ortega, 1995; Bailey et al., 1996). Since the publication of Senft et al., scale has become a fundamentally important topic in literature on ungulate foraging and landscape use. There are several important themes in this work. First, it is clear that herbivores make decisions at multiple scales, and in many cases, their choices at large-scales cannot be described simply as the aggregate outcome of choices made at small scales (Ward and Saltz, 1994; Schaefer and Messier, 1995b; Fragoso, 1999; Ginnett and Demment, 1999; Mysterud et al., 1999; Wallis de Vries et al., 1999; Skarpe et al., 2000; Apps et al., 2001; Johnson et al., 2001). However, small scale decisions (e.g. the choice of feeding on one plant as opposed to another) can create important large-scale heterogeneities in landscapes and can fundamentally alter highlevel processes like nutrient cycling and community succession (Pastor et al., 1993, 1997, 1988; McInnes et al., 1992; Pastor and Naiman, 1992; Pastor and Cohen, 1997; Frank and Groffman, 1998; Fuhlendorf and Smeins, 1999; Knapp et al., 1999; Augustine and Frank, 2001; Steinauer and Collins, 2001). Progress has been made in understanding patterns of animal movement in response to environmental cues (Gross et al., 1995; Etzenhouser et al., 1998; Viswanathan et al., 1999; Bergman et al., 2000b), and this understanding has supported development of system-level models portraying interactions of ungulates with landscapes and linking landscape characteristics to ungulate population dynamics (Roese et al., 1991; Turner et al., 1994b; Uziel and Berry, 1995; Moen et al., 1998; Carter and Finn, 1999; Cramer and Portier, 2001; Weisberg et al., 2002). All of this progress depends in one way or another on the central, unifying concept of scale. Yet, despite the importance of this concept, there remains substantial ambiguity in its application. A decade of work has revealed that seemingly routine decisions on scale of inquiry can exert profound impacts on the outcomes of ecological investigations (e.g. Milne et al., 1989; Duarte and Vaque, 1992; Reed et al., 1993; Knopf and Sampson, 1994; Martinez, 1994; Palmer and White, 1994; Schaefer and Messier, 1995a; Begg et al., 1997; Dobermann et al., 1997; Keitt et al., 1997; Stohlgren et al., 1997; Bradshaw, 1998; Cooper et al., 1998; Gardner, 1998; Peterson et al., 1998; Ritchie, 1998;

Lawes and Eeley, 2000; Adler et al., 2001; Godfray and Lawton, 2001; Loreau et al., 2001; Whittaker et al., 2001). The dependence of our observations on the scale at which they are made results from heterogeneity in nature—in a uniform world, scaling would be a trivial problem (Powell, 1989; Wiens, 1989). However, heterogeneity is ubiquitous in natural and managed systems (Kolasa and Pickett, 1991). Thus, the rise of interest in scale has coincided with a heightened awareness of the importance of heterogeneity as a fundamental, causal agent driving the operation of many ecological processes (Wiens, 1989; Pickett et al., 1992; Wu and Levin, 1994; Godfray and Lawton, 2001; Whittaker et al., 2001). Here, I offer a brief treatment of the problem of scale in investigations of ungulate foraging, in particular, and ecological studies in general. I have three purposes. The first is to clarify what scale means. More importantly, however, I seek to motivate the importance of multi-scale studies by discussing challenges brought on by heterogeneity and scale in research and management. I then turn to discuss emerging opportunities to rise to those challenges.

2. The meaning of scale Many terms in ecology undergo a remarkably similar evolution. A seminal paper introduces some new concepts and associated terminology. These ideas make their way into other papers, formal lectures, and casual conversations. A process reminiscent of the children’s game ‘‘party line’’ ensues, and the terms and ideas in the original work take on informal, often altered, meanings. This evolution has occurred with the term ‘‘scale’’. There are a couple of fundamental sources of confusion in the application of the term ‘‘scale’’ in ecology. The first has to do with the kinds of things scale describes. The second results from attaching scale to ecological concepts that describe a level of organization. To cope with the first source of confusion, Rykiel (1998, p. 457) points out that ‘‘scale, by itself, has no ecological meaning’’. This may be surprising given that there are many scholarly papers and thick books written about scale in ecology. But it remains true that any ecological meaning attached to scale is just that—an attachment. Scale is not a discipline like bio-geochemistry or

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population dynamics. Ecologists who write about scale do no more than describe the ways that ecological states and processes are observed. So, what is scale? Scale is a way to describe the physical dimensions of objects of interest in time or space (O’Neill and King, 1998, p. 7). The ideas of Rykiel and O’Neill say it all about scale—it has no meaning in its own right—its only meaning comes from measurement systems imposed by scientists. Thus, ‘‘scale pertains to size in both time and space; size is a matter of measurement, so scale does not exist independent of the scientists’ measuring scheme. Something is large-scale if perceiving it required observations over relatively long periods of time, or across large parcels of space, or both’’ (Allen and Hoekstra, 1992, p. 2). Scale defined as the ‘‘size’’ of measurements has two parts, big and small. The big part is the maximum extent included in a measurement scheme. The small part is the minimum difference that can be resolved. For example, the scale of a yardstick is defined by its length (3 ft) and its smallest subdivision (1/16 in.). The scale of a calendar is set by its duration (one year) and its resolution (1 day). More generally, describing the spatial scale of an inductive study requires specifying the total area from which sub-samples are drawn, the number of sub-samples, and their area. Describing the temporal scale requires specifying the duration of the study, and the frequency and duration of its measurements. These concepts also apply to models. For example, in a difference equation model of temporal dynamics, scale is defined by the time step (the smallest unit of change) and the maximum duration of model runs. In a cell-based simulation model of spatial dynamics, scale is defined by the cell size and the area modeled. The ‘‘big part’’ of scale has been variously termed ‘‘extent’’, ‘‘range’’, and ‘‘domain’’ while the small part is called ‘‘grain’’ (Wiens, 1989), ‘‘support’’ (Atkinson, 1997), and ‘‘resolution’’. To avoid jargon, I will use the term ‘‘resolution’’ to describe the small part of scale and ‘‘extent’’ to describe the large part. The ratio of extent to resolution defines the scope of a measurement (Schneider, 1997, 1998). Scope is fundamentally important to the process of synthesizing, aggregating, and comparing data. Because the measurements of ecology are quantities, not pure numbers, there are rules for making valid comparisons among

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measurements. A particularly important rule is that valid comparisons among studies require observations of similar scope (Schneider, 1998). For example, data on net primary production from a study with a maximum extent of 100 km2 and a sub-sample of 100 m2 plots (scope ¼ 100,000 m2/100 m2 ¼ 1000) cannot be reasonably combined with data from a study with an extent of 1 km2 and a sub-sample of fifty 0.25 m2 plots (scope ¼ 1000 m2/12.5 m2 ¼ 80). Confusion about scale arises because many ecologists use the term scale loosely to refer to the extent of a study (Atkinson, 1997). That is, we gloss over the small part (resolution) when talking about scale. This informal use is not crippling because resolution and extent are usually related as a result of the cost of obtaining field measurements (large study areas require large subplots) or the limitations of computing power (large cell-based maps require large grid cells). However, we should bear in mind that both components of scale (resolution and extent) can affect the outcome of our observations and determine whether those observations can be compared with others. An additional source of confusion comes from attaching the term scale to abstract concepts like ‘‘ecosystem’’ or ‘‘community’’ (O’Neill and King, 1998). There is no such thing as the ‘‘scale of the ecosystem’’ because there is no measurement system that applies to ecosystems or communities in the same way that measurements of area can describe surfaces or measurements of time can describe episodes or epochs. Although levels of organization in ecology (individual, population, community, ecosystem) can have characteristic temporal and spatial scales associated with them, these associations depend on the specific organisms involved, the questions posed by the investigator, and the measurement systems he or she imposes (O’Neill and King, 1998). So, I will use the term ‘‘level’’ to describe differences in ecological organization that result from differences in the kind and number of interactions under study. I will reserve use of the term ‘‘scale’’ to refer to the size of measurements required to study those interactions.

3. The challenge of scaling Ecologists can offer important solutions to pressing environmental problems. Many of these problems

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result from mismatches in scale (Lee, 1993). For example, depletion of ungulate populations can occur when short-term rates of harvest exceed long-term rates of renewal. Carbon dioxide accumulates in the atmosphere because the time-scale of burning fossil fuels is many times briefer than the time-scale required to accumulate them. Forest fragmentation results because the spatial scales of resource extraction do not match the scales of natural disturbance that shaped the evolution of the landscape. Point-source pollution is the outcome of a release of effluents affecting an area far larger than the area of accountability of the polluter. Excessive exploitation of open access resources like fisheries and grazing land (the tragedy of the commons) extends from behavior that is rational at the level of the individual, but is destructive to values held by the community—short-term benefits accruing to individuals lead to long-term degradation. Ecologists can contribute to solving these problems by making predictions about the consequences of human actions for natural systems. As ecology develops from a descriptive to a predictive science, issues of scaling become increasingly important—making reliable predictions creates challenges brought on by the need to integrate information across scales of time and space. In this section, I discuss some of those challenges.

and Field, 1993; Edwards et al., 1994; Van Gardingen et al., 1997; Wallis de Vries et al., 1998), there remains a disproportionate investment in research at relatively small scales. Focusing on small scales and simple systems is justified (sometimes correctly) by the need to obtain scientifically reliable knowledge. Reliability is enhanced by statistical precision, which of course is easiest to obtain on well-replicated, small plots under the strict control of the investigator. It follows that the choice of scale is often driven by considerations of scientific convenience rather than relevance (Squire and Gibson, 1997). Many environmental problems, however, are not easily dissected into tidy plots that can be studied for brief intervals. For example, despite the widely recognized importance of linking ungulate foraging behavior to population dynamics, no studies have examined differences in population processes that result from multi-scale foraging decisions. This failure results because these processes operate at a scale of time and space that is difficult to study. It follows that one of the greatest challenges confronting contemporary ecologists is rectifying the mismatch between the scales of our work with the scales of problems that matter (Grace et al., 1997; Squire and Gibson, 1997; Hobbs, 1998; Rykiel, 1998). 3.2. The scale of observation affects what we observe

3.1. Ecologists observe a small part of the world Most environmental problems that matter to society operate at relatively large-scales and involve complicated human organizations as well as complex ecological relationships (Van Gardingen et al., 1997; Hobbs, 1998; Rykiel, 1998). Most ecological research is done on relatively small areas where the complexities of natural and human systems can be controlled and simplified (Squire and Gibson, 1997; Van Gardingen et al., 1997). For example, many studies of ungulate foraging occur at spatial scales less than 1 km2 (Hobbs et al., 1981, 1983; Baker and Hobbs, 1982; Wickstrom et al., 1984; Clark and Harris, 1985; Gillingham and Bunnell, 1989a,b; Lundberg and Danell, 1990a,b; Danell et al., 1991; Vivas et al., 1991; Andersen and Saether, 1992; Shipley and Spalinger, 1992; Weckerly, 1994; Jia et al., 1995; Edwards et al., 1996; Gillingham et al., 1997a). Although there has been much interest in ‘‘scaling up’’ (Ehleringer

Mismatch of scale between the spatial extent of research and the spatial extent of management decisions is important because the outcome of ecological investigations often depends on the scale at which the observations are made, a phenomenon known as scale dependence. Whenever phenomena are scale dependent, inferences about large-scale behavior cannot be made reliably based on small-scale studies (and vice versa) unless the pattern of scale dependence is well established (Wiens, 1989; Atkinson, 1997; Grace et al., 1997). The best-known example of scale dependence probably comes from the species-area relation, where the number of species counted in a survey varies as a power function of the size of areas surveyed (Palmer and White, 1994). This dependency, of course, has contributed to the controversy about the design of conservation reserves (Simberloff, 1988). There are other examples of scale dependence. Habitat variables that influence foraging decisions

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Fig. 1. At coarse scales (A), the spatial distribution of predators and prey tends to be spatially correlated; that is, they tend to occur in the same locations. At fine-scales (B), the spatial distribution of predators and prey is not correlated; they tend to be found in spatially distinct locations. Whether their ‘‘locations’’ are similar or distinct depends on the scale of observation.

at the scale of the feeding station differ from those that affect patch level choices (Apps et al., 2001; Johnson et al., 2001). Densities of predators and prey are positively correlated at large-scales because predators seek prey that are spatially concentrated, and as a result, both species are likely to be found within the same habitat types (Stern, 1998) (Fig. 1A). In contrast, the spatial distributions of predators and prey are negatively correlated at fine-scales because individual prey seek to avoid predators (Fig. 1B). For example, white-tailed deer concentrate in buffers between territories of wolf packs in the boreal forest, thereby creating inverse relationships between wolf and deer densities at small scales (Rogers et al., 1980; Taylor and Pekins, 1991; Lewis and Murray, 1993; White et al., 1996). However, wolves and deer are broadly sympatric in this region. This sympatry at large-scales and fine-tuning of habitat use at small scales creates strong scale dependence in relationships between deer and wolf density. Another prominent example of scale dependence in ungulates is the regulation of food intake, which in turn, has fundamental implications for habitat carrying capacity, forage allocation, and other management issues. When we observe an animal foraging for brief time intervals (e.g. <1 h), the rate of forage intake by ungulates is set by the arrangement of plant leaves in space (Spalinger and Hobbs, 1992; Gross et al., 1993). The spatial arrangement of plant leaves affects bite

mass and the spatial arrangement of plants determine an ungulate’s encounter rate with plants. At these fine time-scales, one or the other of these characteristics (bite mass or encounter rate) regulates intake rate (Fig. 2). However, as time-scales expand (e.g. >1 h to <1 day), intake rate is regulated by the ability of the animal to digest and excrete its food, which is determined by cellular properties of the plant, particularly the structure and composition of plant cell walls (Fig. 2).

Fig. 2. Mechanisms controlling rate of food intake by ungulate herbivores are scale dependent. Over brief time-scales, intake is regulated by geometric properties of plants that control cropping rate or encounter rate. At intermediate scales, rate of intake is controlled by cellular properties of the plant that affect rate of digestion. At long time-scales, endocrine feedback limits food intake.

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At yet coarser scales (i.e. the lifetime of the animal), intake rate is determined by genetic characteristics mediated by the effect of the endocrine system on satiety (Brockman and Laarveld, 1986) (Fig. 2). Thus, the mechanism that regulates voluntary intake in ungulates depends in a fundamental way on the time-scale at which we observe the animal (Wallis de Vries et al., 1998) and the habitat where it forages (Bradbury et al., 1996). Probably the best example of scale dependence is seen in the interpretation of equilibrium in population and community studies (Wu and Levin, 1994; Bravo De La Parra et al., 1997; Nisbet et al., 1997; Tyler and Hargrove, 1997; De Mazancourt et al., 1998; He Hong and Mladenoff, 1999). The concept of equilibrium lies at the heart of classical theory in ecology, and much of the debate (Illius and O’Connor, 1999, 2000) about whether the concept is a theoretical curiosity or a real world phenomenon probably results from scale. Consider, for example, the population trajectories shown in Fig. 3. In the upper panel, there is a strong suggestion of density dependence regulating the population close to a steady state. In the lower panel, the population appears to drift without a strong tendency to

return to equilibrium. The importance of scale is revealed by the fact that the data in the ‘‘non-equilibrium’’ case are, in fact, drawn from the later years of the ‘‘equilibrium one’’ The time-scale over which we view population trajectories exerts a strong effect on how we interpret them. This has fundamentally important implications for managers. Over the short timescale, the effect of density dependence may be small enough to safely ignore those effects in decisions on harvest rates. In contrast, longer time frames may necessitate careful consideration of density feedbacks. Similar problems extend from differences in the spatial scale of observation of population dynamics (Illius and O’Connor, 1999). The consequence of scale dependence is simple. Investigators of natural systems must explicitly consider how their results can be extended across scales (Wallis de Vries, 1996; Atkinson, 1997; Schneider, 1997, 1998; Wallis de Vries et al., 1998). The ability to make this extension should be a fundamental component of experimental and sampling designs, a component that is weighted equally with more traditional considerations, such as sample size, statistical power, and freedom from bias (Dutilleul, 1993, 1998). 3.3. Heterogeneity rules

Fig. 3. Two population trajectories illustrating effects of temporal scale on interpretation of equilibrium. Panel (A) would likely be interpreted as an example of equilibrial behavior in population dynamics. Panel (B) appears to show little tendency to return to an equilibrium point. However, the dates in (B) are simply the later years of the data in (A).

Scale dependence results from spatial and temporal heterogeneity inherent in natural systems (Wu and Loucks, 1995) (Fig. 4). Although virtually nothing in nature is randomly or uniformly distributed at all scales, much ecological theory rests on assumptions of randomness in spatial distributions of organisms. Historically, heterogeneity was seen as a nuisance to be assumed away in theoretical studies and designed away in empirical work because it complicates modeling and field research (Tilman and Kareiva, 1997b). A more contemporary view sees the patchiness of nature as a fundamental, causal agent driving the dynamics of ecological systems (Levin, 1992; Wu and Loucks, 1995; Tilman and Kareiva, 1997a; Illius and O’Connor, 2000). For example, ungulate responses to patchiness in resources expressed at several scales has emerged as an active area of inquiry (Klein and Bay, 1991; Seagle and McNaughton, 1992; Jiang and Hudson, 1993; Kidunda et al., 1993; Laca et al., 1993, 1994; Langvatn and Hanley, 1993; Kohlmann and Risenhoover, 1994, 1997; Kotler et al.,

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Fig. 4. An example of the importance of heterogeneity in creating scale effects. The density of objects in space remains constant with scale when objects are randomly or uniformly distributed in space. In this case, density scales isometrically. By contrast, objects that are clustered show allometric scaling—the density changes as a power function of scale.

1994; Wallis de Vries and Schippers, 1994; Wilmshurst and Fryxell, 1995; Wallis de Vries, 1996; Gillingham et al., 1997b; Bergman et al., 2000a; Wilmshurst et al., 2000; Johnson et al., 2001). More broadly, ecologists are increasingly seeking to understand the role of environmental heterogeneity in shaping the behavior of populations, communities, and ecosystems. If we wish to understand heterogeneity, whether it is expressed over time or space, then we must pay attention to scale (Powell, 1989). 3.4. Traditional experiments writ large are not the answer One of the potential solutions to the mismatch between the scales at which ecologists work and the scales that matter to policy and management is to simply apply traditional experimental techniques and statistical analysis at larger spatial extents (Squire and Gibson, 1997). Since the highly influential papers of Romesburg (1981) and Hurlbert (1984), ecologists have been slavishly faithful to tidy experimental designs and ‘‘rigorous’’ analysis using traditional statistical techniques. It is important to remember that this way of gaining knowledge was largely derived from agronomic systems where small sub-samples (e.g. 10 m  10 m plots) could be isometrically related to a larger, homogeneous area (e.g. 1000 m  1000 m fields). We should not forget that these inductive

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inferences were made feasible by many ‘‘homogenizing’’ agricultural practices (tilling, fertilization, herbicide treatment, insect and disease control), the fundamental purpose of which was to attain uniform yields over time and space. We should not overlook the fact that the starting point for traditional statistical inference is the random choice of experimental or sample units from a large population. There are limits to the agronomic approach to science at large-scales in natural systems that result in part from what I will call the ‘‘tyranny of power’’. As study areas increase in size, the statistical power of traditional experiments declines, and hence, the ability to understand and predict system behavior also diminishes. There are at least three reasons for this. First, it is a simple truth that small things are more amenable to study by traditional scientific methods than large things (Grace et al., 1997). However, although small things are easier to study, they are also more numerous, and understanding many interacting elements of a system is far more difficult than understanding a few (Grace et al., 1997). For example, the physiology of individual leaves is relatively easily studied and understood, but it remains a daunting task to reliably extend this understanding to whole canopies, to regions, or to the globe (Grime et al., 1997; Kruit et al., 1997). The energy balance of individual ungulates can be estimated given sufficient time and effort (Wickstrom et al., 1984; Fancy and White, 1985; Murray, 1991; Adamczewski et al., 1993; Parker et al., 1996), but estimating the carrying capacity of a habitat based on the supply of energy and the energetic demands of the population has rarely been accomplished (Hobbs, 1988; Illius and Gordon, 1999; Weisberg et al., 2002). Second, variability increases with scale. Large-scale studies inevitably confront many sources of spatial and temporal heterogeneity. Coping with increasing variability is made difficult by the often-exorbitant cost of replication at large-scales (Hargrove and Pickering, 1992; Walters and Green, 1997). As a result, we are often left with only a few replications chosen from a heterogeneous population. The scarcity of replications allows us to detect only exceedingly large effects of treatment if we apply traditional standards of inference. Finally, the potential for bias increases with scale. ‘‘Replications’’ in large-scale studies are usually chosen in a decidedly non-random way, at least with

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respect to the spatial ‘‘population’’ we wish to learn about (Walters, 1997). The absence of true randomization means we cannot be assured of freedom from bias. No matter how rigorous our statistical inference to large-scales, we cannot be sure that those inferences are biologically reliable for scale dependent phenomena. The observation that ‘‘all landscapes are different’’ raises fundamental questions about portability of results from one large study area to another (Hargrove and Pickering, 1992). The implication of the tyranny of power is that many of the traditionally sanctioned techniques for ecological investigation are simply not appropriate at large-scales (Hargrove and Pickering, 1992). This means that inferences at large-scales are likely to require research designs that bear little resemblance to the approaches many of us learned in graduate school. For example, valuation of the consequences of errors in policy and management that are likely to occur in the absence of knowledge gained by management experiments should be considered alongside traditional forecasts of statistical power in designing large-scale studies (Walters and Green, 1997). 3.5. Scales of human perception constrain our ability to act People have a scale of perception that is set by the duration of our experience (Ornstein and Ehrlich, 1989). We remember not much more than half a century of experience (at best), and those memories are relatively imprecise. People’s experience extends over relatively small scales of space. Given the spatial and temporal limits on what people experience and remember, it should come as no surprise that they have difficulty coping with problems that extend well beyond those limits (Ornstein and Ehrlich, 1989). An excellent example of this difficulty is the effect of fire on landscapes. The short-term effects of fire are well understood and appreciated by land managers. These effects occur over scales of time and space making them easy to perceive, and this perception provides a firm basis for action. Alternatively, the effects of the absence of fire are difficult to perceive because they accumulate slowly over large areas (e.g. Veblen and Lorenz, 1991). In contrast to the many elaborate prescriptions for fighting fires, there is almost a total absence of approaches for dealing with

large-scale effects of fire suppression (e.g. maturation of plant communities, canopy closure, etc.).

4. Opportunities to extend knowledge across scales In an ideal world, all reports of ecological investigations would contain a section explicitly describing how the results could be used to support understanding and predictions at scales other than those investigated. This section would come as naturally as ‘‘Methods’’ or ‘‘Discussion’’. Much progress has been made to equip ecologists with the tools needed to make reliable inferences across scales. Here, I review some of that progress. 4.1. Technology facilitates large-scale observation, analysis, and modeling One of the fundamental limitations of ecological investigations at large spatial scales has been the expense and logistical difficulties of obtaining measurement over large areas. Moreover, until recently, obtaining precise estimates of measurement locations required cumbersome surveying methods. The ability to obtain and analyze spatially referenced data has risen dramatically with the development of remote sensing techniques, geographic information systems, and in-situ global positioning (Schimel, 1995; Alves and Skole, 1996; Cohen et al., 1996; Goetz and Prince, 1996; Roy and Ravan, 1996; Tiwari et al., 1996; Bondesson et al., 1998; Burns and Castellini, 1998; Kaye and Croft, 1998; Oesterheld et al., 1998; Payne and Harty, 1998; Sommer et al., 1998). These advances have been accelerated by the geometric increase in cheap computing power. Ecologists have the wherewithal to observe many phenomena over large extents with increasingly fine resolution. This ability has given rise to spatially explicit models that would have been infeasible to parameterize or implement in the recent past (Coughenour, 1992; Turner et al., 1993, 1994a; Moen et al., 1997; Falloon et al., 1998; Letcher et al., 1998; Moen et al., 1998; Weisberg et al., 2002). However, bigger is not necessarily better; faster computing and higher resolution data tempt ecologists to build models that are overparameterized, suffer from propagated error, and may

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omit key processes while focusing on detailed treatment of less important ones. The challenge is to match data appropriately with understanding. 4.2. The mathematics of scaling are becoming widely appreciated Two bodies of work have called attention to scaling in calculations and analysis. The seminal work of Schneider (1997) informed ecologists about the fundamental difference between pure numbers and quantities, and the implications of this difference for routine calculations in ecological investigations. Pure numbers do not require attention to dimensions and their units, while quantities are not defined without reference to dimensions of time, length, or mass. Whenever patterns in nature are patchy (as opposed to uniform or random), measures of properties related to these patterns scale allometrically as a power function. Consequently, making inferences across scales requires reference to these functions. Moreover, fractal geometry has illustrated the application of scaled calculations to a variety of ecological problems (Loehle, 1990; Milne, 1991a,b; Meltzer and Hasting, 1992; Vedyushkin, 1994; Wiens et al., 1995; Ritchie, 1998). This body of work is accessible to ecologists with relatively rudimentary mathematical training. It provides a basis for making reliable inferences at multiple scales. 4.3. New statistical techniques for multi-scale design and analysis enhance experiments and monitoring Despite the advances in technology outlined above, ecologists are likely to be able to measure only a small portion of the areas of land they wish to describe and understand. If we were able to measure all of the areas and time periods that were of interest, questions of scale would vanish. Making inferences from these small areas to the larger surface they represent lie at the heart of many scaling problems. Replication alone is not the answer to the problem of integrating across scales (Dutilleul, 1993, 1998). Rather, emerging techniques facilitate these inferences by offering scale-sensitive experimental and sampling designs (Dutilleul, 1993, 1998). Moreover, it is increasingly possible to map land characteristics

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over large areas by extrapolating from relatively small samples of those areas (Cox et al., 1997; Johnson and Gage, 1997; Kitron, 1998; Mugglin and Carlin, 1998; Stein et al., 1998). There are procedures for aggregating fine-scale ecological knowledge into models representing coarser-scale, ecological properties and processes (Rastetter et al., 1992). These emerging techniques enhance the ability of ecologists to extend spatially explicit measurements across scales and to incorporate these measurements into useful models of ecosystems. 4.4. Model selection offers an alternative to hypothesis testing Simple hypothesis testing has a long tradition of application at single scales, while ecological modeling was one of the first areas of work to incorporate scaling effects. The rapid advance of a philosophy of research emphasizing testing among multiple competing models may soon eclipse hypothesis testing as a way of gaining ecological understanding (Johnson, 1999; Anderson et al., 2000, 2001). Because model selection explicitly considers trade-offs between model parsimony and explanatory power, it is particularly well suited for comparing multi-scale models. This is simply because incorporating relationships developed at fine-scales in ecological models often expand the number of variables and parameters they contain. Model selection allows us to carefully consider the costs and benefits of including scale-specific detail in choosing useful models. Accessible reviews of model selection techniques are available (Hilborn and Mangel, 1997; Burnham and Anderson, 1998). I urge all ecologists and resource managers to become familiar with these approaches as alternatives to traditional hypothesis testing. 4.5. Large-scale research is being rewarded The choices of research problems by scientists are strongly influenced by the rewards that accrue from different kinds of work. These rewards include grants, publications, speaking invitations, membership in academies, and so on. The traditional emphasis on research at single scales (typically small scales) results from a reward structure favoring high levels of precision in research results.

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However, it has become clear that an insistence on certainty in research results constrains the kinds of problems scientists are willing to attack, and in particular, this insistence has provided strong disincentives for large-scale work. As a result, ecologists are in danger of looking for a missing wallet under a street lamp (because the light was good) knowing full well it was lost in a dark parking lot. However, the reward structure of ecology is changing. This change is revealed clearly in the call for papers for the new journal of the Ecological Society of America, Conservation Ecology (Holling, 1997): ‘‘We prefer approximate answers to the right questions, not precise answers to the wrong questions.’’ The editorial policy of a major ecological journal emphasizes accepting some level of imprecision necessitated by attacking particularly important, but difficult, problems. Judging from recent publications in this journal, this often means research done at largescales, or better, work accomplished at multiple scales. There are other examples of a shift in rewards favoring large-scale work. Work at regional and global scales has been well supported as part of international efforts to understand effects of climate and land-use change on the biosphere (e.g. Walker and Steffen, 1997). The emergence of an entire sub-discipline of ecology, landscape ecology, has led to many new opportunities for large-scale research in universities and agencies. Major funding initiatives (e.g. Environmental Protection Agency STAR grants, National Science Foundation Integrated Research Challenges) have explicitly called for work requiring integration across scales. 4.6. Adaptive management is showing success The process of adaptive management (Holling, 1978; Walters, 1986; Lee, 1993) offers a clear, logical way to make use of the opportunities outlined above. There are a growing number of case studies illustrating its successful application (Walters and Green, 1997, Table 1). Here, I point out how the process can facilitate integration of knowledge across scales. As it was originally conceived, adaptive management required three phases (Holling, 1978; Walters, 1986). The first phase includes development of models abstracting the behavior of a system of interest. Often this phase involves ‘‘reverse reductionism’’

(Squire and Gibson, 1997) as systems models are assembled from small-scale, mechanistic studies. The model that emerges is used to ‘‘screen’’ credible hypotheses to be tested in management experiments, usually conducted at large-scales (Walters and Holling, 1990). Thus, the role of models is to eliminate ‘‘outlier’’ hypotheses that are not worth the cost and risks involved in implementing large-scale investigations (Walters, 1997). In the second phase, a range of potential actions screened in the initial modeling phase is incorporated into a research and management plan designed to evaluate those actions as competing alternatives. This evaluation should: (1) guide choices of future management actions, and (2) fill knowledge gaps identified in the initial modeling. In the third phase, the competing alternatives are implemented and evaluated at a scale relevant to the decisions they are intended to support. Performed correctly, this process explicitly integrates knowledge gained at multiple scales. Individual, mechanistic studies (often conducted using traditional experimental approaches at small scales) are aggregated to make quantitative and qualitative predictions. These predictions are used to produce competing explanations for whole system behavior. The competing explanations are then tested in designed experiments conducted at appropriate scales. Adaptive management is so sensible and widely advocated (Kessler et al., 1992; Swanson and Franklin, 1992; Lee, 1993; Kaufmann et al., 1994; Haney and Power, 1996; Williams et al., 1996; Ringold et al., 1999; but also see Illius et al., 2000) that it is puzzling that there are not more examples of its successful application than have been reported. This is, in part, because adaptive management has been challenged by many of the scale-related problems described above (McLain and Lee, 1996; Walters, 1997; Smith et al., 1998). Moreover, although adaptive management has been advocated for two decades, those 20 years may be insufficient to implement many truly long-term, large area studies. This is particularly true if those studies employ the staggered designs that are often needed to remove interactions between treatment and time (Walters et al., 1988). So, unlike traditional research approaches that can be evaluated in a few years, we should not expect the jury to return quickly, given the kinds of large-scale problems that adaptive management was designed to attack. However, serious

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institutional barriers continue to impede widespread application of adaptive management to environmental problems and account for many of the failures of the approach (Walters, 1997).

5. Conclusions: the need for integration of parts The imperative to contribute solutions to pressing environmental problems has motivated ecologists to match their scales of inquiry with the scales that matter to stakeholders and decision-makers (Theobald et al., 2000; Theobald and Hobbs, 2002). Simply ‘‘scalingup’’ studies to extend over larger areas and longer time frames is not the sole answer to this need. Traditional experiments writ large often fail to illuminate mechanisms that allow true understanding of system function, an understanding that is vital to making predictions about a changing world. Although large-scale studies will play a role, they will be complemented by an increased emphasis on the ‘‘science of integration of parts’’ (sensu Walters and Holling, 1990), on our ability to usefully aggregate diverse sources of information obtained across a range of scales.

Acknowledgements This work was supported by grants from United States National Science Foundation (9981368, 0119618).

References Adamczewski, J.Z., Hudson, R.J., Gates, C.C., 1993. Winter energy balance and activity of female caribou on coats island northwest territories: the relative importance of foraging and body reserves. Can. J. Zool. 71, 1221–1229. Adler, P.B., Raff, D.A., Lauenroth, W.K., 2001. The effect of grazing on the spatial heterogeneity of vegetation. Oecologia 128, 465–479. Allen, T.F.H., Hoekstra, T.W., 1992. Toward a Unified Ecology. Columbia University Press, New York. Alves, D.S., Skole, D.L., 1996. Characterizing land cover dynamics using multi-temporal imagery. Int. J. Remote Sens. 17, 835– 839. Andersen, R., Saether, B.E., 1992. Functional response during winter of a herbivore, the moose, in relation to age and size. Ecology 73, 542–550.

233

Anderson, D.R., Burnham, K.P., Thompson, W.L., 2000. Null hypothesis testing: problems, prevalence, and an alternative. J. Wildl. Manage. 64, 912–923. Anderson, D.R., Link, W.A., Johnson, D.H., Burnham, K.P., 2001. Suggestions for presenting the results of data analyses. J. Wildl. Manage. 65, 373–378. Apps, C.D., McLellan, B.N., Kinley, T.A., Flaa, J.P., 2001. Scaledependent habitat selection by mountain caribou, Columbia Mountains, British Columbia. J. Wildl. Manage. 65, 65–77. Atkinson, P.M., 1997. Scale and spatial dependence. In: Van Gardingen, P.R., Foody, G.M., Curran, P.J. (Eds.), Scaling-Up: From Cell to Landscape. Cambridge University Press, Cambridge, UK, pp. 35–60. Augustine, D.J., Frank, D.A., 2001. Effects of migratory grazers on spatial heterogeneity of soil nitrogen properties in a grassland ecosystem. Ecology 82, 3149–3162. Bailey, D.W., Gross, J.E., Laca, E.A., Rittenhouse, L.R., Coughenour, M.B., Swift, D.M., Sims, P.L., 1996. Mechanisms that result in large herbivore grazing distribution patterns. J. Range Manage. 49, 386–400. Baker, D.L., Hobbs, N.T., 1982. Composition and quality of elk summer diets in Colorado. J. Wildl. Manage. 46, 694–703. Begg, G.S., Reid, J.B., Tasker, M.L., Webb, A., 1997. Assessing the vulnerability of seabirds to oil pollution: sensitivity to spatial scale. Colon. Waterbirds 20, 339–352. Bergman, C.M., Fryxell, J.M., Gates, C.C., 2000a. The effect of tissue complexity and sward height on the functional response of Wood Bison. Funct. Ecol. 14, 61–69. Bergman, C.M., Schaefer, J.A., Luttich, S.N., 2000b. Caribou movement as a correlated random walk. Oecologia 123, 364–374. Bondesson, L., Stahl, G., Holm, S., 1998. Standard errors of area estimates obtained by traversing and GPS. For. Sci. 44, 405–413. Bradbury, J.W., Vehrencamp, S.L., Clifton, K.E., Clifton, L.M., 1996. The relationship between bite rate and local forage abundance in wild Thompson’s gazelles. Ecology 77, 2237– 2255. Bradshaw, G.A., 1998. Defining ecologically relevant change in the process of scaling up: implications for monitoring at the landscape level. In: Peterson, D.L., Parker, V.T. (Eds.), Ecological Scale: Theory and Applications. Columbia University Press, New York, pp. 227–249. Bravo De La Parra, R., Sanchez, E., Auger, P., 1997. Time-scales in density dependent discrete models. J. Biol. Syst. 5, 111–129. Brockman, R.P., Laarveld, B., 1986. Hormonal regulation of metabolism in ruminants a review. Livest. Prod. Sci. 14, 314–334. Burnham, K.P., Anderson, D.R., 1998. Model Selection and Inference: A Practical Information–Theoretic Approach. Springer-Verlag, New York. Burns, J.M., Castellini, M.A., 1998. Dive data from satellite tags and time-depth recorders: a comparison in Weddell seal pups. Mar. Mamm. Sci. 14, 750–764. Carter, J., Finn, J.T., 1999. MOAB: a spatially explicit, individualbased expert system for creating animal foraging models. Ecol. Model. 119, 29–41.

234

N.T. Hobbs / Forest Ecology and Management 181 (2003) 223–238

Clark, D.A., Harris, P.S., 1985. Composition of the diet of sheep grazing swards of differing white clover content and spatial distribution. N. Z. J. Agric. Res. 28, 233–240. Cohen, W.B., Kushla, J.D., Ripple, W.J., Garman, S.L., 1996. An introduction to digital methods in remote sensing of forested ecosystems: focus on the Pacific Northwest, USA. Environ. Manage. 20, 421–435. Cooper, S.D., Diehl, S., Kratz, K., Sarnelle, O., 1998. Implications of scale for patterns and processes in stream ecology. Aust. J. Ecol. 23, 27–40. Coughenour, M.B., 1991. Spatial components of plant–herbivore interactions in pastoral, ranching, and native ungulate ecosystems. J. Range Manage. 44, 530–542. Coughenour, M.B., 1992. Spatial modeling and landscape characterization of an African pastoral ecosystem: a prototype model and its potential use for monitoring drought. In: Ecological Indicators, vol. I. Elsevier, London and New York, pp. 787–810. Cox, D.D., Cox, L.H., Ensor, K.B., 1997. Spatial sampling and the environment: some issues and directions. Environ. Ecol. Stat. 4, 219–233. Cramer, P.C., Portier, K.M., 2001. Modeling Florida panther movements in response to human attributes of the landscape and ecological settings. Ecol. Model. 140, 51–80. Danell, K., Edenius, L., Lundberg, P., 1991. Herbivory and tree stand composition moose patch use in winter. Ecology 72, 1350–1357. De Mazancourt, C., Loreau, M., Abbadie, L., 1998. Grazing optimization and nutrient cycling: when do herbivores enhance plant production? Ecology 79, 2242–2252. Dobermann, A., Goovaerts, P., Neue, H.U., 1997. Scale-dependent correlations among soil properties in two tropical lowland rice fields. Soil Sci. Soc. Am. J. 61, 1483–1496. Duarte, C.M., Vaque, D., 1992. Scale dependence of bacterioplankton patchiness. Mar. Ecol. Prog. Ser. 84, 95–100. Dutilleul, P., 1993. Spatial heterogeneity and the design of ecological field experiments. Ecology 74, 1646–1658. Dutilleul, P., 1998. Incorporating scale in ecological experiments: study design. In: Peterson, D.L., Parker, V.T. (Eds.), Ecological Scale: Theory and Applications. Columbia University Press, New York, pp. 369–386. Edwards, P.J., May, R.M., Webb, N.R., 1994. Large-Scale Ecology and Conservation Biology. Blackwell Scientific, Oxford. Edwards, G.R., Newman, J.A., Parsons, A.J., Krebs, J.R., 1996. The use of spatial memory by grazing animals to locate food patches in spatially heterogeneous environments: an example with sheep. Appl. Anim. Behav. Sci. 50, 147–160. Ehleringer, J.R., Field, C.B., 1993. Scaling Physiological Process: Leaf to Globe. Academic Press, New York. Etzenhouser, M.J., Owens, M.K., Spalinger, D.E., Murden, S.B., 1998. Foraging behavior of browsing ruminants in a heterogeneous landscape. Landsc. Ecol. 13, 55–64. Falloon, P.D., Smith, P., Smith, J.U., Szabo, J., Coleman, K., Marshall, S., 1998. Regional estimates of carbon sequestration potential: linking the Rothamsted Carbon Model to GIS databases. Biol. Fertil. Soils 27, 236–241. Fancy, S.G., White, R.G., 1985. Energy expenditures by caribou while cratering in snow. J. Wildl. Manage. 49, 987–993.

Fragoso, J.M.V., 1999. Perception of scale and resource partitioning by peccaries: behavioral causes and ecological implications. J. Mammal. 80, 993–1003. Frank, D.A., Groffman, P.M., 1998. Ungulate vs. landscape control of soil C and N processes in grasslands of Yellowstone National Park. Ecology 79, 2229–2241. Fuhlendorf, S.D., Smeins, F.E., 1999. Scaling effects of grazing in a semi-arid grassland. J. Veg. Sci. 10, 731–738. Gardner, R.H., 1998. Pattern, process and the analysis of scale dependence. In: Peterson, D.L., Parker, V.T. (Eds.), Ecological Scale: Theory and Applications. Columbia University Press, New York, pp. 18–34. Gillingham, M.P., Bunnell, F.L., 1989a. Black-tailed deer feeding bouts dynamic events. Can. J. Zool. 67, 1353–1362. Gillingham, M.P., Bunnell, F.L., 1989b. Effect of learning on food selection and searching behavior of deer. Can. J. Zool. 67, 24–32. Gillingham, M.P., Parker, K.L., Hanley, T.A., 1997a. Forage intake by black-tailed deer in a natural environment: bout dynamics. Can. J. Zool. 75, 1118–1128. Gillingham, M.P., Parker, K.L., Hanley, T.A., 1997b. Forage intake by black-tailed deer in a natural environment: bout dynamics. Can. J. Zool.-Rev. Can. Zool. 75, 1118–1128. Ginnett, T.F., Demment, M.W., 1999. Sexual segregation by Masai giraffes at two spatial scales. Afr. J. Ecol. 37, 93–106. Godfray, H.C.J., Lawton, J.H., 2001. Scale and species numbers. Trends Ecol. Evol. 16, 400–404. Goetz, S.J., Prince, S.D., 1996. Remote sensing of net primary production in boreal forest stands. Agric. For. Meteorol. 78, 149–179. Grace, J., Gardingen, P.R.V., Luan, J., 1997. Tackling large-scale problems by scaling-up. In: Van Gardingen, P.R., Foody, G.M., Curran, P.J. (Eds.), Scaling-Up: From Cell to Landscape. Cambridge University Press, Cambridge, UK, pp. 7–16. Grime, J.P., Thompson, K., Macguillivray, C.W., 1997. Scaling from plant to community and from plant to regional flora. In: Van Gardingen, P.R., Foody, G.M., Curran, P.J. (Eds.), ScalingUp: From Cell to Landscape. Cambridge University Press, Cambridge, UK, pp. 105–128. Gross, J.E., Shipley, L.A., Hobbs, N.T., Spalinger, D.E., Wunder, B.A., 1993. Functional response of herbivores in foodconcentrated patches tests of a mechanistic model. Ecology 74, 778–791. Gross, J.E., Zank, C., Hobbs, N.T., Spalinger, D.E., 1995. Movement rules for herbivores in spatially heterogeneous environments: responses to small scale pattern. Landsc. Ecol. 10, 209–217. Haney, A., Power, R.L., 1996. Adaptive management for sound ecosystem management. Environ. Manage. 20, 879–886. Hargrove, W.W., Pickering, J., 1992. Pseudoreplication a sine qua non for regional ecology. Landsc. Ecol. 6, 251–258. He Hong, S., Mladenoff, D.J., 1999. Spatially explicit and stochastic simulation of forest-landscape fire disturbance and succession. Ecology 80, 81–99. Hilborn, R., Mangel, M., 1997. The Ecological Detective: Confronting Models with Data. Princeton University Press, Princeton, NJ, USA.

N.T. Hobbs / Forest Ecology and Management 181 (2003) 223–238 Hobbs, N.T., 1988. Estimating habitat carrying capacity: an approach for planning reclamation and mitigation for wild ungulates. In: Proceedings III: Issues and Technology in the Management of Impacted Wildlife, November 2–4, 1987, Colorado Springs, CO. Thorne Ecological Institute, pp. 3–7. Hobbs, N.T., Baker, D.L., Ellis, J.E., Swift, D.M., 1981. Composition and quality of elk winter diets in Colorado. J. Wildl. Manage. 45, 156–171. Hobbs, N.T., Baker, D.L., Gill, R.B., 1983. Comparative nutritional ecology of montane ungulates during winter. J. Wildl. Manage. 47, 1–16. Hobbs, R.J., 1998. Managing ecological systems and processes. In: Peterson, D.L., Parker, V.T. (Eds.), Ecological Scale: Theory and Applications. Columbia University Press, New York, pp. 459–484. Holling, C.S. (Ed.), 1978. Adaptive Environmental Assessment and Management. Wiley International Series on Applied Systems Analysis, Chichester, UK. Holling, C.S., 1997. The inaugural issue of Conservation Ecology. Conserv. Ecol. 1 (online), URL: http://ns2.resalliance.org/pub/ www/Journal/vol1/iss1/art1/index.html. Hurlbert, S.H., 1984. Pseudoreplication and the design of ecological field experiments. Ecol. Monogr. 54, 187–211. Illius, A.W., Gordon, I.J., 1999. Scaling-up from functional response to numerical response in vertebrate herbivores. In: Olff, H., Brown, V.K., Drent, R.H. (Eds.), Herbivores: Between Predators and Plants. Blackwell Science, Oxford, UK, pp. 397– 497. Illius, A.W., O’Connor, T.G., 1999. On the relevance of nonequilibrium concepts to arid and semiarid grazing systems. Ecol. Appl. 9, 798–813. Illius, A.W., O’Connor, T.G., 2000. Resource heterogeneity and ungulate population dynamics. Oikos 89, 283–294. Illius, A.W., Derry, J.F., Gordon, I.J., 2000. Evaluation of strategies for tracking climatic variation in semi-arid grazing systems. Agric. Syst. 63, 73–74. Jia, J., Niemela, P., Danell, K., 1995. Moose (Alces alces) bite diameter selection in relation to twig quality on four phenotypes of Scots pine Pinus sylvestris. Wildl. Biol. 1, 47–55. Jiang, Z., Hudson, R.J., 1993. Optimal grazing of wapiti Cervus elaphus on grassland patch and feeding station departure rules. Evol. Ecol. 7, 488–498. Johnson, D.H., 1999. The insignificance of statistical significance testing. J. Wildl. Manage. 63, 763–772. Johnson, L.B., Gage, S.H., 1997. Landscape approaches to the analysis of aquatic ecosystems. Freshwater Biol. 37, 113–132. Johnson, C.J., Parker, K.L., Heard, D.C., 2001. Foraging across a variable landscape: behavioral decisions made by woodland caribou at multiple spatial scales. Oecologia 127, 590–602. Kaufmann, M.R., Graham, R.T., Boyce, D.A., Perry, W.H.M.L., Reynolds, R.T., Bassett, R.L., Mehlhop, P.C., Edminster, B., Block, W.M., Corn, P.S., 1994. An Ecological Basis for Ecosystem Management. Rocky Mountain Forest and Range Experiment Station, Fort Collins, CO. Kaye, T., Croft, D.A., 1998. Do you know where your field crew has been? Using full-spectrum GPS to track, coordinate, and assess. J. Vertebr. Paleontol. 18, 201–207.

235

Keitt, T.H., Urban, D.L., Milne, B.T., 1997. Detecting critical scales in fragmented landscapes. Conserv. Ecol. 1 (online), URL: http://ns2.resalliance.org/pub/www/Journal/vol1/iss1/art4/ index.html. Kessler, W.B., Salwasser, H., Cartwright Jr., C.W., Caplan, J.A., 1992. New perspectives for sustainable natural resource management. Ecol. Appl. 2, 221–225. Kidunda, R.S., Rittenhouse, L.R., Swift, D.M., Richards, R.W., 1993. Spatial behavior of free-grazing cattle: movement from patch to patch. Proceedings Western Section. Am. Soc. Anim. Sci. 44, 44. Kitron, U., 1998. Landscape ecology and epidemiology of vectorborne diseases: tools for spatial analysis. J. Med. Entomol. 35, 435–445. Klein, D.R., Bay, C., 1991. Diet selection by vertebrate herbivores in the high arctic of Greenland. Holarct. Ecol. 14, 152–155. Knapp, A.K., Blair, J.M., Briggs, J.M., Collins, S.L., Hartnett, D.C., Johnson, L.C., Towne, E.G., 1999. The keystone role of bison in North American tallgrass prairie—Bison increase habitat heterogeneity and alter a broad array of plant, community, and ecosystem processes. Bioscience 49, 39–50. Knopf, F.L., Sampson, F.B., 1994. Scale perspectives of avian diversity in western riparian ecosystems. Conserv. Biol. 8 (3), 669–676. Kohlmann, S.G., Risenhoover, K.L., 1994. Spatial and behavioralresponse of white-tailed deer to forage depletion. Can. J. Zool.Rev. Can. Zool. 72, 506–513. Kohlmann, S.G., Risenhoover, K.L., 1997. White-tailed deer in a patchy environment: a test of the ideal-free-distribution theory. J. Mammal. 78, 1261–1272. Kolasa, J., Pickett, S.T.A., 1991. Ecological Heterogeneity. Springer-Verlag, Berlin. Kotler, B.P., Gross, J.E., Mitchell, W.A., 1994. Applying patch use to assess aspects of foraging behavior in Nubian Ibex. J. Wildl. Manage. 58, 299–307. Kruit, B., Ongeri, S., Jarvis, P.G., 1997. Scaling of PAR absorption, photosynthesis and transpiration from leaves to canopy. In: Van Gardingen, P.R., Foody, G.M., Curran, P.J. (Eds.), Scaling-Up: From Cell to Landscape. Cambridge University Press, Cambridge, UK, pp. 79–104. Laca, E., Ortega, I.M., 1995. Integrating foraging mechanisms across spatial and temporal scales. In: West, N.E. (Ed.), Rangelands in a Sustainable Biosphere. Society for Range Management, Denver, CO, pp. 129–132. Laca, E.A., Distel, R.A., Griggs, T.C., Deo, G.P., Demment, M.W., 1993. Field test of optimal foraging with cattle: the marginal value theorem successfully predicts patch selection and utilisation. In: Proceedings of XVII International Grassland Congress, New Zealand and Queensland, February 1993, pp. 709–701. Laca, E.A., Distel, A., Griggs, T.C., Demment, M.W., 1994. Effects of canopy structure on patch depression by grazers. Ecology 75, 706–716. Langvatn, R., Hanley, T.A., 1993. Feeding-patch choice by red deer in relation to foraging efficiency. Oecologia 95, 164–170. Lawes, M.J., Eeley, H.A.C., 2000. Are local patterns of anthropoid primate diversity related to patterns of diversity at a larger scale? J. Biogeogr. 27, 1421–1435.

236

N.T. Hobbs / Forest Ecology and Management 181 (2003) 223–238

Lee, K.N., 1993. Compass and Gyroscope: Integrating Science and Politics for the Environment. Island Press, Washington, DC. Letcher, B.H., Priddy, J.A., Walters, J.R., Crowder, L.B., 1998. An individual-based, spatially-explicit simulation model of the populations dynamics of the endangered red-cockaded woodpecker, Picoides borealis. Biol. Conserv. 86, 1–14. Levin, S.A., 1992. The problem of pattern and scale in ecology. Ecology 73, 1943–1967. Lewis, M.A., Murray, J.D., 1993. Modelling territoriality and wolf– deer interactions. Nature 366, 738–740. Loehle, C., 1990. Home range: a fractal approach. Landsc. Ecol. 5, 39–52. Loreau, M., Naeem, S., Inchausti, P., Bengtsson, J., Grime, J.P., Hector, A., Hooper, D.U., Huston, M.A., Rafaelli, D., Schmid, B., Tilman, D., Wardle, D.A., 2001. Ecology—biodiversity and ecosystem functioning: current knowledge and future challenges. Science 294, 804–808. Lundberg, P., Danell, K., 1990a. Functional response of browsers tree exploitation by moose. Oikos 58, 378–384. Lundberg, P., Danell, K., 1990b. Functional response of browsers tree exploitation by moose. Oikos 58, 378–384. Martinez, N.D., 1994. Scale-dependent constraints on food-web structure. Am. Nat. 144, 935–953. McInnes, P.F., Naiman, R.J., Pastor, J., Cohen, Y., 1992. Effects of moose browsing on vegetation and litter of the boreal forest, Isle Royale, Michigan, USA. Ecology 73, 2059–2075. McLain, R.J., Lee, R.G., 1996. Adaptive management: promises and pitfalls. Environ. Manage. 20, 437–448. Meltzer, M.I., Hasting, H.M., 1992. The use of fractals to assess the ecological impact of increased cattle population: case study from the Runde Communal Land, Zimbabwe. J. Appl. Ecol. 29, 635–646. Milne, B.T., 1991a. Lessons from applying fractal models to landscape patterns. In: Turner, M.G., Gardner, R.H. (Eds.), Springer-Verlag, New York, pp. 199–238. Milne, B.T., 1991b. The utility of fractal geometry in landscape design. Landsc. Urban Plann. 21, 81–90. Milne, B.T., Johnston, K.M., Forman, R.T.T., 1989. Scaledependent proximity of wildlife habitat in a spatially-neutral Bayesian model. Landsc. Ecol. 2, 101–110. Moen, R., Pastor, J., Cohen, Y., 1997. A spatially explicit model of moose foraging and energetics. Ecology 78, 505–521. Moen, R., Cohen, Y., Pastor, J., 1998. Linking moose population and plant growth models with a moose energetics model. Ecosystems 1, 1–13. Mugglin, A.S., Carlin, B.P., 1998. Hierarchical modeling in geographic information systems: population interpolation over incompatible zones. J. Agric. Biol. Environ. Stat. 3, 111–130. Murray, M.G., 1991. Maximizing energy retention in grazing ruminants. J. Anim. Ecol. 60, 1029–1045. Mysterud, A., Lian, L.B., Hjermann, D.O., 1999. Scale-dependent trade-offs in foraging by European roe deer (Capreolus capreolus) during winter. Can. J. Zool.-Rev. Can. Zool. 77, 1486–1493. Nisbet, R.M., Diehl, S., Wilson, W.G., Cooper, S.D., Donaldson, D.D., Kratz, K., 1997. Primary-productivity gradients and short-term population dynamics in open systems. Ecol. Monogr. 67, 535–553.

Oesterheld, M., Dibella, C.M., Kerdiles, H., 1998. Relation between NOAA-AVHRR satellite data and stocking rate of rangelands. Ecol. Appl. 8, 207–212. O’Neill, R.V., King, A.W., 1998. Homages to St. Michael or, why are there so many books on scale? In: Peterson, D.L., Parker, V.T. (Eds.), Ecological Scale: Theory and Applications. Columbia University Press, New York, pp. 3–15. O’Neill, R.V., DeAngelis, D.L., Waide, J.B., Allen, T.F.H., 1986. A Hierachical Concept of Ecosystems. Princeton University Press, Princeton, NJ. Ornstein, R., Ehrlich, P.R., 1989. New World. New Mind. Doubleday, New York. Palmer, M.W., White, P.S., 1994. Scale dependence and the species–area relationship. Am. Nat. 144, 717–740. Parker, K.L., Gillingham, M.P., Hanley, T.A., Robbins, C.T., 1996. Foraging efficiency: energy expenditure versus energy gain in free-ranging black-tailed deer. Can. J. Zool.-Rev. Can. Zool. 74, 442–450. Pastor, J., Naiman, R.J., 1992. Selective foraging and ecosystem processes in boreal forests. Am. Nat. 139, 690–705. Pastor, J., Cohen, Y., 1997. Herbivores, the functional diversity of plants species, and the cycling of nutrients of ecosystems. Theor. Popul. Biol. 51, 165–179. Pastor, J.P., Naiman, R.J., Dewey, B., McInnes, P., 1988. Moose, microbes, and the boreal forest. Biol. Sci. 38, 770–778. Pastor, J., Dewey, B., Naiman, R.J., McInnes, P.F., Cohen, Y., 1993. Moose browsing and soil fertility in the boreal forests of Isle Royale National Park. Ecology 74, 467–480. Pastor, J., Moen, R., Cohen, Y., 1997. Spatial heterogeneities, carrying capacity, and feedbacks in animal–landscape interactions. J. Mammal. 78, 1040–1052. Payne, R., Harty, C., 1998. Wetland vegetation mapping using a global positioning system. Cunninghamia 5, 633–643. Peterson, G., Allen, C.R., Holling, C.S., 1998. Ecological resilience, biodiversity, and scale. Ecosystems 1, 6–18. Pickett, S.T.A., Parker, V.T., Fiedler, P.L., 1992. The new paradigm in ecology: implications for conservation biology above the species level. In: Fiedler, P.L., Jain, S.K. (Eds.), Conservation Biology: The Theory and Practice of Nature Conservation, Preservation, and Management. Chapman and Hall, New York, pp. 66–88. Powell, T.M., 1989. Physical and biological scales of variability in lakes, estuaries, and the coastal ocean. In: Roughgarden, J., May, R.M., Levin, S.A. (Eds.), Perspectives in Ecological Theory. Princeton University Press, Princeton, NJ, pp. 157–176. Rastetter, E.B., King, A.W., Cosby, B.J., Hornberger, G.M., O’Neill, R.V., Hobbie, J.E., 1992. Aggregating fine-scale ecological knowledge to model coarser-scale attributes of ecosystems. Ecol. Appl. 2, 55–70. Reed, R.A., Peet, R.K., Palmer, M.W., White, P.S., 1993. Scale dependence of vegetation-environment correlations a case study of a North Carolina piedmont woodland. J. Veg. Sci. 4, 329–340. Ringold, P.L., Mulder, B., Alegria, J., Czaplewski, R.L., Tolle, T., Burnett, K., 1999. Establishing a regional monitoring strategy: the Pacific Northwest Forest Plan. Environ. Manage. 23, 179–192.

N.T. Hobbs / Forest Ecology and Management 181 (2003) 223–238 Ritchie, M.E., 1998. Scale-dependent foraging and patch choice in fractal environments. Evol. Ecol. 12, 309–330. Roese, J.H., Risenhoover, K.L., Folse, L.J., 1991. Habitat heterogeneity and foraging efficiency: an individual-based model. Ecol. Model. 57, 133–143. Rogers, L.L., Mech, D., Dawson, D.K., Peek, J.M., Korb, M., 1980. Deer distribution in relations to wolf pack territory edges. J. Wildl. Manage. 44, 253–258. Romesburg, H.C., 1981. Wildlife science: gaining reliable knowledge. J. Wildl. Manage. 45, 293–314. Roy, P.S., Ravan, S.A., 1996. Biomass estimation using satellite remote sensing data: an investigation on possible approaches for natural forest. J. Biosci. 21, 535–561. Rykiel, E.R., 1998. Relationships of scale to policy and decisionmaking. In: Peterson, D.L., Parker, V.T. (Eds.), Ecological Scale: Theory and Applications. Columbia University Press, New York, pp. 485–497. Schaefer, J.A., Messier, F., 1995a. Scale-dependent correlations of arctic vegetation and snow cover. Arct. Alp. Res. 27, 38–43. Schaefer, J.A., Messier, F., 1995b. Winter foraging by Muskoxen— a hierarchical approach to patch residence time and cratering behavior. Oecologia 104, 39–44. Schimel, D.S., 1995. Terrestrial biogeochemical cycles: global estimates with remote sensing. Remote Sens. Environ. 51, 49–56. Schneider, D.C., 1997. Quantitative Ecology: Spatial and Temporal Scaling. Academic Press, New York. Schneider, D.C., 1998. Applied scaling theory. In: Peterson, D.L., Parker, V.T. (Eds.), Ecological Scale: Theory and Applications. Columbia University Press, New York, pp. 254–269. Seagle, S.W., McNaughton, S.J., 1992. Spatial variation in forage nutrient concentrations and the distribution of Serengeti grazing ungulates. Landsc. Ecol. 7, 229–241. Senft, R.L., Coughenour, M.B., Bailey, D.W., Rittenhouse, L.R., Sala, O.E., Swift, D.M., 1987. Large herbivore foraging and ecological hierarchies. Bioscience 37, 789–799. Shipley, L.A., Spalinger, D.E., 1992. Mechanics of browsing in dense food patches effects of plant and animal morphology on intake rate. Can. J. Zool. 70, 1743–1752. Simberloff, D., 1988. The contribution of population and community biology to conservation science. Ann. Rev. Ecol. Syst. 19, 473–511. Skarpe, C., Bergstrom, R., Braten, A.L., Danell, K., 2000. Browsing in a heterogeneous savanna. Ecography 23, 632–640. Smith, C.L., Gilden, J., Steel, B.S., Mrakovcich, K., 1998. Sailing the shoals of adaptive management: the case of salmon in the Pacific Northwest. Environ. Manage. 22, 671–681. Sommer, S., Hill, J., Megier, J., 1998. The potential of remote sensing for monitoring rural land-use changes and their effects on soil conditions. Agric. Ecosyst. Environ. 67, 197–209. Spalinger, D.E., Hobbs, N.T., 1992. Mechanisms of foraging in mammalian herbivores: new models of functional response. Am. Nat. 140, 325–348. Squire, G.R., Gibson, G.J., 1997. Scaling-up and scaling-down: matching research with requirements in land management and policy. In: Van Gardingen, P.R., Foody, G.M., Curran, P.J. (Eds.), Scaling-Up: From Cell to Landscape. Cambridge University Press, Cambridge, UK, pp. 17–34.

237

Stein, A., Bastiaanssen, W.G.M., De Bruin, S., Cracknell, A.P., Curran, P.J., Fabbri, A.G., Gorte, B.G.H., Van Groenigen, J.W., Van Der Meer, F.D., Saldana, A., 1998. Integrating spatial statistics and remote sensing. Int. J. Remote Sens. 19, 1793–1814. Steinauer, E.M., Collins, S.L., 2001. Feedback loops in ecological hierarchies following urine deposition in tallgrass prairie. Ecology 82, 1319–1329. Stern, S.J., 1998. Field studies of large mobile organisms: scale, movement, and habitat utilization. In: Peterson, D.L., Parker, V.T. (Eds.), Ecological Scale: Theory and Applications. Columbia University Press, New York, pp. 289– 307. Stohlgren, T.J., Chong, G.W., Kalkhan, M.A., Schell, L.D., 1997. Multi-scale sampling of plant diversity: effects of the minimum mapping unit. Ecol. Appl. 7, 1064–1074. Swanson, F.J., Franklin, J.F., 1992. New forestry principles from ecosystem analysis of pacific northwest forests. Ecol. Appl. 2, 262–274. Taylor, R.J., Pekins, P.J., 1991. Territory boundary avoidance as a stabilizing factor in wolf–deer interactions. J. Theor. Popul. Biol. 39, 115–128. Theobald, D.M., Hobbs, N.T., 2002. A framework for evaluating land-use planning alternatives: protecting biodiversity on private land. Conserv. Ecol. 6, 5 (online), URL: http:// www.consecol.org/vol6/iss1/art5. Theobald, D.M., Hobbs, N.T., Bearly, T., Zack, J.A., Shenk, T., Riebsame, W.E., 2000. Incorporating biological information in local land-use decision-making: designing a system for conservation planning. Landsc. Ecol. 15, 35–45. Tilman, D.T., Kareiva, P., 1997a. Spatial Ecology: The Role of Space in Populations Dynamics and Interspecific Interactions. Princeton University Press, Princeton, NJ. Tilman, D.T., Kareiva, P., 1997b. Preface. In: Tilman, D., Kareiva, P. (Eds.), Spatial Ecology: The Role of Space in Populations Dynamics and Interspecific Interactions. Princeton University Press, Princeton, NJ, pp. vii–xi. Tiwari, A.K., Kudrat, M., Manchanda, M.L., 1996. Remote sensing and GIS: indispensable tools for regional ecological studies. Trop. Ecol. 37, 79–92. Turner, M.G., Wu, Y., Romme, W.H., Wallace, L.L., 1993. A landscape simulation model of winter foraging by large ungulates. Ecol. Model. 69, 163–184. Turner, M.G., Wu, Y., Wallace, L.L., Romme, W.H., Brenkert, A., 1994a. Simulating winter interactions among ungulates, vegetation and fire in Northern Yellowstone Park. Ecol. Appl. 4, 472–496. Turner, M.G., Wu, Y.A., Wallace, L.L., Romme, W.H., Brenkert, A., 1994b. Simulating winter interactions among ungulates, vegetation, and fire in Northern Yellowstone Park. Ecol. Appl. 4, 472–486. Tyler, J.A., Hargrove, W.W., 1997. Predicting spatial distribution of foragers over large resource landscapes: a modeling analysis of the ideal free distribution. Oikos 79, 376–386. Uziel, E., Berry, M.W., 1995. Parallel models of animal migration in Northern Yellowstone National Park. Int. J. Supercomput. Appl. High Perform. Comput. 9, 237–255.

238

N.T. Hobbs / Forest Ecology and Management 181 (2003) 223–238

Van Gardingen, P.R., Foody, G.M., Curran, P.J., 1997. Scaling-Up: From Cell to Landscape. Cambridge University Press, Cambridge, UK. Veblen, T.T., Lorenz, D.C., 1991. The Colorado Front Range: A Century of Ecological Change. University of Utah Press, Salt Lake City, UT. Vedyushkin, M.A., 1994. Fractal properties of forest spatial structure. Vegetatio 113, 65–70. Viswanathan, G.M., Buldyrev, S.V., Havlin, S., da Luz, M.G.E., Raposo, E.P., Stanley, H.E., 1999. Optimizing the success of random searches. Nature 401, 911–914. Vivas, H.J., Saether, B.E., Andersen, R., 1991. Optimal twig-size selection of a generalist herbivore, the moose Alces alces: implications for plant–herbivore interactions. J. Anim. Ecol. 60, 395–408. Walker, B., Steffen, W., 1997. An overview of the implications of global change for natural and managed terrestrial ecosystems. Conserv. Ecol. (online), URL: http://www.consecol.org/vol1/ iss2/art2. Wallis de Vries, M.F., 1996. Effects of resource distribution patterns on ungulate foraging behaviour: a modelling approach. For. Ecol. Manage. 88, 167–177. Wallis de Vries, M.F., Schippers, P., 1994. Foraging strategy of cattle in patchy grassland. Oecologia 100, 98–106. Wallis de Vries, M.F., Laca, E.A., Demment, M.W., 1998. From feeding station into patch: Scaling up food intake measurements in grazing cattle. Appl. Anim. Behav. Sci. 60, 301–315. Wallis de Vries, M.F., Laca, E.A., Demment, M.W., 1999. The importance of scale of patchiness for selectivity in grazing herbivores. Oecologia 121, 355–363. Walters, C.J., 1986. Adaptive Management of Renewable Resources. Macmillian, New York. Walters, C., 1997. Challenges in adaptive management of riparian and coastal ecosystems. Conserv. Ecol. 1 (online), URL: http:// ns2.resalliance.org/pub/www/Journal/vol1/iss2/art1/index.html. Walters, C.J., Holling, C.S., 1990. Large-scale management experiments and learning by doing. Ecology 71, 2060–2068. Walters, C.J., Green, R., 1997. Valuation of experimental management options for ecological systems. J. Wildl. Manage. 61, 987–1006.

Walters, C.J., Collie, J.S., Webb, T., 1988. Experimental designs for estimating transient responses to management disturbances. Can. J. Fish. Aquat. Sci. 45, 530–538. Ward, D., Saltz, D., 1994. Foraging at different spatial scales: dorcas gazelles foraging for lilies in the Negev Desert. Ecology 75, 48–58. Weckerly, F.W., 1994. Selective feeding by black-tailed deer: forage quality or abundance? J. Mammal. 75, 905–913. Weisberg, P.J., Hobbs, N.T., Ellis, J.E., Coughenour, M.B., 2002. An ecosystem approach to population management of ungulates. J. Environ. Manage., in press. White, K.A.J., Murray, J.D., Lewis, M.A., 1996. Wolf–deer interactions: a mathematical model. Proc. R. Soc. Lond. Series B: Biol. Sci. 263, 299–305. Whittaker, R.J., Willis, K.J., Field, R., 2001. Scale and species richness: towards a general, hierarchical theory of species diversity. J. Biogeogr. 28, 453–470. Wickstrom, M.L., Robbins, C.T., Hanley, T.A., Spalinger, D.E., Parish, S.M., 1984. Food intake and foraging energetics of elk and mule deer. J. Wildl. Manage. 48, 1285–1301. Wiens, J.A., 1989. Spatial scaling in ecology. Funct. Ecol. 3, 385–397. Wiens, J.A., Crist, T.O., With, K.A., Milne, B.T., 1995. Fractal patterns of insect movement in microlandscape mosaics. Ecology 76, 663–666. Williams, B., Johnson, F.A., Wilkins, K., 1996. Uncertainty and the adaptive management of waterfowl harvests. J. Wildl. Manage. 60, 223–232. Wilmshurst, J.F., Fryxell, J.M., 1995. Patch selection by red deer in relation to energy and protein intake: a re-evaluation of Langvatn and Hanley’s (1993) results. Oecologia 104, 297–300. Wilmshurst, J.F., Fryxell, J.M., Bergman, C.M., 2000. The allometry of patch selection in ruminants. Proc. R. Soc. Lond. Ser. B—Biol. Sci. 267, 345–349. Wu, J., Levin, S.A., 1994. A spatial patch dynamic modeling approach to pattern and process in an annual grassland. Ecol. Monogr. 64, 447–464. Wu, J., Loucks, O.L., 1995. From balance of nature to hierarchical patch dynamics: a paradigm shift in ecology. Q. Rev. Biol. 70, 439–466.