Title: Range of Natural Variability: What does it mean for sustainable forest management?
Publication Type: Journal Article
Year of Publication: 2002
Authors: Drever R, Wong C
Start Page: 11-17
What is RONV? The concept of the range of natural variability is increasingly recognized as an important element of strategies for sustainable forest management. Generally, the range of natural variability (RONV) refers to the spectrum of natural conditions possible in ecosystem structure, composition, and function, when considering both temporal and spatial scales (Swanson et al. 1994) (Figure 1). This concept is useful for evaluating how management interventions affect the structures, composition, and functions of forest ecosystems. Here, our objectives are to review the concept of RONV, discuss its merits and challenges and provide a checklist with which to evaluate a defensible determination of RONV.
Figure 1 – Hypothetical range of natural variability for a given attribute of interest in a forest. This could refer to stand-level
attributes such as snag density or amount of tall old trees as well as landscape-level attributes such as the amount of old forest or
size of stand-replacing fires. The outside lines indicate the full range of possible values over time, whereas the inside dotted lines
indicate the range within which lie 90 percent of the values for the attribute of interest. The box at the right side of the figure indicates
a hypothetical “apparent range of variability” – the variability that can be observed by a given method of observation, e.g., tree
Why is it important for sustainable forest management?
Holling and Meffe (1996) defined the “Golden Rule” of sustainable natural resource management as: “management should strive to retain critical types and ranges of natural variation in resource systems in order to maintain their resiliency.” Retaining ecosystem structure, composition, and function in managed forests within their RONV increases the likelihood of maintaining ecosystem integrity and biodiversity. Ecosystem integrity and biodiversity are thought to be two characteristics of ecosystems that allow them to be resilient, i.e., capable of absorbing disturbances without reorganizing from one ecosystem to another stable state (Holling 1973; Folke et al. 1996). Forest ecosystems that are resilient are capable, theoretically, of providing in perpetuity ecological services and products such as water filtration and timber (Scheffer et al. 2001). What are the challenges in using RONV?
Using RONV to guide planning and implementation of forest practices is not simple. The use of RONV is inherently challenging because it attempts to characterize ecological processes and patterns that can vary greatly across time and space and are stochastic rather than strictly deterministic (Swanson et al. 1994; Landres et al. 1999). Moreover, little consensus exists in the scientific literature about clear definitions (e.g., just what is “natural”?) and what methods are appropriate for measuring and applying the RONV concept. Several of these issues are related to scale, in particular about how to adequately determine the ‘bounds’ of an ecosystem that remain relatively consistent over time and space (Morgan et al. 1994).
Simply put, how far back in time and how big of an area is necessary to capture the full range of variability? In general, the scales of analysis for forest research and management depend intimately on the ecosystem attribute or landscape pattern in consideration, i.e., the nature of the disturbance regimes that create the attribute or pattern, their climatic and topographic “drivers,” and the lifespan of trees (Allen and Hoekstra 1991; Swetnam et al. 1999; Wong and Iverson 2001). For instance, measuring the variation in the recruitment of large downed cedar material in coastal rainforests involves time scales on the order of several hundred to a thousand years, whereas to determine the RONV in the frequency of fires in interior Ponderosa pine forests involves measuring intervals between fires over at least a hundred years.
No single, widely applicable optimal period exists for determining RONV, and “relevance is lost if too long a time period is used, because conditions such as climate and species composition may have changed dramatically” (Landres et al. 1999). For forest ecosystems, an appropriate time scale should generally encompass several generations of trees or disturbance events in order to estimate the variation between events (Morgan et al. 1994). Thus, selecting an appropriate time period becomes a trade-off between a period long enough to fully capture natural variability but not so long that it encompasses significant changes like climatic shifts. A temporal extent of 2,000 years – as, for example, is stipulated in the Forest Stewardship Council’s definition of RONV – is likely broad enough to allow achieving this tradeoff in most ecosystems for the various ecosystem attributes considered in the Standards (FSC-BC Standards Team 2001). However, the temporal extent must consider RONV before the advent of European settlement to estimate the variation between events (Morgan et al. 1994). Thus, selecting an appropriate time period becomes a trade-off between a period long enough to fully capture natural variability but not so long that it encompasses significant changes like climatic shifts.
A temporal extent of 2,000 years – as, for example, is stipulated in the Forest Stewardship Council’s definition of RONV – is likely broad enough to allow achieving this tradeoff in most ecosystems for the various ecosystem attributes considered in the Standards (FSC-BC Standards Team 2001). However, the temporal extent must consider RONV before the advent of European settlement to avoid capturing our relatively rapid (from the perspective of ecosystem change) impacts on forest structure and composition from clear cutting, road building, igniting fires for mineral exploration, introduced species, fire suppression, livestock grazing (Wallin et al. 1996; Delong and Tanner 1996). Although 2,000 years from present encapsulates a time period during which temperate climates, ecosystems, and their disturbance regimes were relatively stable (Hann et al. 1997; FSC-BC Standards Team 2001), the Little Ice Age (1450-1850) has been documented in BC (Smith and Laroque 1996) and estimating RONV within its context is challenging (Miller and Woolfenden 1999).
Unfortunately, the temporal scale will most often be limited by the availability of data. For most disturbance regimes, very little evidence extends back for 2,000 years in time. For example, most fire history studies in the interior dry Douglas-fir and Ponderosa pine forests are based on the record of fire-scarred trees that does not extend back further than the 15th century and most periods of reliability begin from the early 1700s onwards (e.g., Gray et al. 1998, Riccius 1998, Wong 2000, Heyerdahl and Lertzman unpublished). Disturbance history is also difficult to reconstruct in the coastal forests in southern BC because much of it has been logged. The use of simulation modeling (i.e., repeated projections through long time periods) and substituting space for time in sampling (i.e., sampling many stands to capture the variability in stand development that may occur throughout time in one stand; Lertzman et al. 1998) may allow us to overcome some of the limitations of the historical record.
The relevant spatial scale of analysis depends fundamentally on the scale at which the attribute in consideration functions, as well as on the relevant management scale. For example, if RONV in landscape pattern is estimated from simulated disturbance regimes, the model extent (i.e., the size of a simulated landscape) should be at least 10 times larger than the largest expected disturbance (Cumming and Burton 1996). In general, at least a landscape scale is necessary for setting retention targets for landscape pattern and other goals for forest management and health, according to the RONV concept (Dellasalla et al. 1996).
Moore et al. (1994) provide more specific direction by recommending that RONV be assessed over areas that are consistent in terms of their edaphic, topographic, and bio-geographic conditions. For example, Gavin (2000) found the time-since-fire varied greatly with topography in Clayoquot Sound − the median time-since-fire was significantly six times greater on terraces (ca. 4,500 years) than hill slopes (ca. 750 years). Thus, there are specific “terrain units” or “process domains” (Montgomery 1999) – physical units over which disturbance regimes act relatively uniformly – which can form the spatial resolution of RONV estimates; i.e., RONV estimates on terraces should guide management on terraces and RONV estimates on hill slopes should guide management on hill slopes. This is a clear example of where averages over larger areas are unlikely to be ecologically meaningful without consideration of process domains (Lertzman et al. 1998). Checklist for managers to evaluate how RONV was determined In most areas in BC, data on RONV are scarce or absent. This is due to a lack of research, lack of remaining evidence of past structure and function and/or lack of methods (e.g., spatially explicit simulation models) to derive RONV. Moreover, even where data are present, they may only represent the portion of RONV they were able to capture, rather than the entire range of variability. The term ”apparent range of variability” has been suggested to convey the dependence of the definition on the temporal and spatial extent of available data and information (Figure 1; Wong 1999, Wong and Iverson 2001) – e.g., the fire history of an area could be that apparent from 200- 400 years of tree ring records sampled over 100 ha on a north-facing aspect. Forest managers and auditors will often be faced with evaluating whether the temporal and spatial scales over which the RONV was determined are adequate. For example, the field testing for Draft 2 of the FSC Regional Certification Standards for British Columbia indicated that simpler guidance in the RONV concept is required for auditors to consistently use RONV as a verifier (Moore 2001).
Below we suggest a checklist for managers and auditors to determine whether RONV has been adequately determined. When evaluating RONV for stand- and landscape-level attributes, was RONV estimated by:
1. Using data from stands or landscape with sufficient pre-settlement structure?
2. Sampling stands or landscapes representative of pre-settlement conditions? For instance, remaining old-growth stands are often a function of previous logging patterns and may not represent the entire range of pre-settlement stand conditions.
3. Sampling stands or landscape representative of the operating area? Or, if landscape pattern was simulated, was RONV estimated by using locally determined disturbance parameters for the spatially explicit model and simulating a landscape similar in topography and vegetation as the operating area?
4. Sampling at a resolution appropriate for describing the variation of the attribute in question?
5. Methods that account for post-settlement changes? For example, stand reconstruction of logged areas must examine whether patterns of stand development in a given area are “natural” or due to fire exclusion from fire suppression and fuel reduction from grazing.
6. Considering temporal and spatial trends in the data?
7. Considering the uncertainty and assumptions in data by using sensitivity analyses? This includes age errors in the Forest Inventory Planning data, assumptions about the original composition and structure of logged stands, assumptions about the role of insects.
8. Using appropriate methods for determining RONV? For example, fire return intervals determined from fire scars must be from cross-dated samples whereas fire return intervals determined from time-since-fire maps (in BC, most studies have used the forest cover maps as proxies) require different assumptions depending on the method used (see review in Wong et al. 2002).
9. Defining clearly, consistently and a priori what part of the distribution will define the “range”?
10. Using a process that included independent peer-review?
How is RONV used in current forest management?
At the time of writing, the concept of RONV is incorporated into forest management in British Columbia (BC) through the Biodiversity Guidebook of the Forest Practices Code (B.C. Ministry of Forests and B.C. Ministry of Environment, Lands, and Parks 1995). The Biodiversity Guidebook classifies BC into five different natural disturbance types, based on BC’s established system of ecosystem classification. From this classification flow management recommendations for targets of landscape-level attributes such as the distribution of seral stages and connectivity, as well as targets for stand-level retention of forest structure and wildlife trees. Unfortunately, most of these recommended targets are not based on empirical data of natural disturbance regimes – rather, they use informed professional judgment and “represent an attempt to integrate society’s desire both to generate commercial forest products and to ensure the conservation of biological diversity in managed forests” (B.C. Ministry of Forests and B.C. Ministry of Environment, Lands, and Parks 1995). In some cases, the targets are inconsistent with recent research on natural disturbance regimes. For example, based on a mean return interval for disturbances of 250 years, the Guidebook recommends a target for less than 30 percent of a given forest landscape in the coastal rainforest to be in early seral stage; this contrasts with findings that coastal rainforests are dominated by old-growth and have mean disturbance return intervals between ca. 750 and 6,000 years (Gavin 2000).
General recommendations for appropriate use of RONV
1. Require that data sources and scales of analyses used to estimate RONV and any resulting guidelines for management be explicitly stated – this allows certifiers and other interested parties to make their own judgement about the scientific validity of the RONV assessments used to justify a given management prescription.
2. Use a per-cautionary approach to the interpretation of RONV.
a) Avoid setting levels of retention for stand structural attributes and landscape pattern based solely on estimates of the mathematical range. The minimum or maximum value of given attributes over long-time scales are typically defined by rare, extreme events (Landres et al. 1999). Moreover, uncertainty about ecosystem attributes is typically highest at their spatial and temporal limits (Landres et al. 1999) – i.e., the sampled record “fades” in quality the further back in time. Focus instead on the distributions around the central tendency for the attribute of interest, e.g., values within the 90 percent confidence intervals or the 80 percent percentile of the distribution, as well as the shape of the curve that describes the attribute, e.g., normal, negative exponential, etc (Figure 1).
b) Where large uncertainty exists in estimates of RONV, resulting descriptions should err on the side of favoring the persistence of attributes of conservation concern. For example, landscape-level simulations of landscape pattern in the Arrow Timber Supply Area were greatly affected by assumptions about what age stands were before harvest (Dorner and Lertzman 2001). Disturbance intervals – the average time between disturbances − were shorter if it were assumed that logging historically targeted all stands greater than 120 years equally than if it were assumed that logging more likely targeted older stands (Dorner and Lertzman 2001). In this case, if late successional forests are of conservation concern, it is necessary to use the second assumption to derive RONV estimates of disturbance intervals in the Arrow TSA.
3. Make explicit that watershed- or landscape-level analyses are necessary to quantify RONV for structures and patterns that occur at this scale and that extrapolating information determined in a different location but in a similar bio-geo climatic sub-zone/variant is not an adequate estimate of local RONV (although it may do in the interim in the absence of data). Process domains are likely better units upon which to base RONV estimates (Cissel et al. 1999; Montgomery 1999).
4. Commit to long-term adaptive management – monitor how well various management activities maintain the RONV in specific attributes and affect biodiversity at the stand and landscape levels, as well as the capacity of ecosystems to resist and be resilient to disturbances.
5. Recognize that determining RONV for stand and landscape level attributes can be an intensive and expensive process posing specific challenges for small forest management operations. Innovative ways to overcome this challenge should be considered in future discussions on RONV. Where there is an absence of data, an interim definition of RONV can be based on inferences from similar forests or from expert opinion from local forest ecologists (Swetnam et al. 1999).
RONV is essentially a “coarse-filter” approach to sustainable forest management, aimed at conserving structural and biological diversity through maintaining forest heterogeneity. It, however, is not an easy concept to operationalize because it has various expressions at different temporal and spatial scales. Judicious use of the RONV concept requires its careful definition both temporally and spatially, careful sampling/modeling, and consistent and precautionary interpretation in forestry and land use planning.
C. Ronnie Drever, when he wrote this article, was with the David Suzuki Foundation, Suite 219, 2211 West 4th Ave., Vancouver BC V6K 4S2 t: 604-732- 4228
Carmen Wong, Independent Consultant, 651 Commonage Rd., Vernon, BC, Canada, V1H 1G3 t: 250-558-5292
 Other terms used to describe the same or a similar concept include: “historic or historical range of variability,” “natural range of variation,” “reference variability” or simply “natural variability or variation.”
 A period of time with sufficient replication in the tree-ring record that fire regimes can be confidently reconstructed.  Ring widths on fire-scarred samples are cross-dated to master chronologies of climatic patterns to obtain annual accuracy of fire dates.
Helpful comments on this paper were provided by Brian Starzomski.
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