Assessing Forest Naturalness

Assessing Forest Naturalness

Ronald E. McRoberts, Susanne Winter, Gherardo Chirici, and Elizabeth LaPoint

Abstract: The concept of naturalness has been proposed and used for describing the ecological value of forestecosystems, evaluating management efforts to conserve biodiversity, and identifying natural, old-growth forestsfor purposes of establishing protection areas. Because the concept is not globally familiar, a literature review wasconducted to document the ecological basis for the concept. In addition, the necessity for harmonized reportingmotivated an investigation of variables that can be used to quantify and assess forest naturalness. National forestinventories (NFIs) are sources of the most comprehensive and extensive data available for assessing naturalness.However, the variety of NFI plot configurations, sampling designs, definitions, and measurement protocolsgreatly impedes the utility of NFI data for purposes of producing compatible estimates. As a component of apan-European harmonization project, a three-phase investigation of harmonized approaches to assessing forestnaturalness using NFI variables was conducted. Although the project was primarily European in scope, forestinventory data for an American ecoprovince were used because of their availability, comprehensiveness, andconsistency. The primary result is that NFI features such as minimum dbh and plot size affect estimates ofindicators of naturalness, and accommodation of their differences may be necessary if harmonized estimates ofindicators of naturalness across countries are required. FOR. SCI. 58(3):294–309.

Keywords: harmonization, biodiversity indicators, hemeroby

THE CONCEPT OF FOREST NATURALNESS relates to thedegree to which forest ecosystems are characterizedby natural processes and/or the absence of human

influence. Although the concept is increasing in importancein some parts of the world, it is almost completely unfamil-iar in other parts of the world. Thus, a review of thenaturalness concept and an investigation of methods forharmonized estimation of indicators of naturalness iswarranted.

The overall objectives of the study were twofold: toreview the forest naturalness concept by documenting theliterature on the topic, describing the ecological devel-opment of the concept, and addressing the increasingimportance of harmonized estimation of indicators ofnaturalness and to investigate methods for estimatingharmonized indicators of forest naturalness using dataobtained for national forest inventory (NFI) variables.The sections that follow immediately address the firstobjective. Specific intermediate objectives for a three-phase study to address the second objective are providedat the end of the Introduction section.

The Forest Naturalness ConceptEcological Integrity

Historically, the evolution and dispersal of species hasbeen thought to keep pace with environmental changes ona geological time scale (Pickett et al. 1992). However,recent opinions are that the pace of human influence onthe environment may fundamentally alter that relation-ship (Crumpacker 1998). In response, conservation biol-

ogy has emerged as the application of scientific princi-ples to address the effects of human disturbance onecological systems (Soule 1985). This view emphasizesmaintenance of the natural integrity of ecosystems and isguided by the principles that biotic diversity, ecologicalcomplexity, and succession are results of natural pro-cesses, are intrinsically good, and are valuable manage-ment objectives (Soule 1985, Angermeier and Karr 1994,Crumpacker 1998) and that naturally evolved ecosystemsare of greater value and quality than disturbed or artificialsystems (Liira et al. 2007).

The term ecological integrity has been used to ascribevalue to older, natural forest stands (Karr 1991, Woodley etal. 1993, Angermeier and Karr 1994, Ohlson et al. 1997,Angermeier 2000). The first reference to integrity in theecological sense may have been Aldo Leopold’s (1949)famous statement, “A thing is right when it tends to preservethe integrity, stability, and beauty of the biotic community.It is wrong when it tends otherwise.” More recently, Cairns(1977) defined integrity as maintenance of the communitystructure and function characteristic of a particular locale.Angermeier and Karr (1994) stated that integrity refers to asystem’s wholeness including the presence of all appropri-ate elements and occurrence of all processes at appropriaterates over spatiotemporal scales. Noss (1990a) asserted thatcommunities have integrity when they are dominated bynative species, are relatively stable, and show attributes ofhealing. Angermeier and Karr (1994) attributed the mostinfluential definition of biological integrity to Frey (1975),who stated that integrity is “the capability of supporting andmaintaining a balanced, integrated, adaptive community of

Manuscript received June 15, 2010; accepted June 1, 2011; published online May 3, 2012; http://dx.doi.org/10.5849/forsci.10-075.

Ronald E. McRoberts, US Forest Service, Northern Research Station, Forest Inventory and Analysis, 1992 Folwell Avenue, St. Paul, MN 55108—Phone:(651) 649-5174; [email protected]. Susanne Winter, Technische Universitat Dresden—[email protected]. Gherardo Chirici, Univer-sita degli Studi del Molise—[email protected]. Elizabeth LaPoint, US Forest Service, Northern Research Station—[email protected].

This article was written by U.S. Government employees and is therefore in the public domain.

294 Forest Science 58(3) 2012

organisms having a species composition, diversity, andfunctional organization comparable to that of natural habitatof the region.”

Naturalness

Angermeier (2000) asserted that the foundation of eco-logical integrity lies in the concept of naturalness. Defini-tions of naturalness vary (Saudyte et al. 2005), but all relateto a continuum with entirely natural and entirely artificial atthe extremes (Angermeier 2000). With respect to forests,terms used to describe regions of the continuum, progress-ing from natural to artificial, include primeval, pristine,virgin, or undisturbed (Lindenmayer and Franklin 1997,Angermeier 2000, Saudyte et al. 2005, Liira et al. 2007,Ministerial Conference on the Protection of Forests in Eu-rope [MCPFE] 2007), which refer to forests that haveevolved without any human intervention and that havestructures encompassing all stages and dynamics of theforest life cycle; near-natural, which refers to naturallyregenerated forests composed of native tree species thathave been managed in the past but have now experienced arelatively long period of minimal human interference(Mountford 2002); intact, which indicates the presence ofall the critical ecosystem components and structure and withprocesses functioning within natural limits (Anderson1991); semi-natural, which indicates forest developmentinfluenced by human activities using ecological principleswith the result that species composition and forest structureare similar to those of the natural forest (Hansen et al. 1991,Saudyte et al. 2005, MCPFE 2007); conventionally man-aged, which indicates levels of intense management (Liiraet al. 2007); and plantations of either native or introducedspecies. The thresholds separating these regions are nebu-lous and assignment of a forest to a particular region of thecontinuum depends on the evaluation approach and localconditions. For this study, a paraphrase of S. Winter’s(manuscript in preparation, 2011) definition of naturalness,whereby an ecosystem is characterized as more natural if itscurrent state is more similar to its expected natural state, isused.

The concept of naturalness has been proposed and usedfor multiple forest-related purposes. First, naturalness isused to describe the ecological value of forest ecosystems(Usher 1986, Lane and Tait 1990, Crumpacker 1998,Saudyte et al. 2005) so that planning and managementpractices can be applied to maintain and/or increase thosevalues (Smith and Theberge 1987). Second, because of itsstrong linkage to biological integrity, naturalness is used asa basis for judging management efforts to maintain andconserve biodiversity (Harris 1984, Hansen et al. 1991,Hoerr 1993, Angermeier and Karr 1994, Norton 1996,Lahde et al. 1999, Trass et al. 1999, Angermeier 2000,Norden and Appelqvist 2001, Bartha et al. 2006, Liira et al.2007). Third, naturalness is used to identify natural, old-growth forests for purposes of establishing protection areas(Smith and Theberge 1987, Gustafsson 2002, Uotila et al.2002, Branquart and Latham 2007). Two specific studiesillustrate these uses. The Biodiversity Evaluation and As-sessment Research (BEAR) project, subtitled “Indicators

for Monitoring and Evaluation of Forest Biodiversity inEurope,” focused on developing tools for evaluating forestbiodiversity and included measures related to naturalnessamong the key factors (Larsson et al. 2001). An assessmentby the MCPFE (2003) of the current state of Europeanforests relative to the criteria and indicators of sustainableforest management used areas and percentages of semi-nat-ural and undisturbed forests as measures of the degree towhich forest biodiversity was maintained and conserved(MCPFE 2007, Criterion 4).

Two perspectives on naturalness serve as bases for ap-proximately complementary approaches to its assessment.The first approach is based on an assessment of ecosystemprocesses (Peterken 1996). The advantage of this approachis that the assessment focuses on the subject of primaryconcern, the ecosystem (Forest Resources Association[FRA] 2000). The disadvantages are the difficulties in de-fining and measuring parameters that relate to ecosystemprocesses and that can be evaluated in a globally consistentmanner at broad geographical scales. The second approachis based on the degree of human influence (Rolston 1990,Anderson 1991) and focuses on human activity as the driverof ecosystem disturbance. Jalas (1955) introduced the termhemeroby, from the Greek hemeros meaning cultivated,tamed, or refined, as a measure of human impact on eco-systems. Sukopp et al. (1990) and Kowarik (1990) charac-terized hemeroby as a measure that integrates the effects ofpast and present human influence on ecosystems. Althoughhemeroby has been used mostly in Central Europe (Kowarik1990, Sukopp et al. 1990, Grabherr et al. 1995, Kim et al.2002), the idea was also used by the Global Forest Re-sources Assessment (FRA 2000), which defined naturalnessas “lack of anthropogenic disturbance.”

Although no single variable has been found adequate forassessing all aspects of forest naturalness, assessment of allvariables is not only impossible but also would not producepolicy-relevant information for nonexperts (Branquart andLatham 2007). Thus, a small set of appropriate indicators isrequired where an indicator is defined as a feature of abiological system whose occurrence contributes to assessingthe most important aspects of forest biodiversity (Larsson etal. 2001). For the hemeroby approach, proposed indicatorsprimarily relate to evidence of human activity and includethe number of cut stumps (FRA 2000, Uotila et al. 2002,Saudyte et al. 2005), signs of slash and burn (Uotila et al.2002), absence of deadwood, growing stock volume (Uotilaet al. 2002), and signs of silvicultural management such assite preparation, artificial regeneration, and thinning. Forthe ecosystem approach, variables that have been proposedinclude numbers of old and large trees (McComb et al.1993, Gustafsson 2002, Nilsson et al. 2002, von Oheimb etal. 2005), number of canopy or height layers (McComb etal. 1993, Uotila et al. 2002), the presence and/or amount ofdeadwood (McComb et al. 1993, FRA 2000, Gustafsson2002, Nilsson et al. 2002, Christensen et al. 2005, Liira et al.2007), shapes of diameter distributions (Koop and Hilgen1987, Buongiorno et al. 1994, Kuuluvainen et al. 1996,1998, Linder et al. 1997, Lahde et al. 1999, Maltamo et al.2000, Uotila et al. 2002, Siipilehto and Siitonen 2004, von

Forest Science 58(3) 2012 295

Oheimb et al. 2005, Westphal et al. 2006), species compo-sition (Gustafsson and Hallingback 1988, McComb et al.1993, Cochrane and Schulze 1999, FRA 2000, Saudyte etal. 2005, Bartha et al. 2006), number of microhabitats(Winter and Moller 2008), growing stock volume (Kuulu-vainen et al. 1998, Uotila et al. 2002), and forest structuraldiversity (Hansen et al. 1991, Burschel 1992, McComb etal. 1993, Buongiorno et al. 1994, Larsen 1995, Kuuluvainenet al. 1996, Peterken 1996, Lahde et al. 1999, McComb andLindenmayer 1999, Uotila et al. 2002, Winter et al. 2010).

Multiple criteria have been proposed for determining theutility of indicators of naturalness. Useful indicators mustdistinguish between natural and anthropogenic features(Hardt and Swank 1997, Lindenmayer and Franklin 1997,Nowacki and Abrams 1997, Angermeier 2000), they mustbe reliable in the sense that they are objective (Uotila et al.2002) and produce consistent and precise results (Smith andTheberge 1987, Liira et al. 2007), they must be readilycalculable from available data (Bartha et al. 2006, Liira etal. 2007), they must be sensitive to a large range of effects(Angermeier and Karr 1994), and they must be appropriateat multiple spatial scales (Bartha et al. 2006).

The national forest inventories (NFIs) conducted in Eu-rope and North America and increasingly in Asia and South

America are sources of the most comprehensive and exten-sive data that can be used to assess forest naturalness.Although the assessment of naturalness is much more com-mon in Europe than elsewhere, even in Europe, consistentobjective multicountry assessments are impeded by fourfactors. First, not all countries assess naturalness as part oftheir NFIs (Figure 1); of the 19 European countries submit-ting data as part of a European harmonization project(COST E43 2007), only 11 countries assess naturalness. Ingeneral, countries with the least amount of forest area char-acterized as natural or close to natural are the countries thatdo not assess naturalness. Second, among countries thatconduct such assessments, consistent data and commonapproaches are not used. Of the European countries whoseNFIs assess naturalness, the most commonly used criterionis signs of silvicultural management; other criteria includetree species composition and tree or stand age, and thepresence and/or amount of deadwood is also used (Figure2). This disparity among approaches to assessing natural-ness can be partially attributed to differences among NFIswith respect to variables assessed, definitions of those vari-ables, sampling designs, plot configurations, measurementprotocols, and analytical methods. Third, few forested areasin either Europe or North America remain that could be

Figure 1. European countries whose NFIs assess naturalness.


construed as natural. Branquart and Latham (2007) reportedthat ratios of the areas of nearly natural forest to the totalareas of forest and other wooded lands for Europe are 0.001for Western Europe (Belgium, Denmark, France, Nether-lands, and the United Kingdom), 0.013 for Southern Europe(Cyprus, Greece, Italy, Portugal, and Spain), 0.025 for Cen-tral Europe (Austria, Bulgaria, Czech Republic, Lithuania,Macedonia, Romania, Slovenia, and Switzerland), and0.083 for Northern Europe (Finland, Norway, and Sweden).They attributed the greater ratio for Northern Europe to thechronologically later onset of human influence. TheMPCFE (2007, Criterion 4), reported that the ratio forundisturbed forest in Europe in its entirety, excluding theRussian Federation, is nearly 0.05 with half the undisturbedarea in Sweden. Fourth, protected or reserved areas whoseforest attributes might be considered references for natural-ness comparisons are often located on relatively unproduc-tive sites that may have low natural levels of biodiversity(Recher 1996) and may lack important forest plant andanimal species (Lindenmayer and Franklin 1997).

Reports in the literature of attempts to construct objec-tive, quantitative indices of forest naturalness are few, andthose reported are specifically adapted to individual cases(Machado 2004). Although European NFIs base their as-sessments on observations of a variety of variables, theassessments are often visual or subjective. Scholes andBiggs (2005) proposed a biodiversity intactness index, but itis still subjective because it relies heavily on expert judg-ments. Petriccione (2006) proposed a more quantitativeapproach based on comparing observed and potential veg-etation types. If the two types are different, a naturalnessvalue of 0.0 is assigned; if they are the same, then thenaturalness value is based on the average of six indicescharacterizing disturbance and species types, richness, and

coverage. For beech forests in Germany, Winter (2006)reported three indices with average thresholds for distin-guishing managed and near-natural forest: 1.8 livingtrees/ha with dbh �80 cm, albeit with diminished vitality;more than half the standing and fallen deadwood pieceswith length �10 m; and 3 trees/ha with dbh �60 cm andwith the fungus, Fomes fomentarious. Winter et al. (2010)presented an assessment approach using a metric based onordinal and presence/absence data for numerous ecologi-cally relevant variables rather than for a minimum set ofvariables.

Despite the difficulty in assessing naturalness, interna-tional interest in silvicultural practices that mimic naturalforest ecosystem processes has increased in recent years(Lahde et al. 1999). In most European countries, a close-to-nature silvicultural approach is becoming the norm (vonOheimb et al. 2005, Ciancio et al. 2006), and forest ecosys-tems with a high degree of naturalness are becoming theforest management standard for comparison (Peterken1996, Lahde et al. 1999). In addition, European countrieshave committed to implementing guidelines for sustainableforest management that conserve biodiversity and promotenaturalness (Loiskekoski et al. 1993, Ratcliffe and Peterken1995).

International Reporting

The increased interest in forest biodiversity and its es-sential features, including naturalness, have led to numerousinternational agreements that include reporting require-ments. The Convention on Biological Diversity (2007) re-quires that countries identify and monitor components ofbiological diversity for purposes of conservation and sus-tainable use. The primary forestry sustainability conven-tions, Forest Europe, formerly the MCPFE (2003), and theMontreal Process (2005) require that member countriesreport on sustainability and biodiversity indicators. Withinthis international reporting context, McRoberts et al. (2009)documented recent increased attention to harmonized re-porting. Monitoring guidelines developed by the Intergov-ernmental Panel on Climate Change (IPCC) addressed is-sues of transparent and harmonized reporting (IPCC 1997).The Good Practice Guidance (GPG) (IPCC 2003, Egglestonet al. 2006) for Land Use, Land Use Change and Forestry-(LULUCF) provided detailed guidance for some aspects ofreporting. The United Nations Economic Commission forEurope (UNECE) and the Food and Agriculture Organiza-tion (FAO) of the United Nations jointly compiled theTemperate and Boreal Forest Resources Assessment (TB-FRA) covering the forest resources of North America, Eu-rope, the Commonwealth of Independent States, Australia,New Zealand, and Japan. For the TBFRA 2000 assessment,the UNECE/FAO team compiled international definitionsand then harmonized national data to conform to thesedefinitions (UNECE and FAO 2000).

Kohl et al. (2000) described two approaches that can beused to increase the consistency of forest resource estimatesamong countries. The first, standardization, is a top-downapproach that requires a common system of nomenclatureand focuses on common standards with respect to NFI

Figure 2. Criteria used by European NFIs to assess natural-ness.


definitions and methods. Although standardization of NFIswould produce the most direct route to compatible esti-mates, other factors must be considered. For example, NFIfeatures such as sampling designs and plot configurationsfor individual countries have been developed over time toaccommodate their unique topographies, climates, foresttypes, and economic conditions. Thus, standardization isoften not a realistic option. The second approach, harmoni-zation, is based on the assumption that the unique featuresof individual NFIs are justified and are unlikely to bechanged. Harmonization, therefore, focuses on developingmethods for producing comparable and compatible forestattribute estimates despite the lack of standardization. Incontrast to standardization, harmonization is a bottom-upapproach that begins in divergence and ends in comparabil-ity (Kohl et al. 2000).

The most comprehensive effort directed toward harmo-nization of estimates of forest biodiversity using NFI datafor international reporting was conducted by COST ActionE43, Harmonisation of National Forest Inventories in Eu-rope: Techniques for Common Reporting (COST E43 2007,Tomppo et al. 2010, Chirici et al. 2011, Tomppo et al.2012). Within COST Action E43, a primary task of Work-ing Group 3 (WG3) was to evaluate the utility of NFIvariables and field methods for estimating components ofbiodiversity and to develop methods for harmonizing esti-mates (Chirici et al. 2011). The WG3 investigations ofnaturalness focused on investigating and recommendingquantitative methods using NFI data for characterizing andidentifying natural forest data and for producing harmo-nized estimates of indicators of naturalness.

The immediately preceding sections document the cur-rent understanding of the naturalness component of biodi-versity and methods for its assessment as reported in theliterature, thus achieving the first overall objective. Theprimary conclusions are that the naturalness concept is notwell defined in the sense that no rigorous definition hasbeen generally accepted and that no completely objectivemethod for quantifying naturalness is available. Thus, anunderlying objective implicit in any study of naturalnessmust be to motivate additional study to increase the maturityof the naturalness concept and methods for its assessment.

Harmonized Estimation of NaturalnessIndicators

A three-phase study was designed to achieve the secondoverall objective, investigation of methods for estimatingharmonized indicators of forest naturalness using data ob-tained for NFI variables. The investigations were sequentialin the sense that results obtained from a previous phase wereused to design analyses for subsequent phases. In the firstphase, NFI variables proposed in the literature for construct-ing indicators of naturalness were analyzed with respect tothe number of countries participating in COST Action E43that measure the variables and with respect to the features ofthe variables that make them desirable or undesirable asindicators. The first-phase objective was to select a subset ofthe variables proposed in the literature that are assessed byNFIs and that satisfy indicator criteria. In the second phase,

indicators based on NFI variables selected from the firstphase were evaluated with respect to their potential forharmonized estimation, particularly their sensitivity to NFIsampling features such as minimum dbh and plot size,which differ considerably among the NFIs of differentcountries. The second-phase objective was to determine thedegree to which NFI variables selected from the first phasecould readily be harmonized for use in a common approachfor assessing naturalness. In the third phase, a multidimen-sional approach based on NFI variables satisfying the first-and second-phase criteria was investigated for estimatingthe degree to which conditions observed on NFI plots cor-respond to more natural forest. The third-phase objectivewas to assess the utility of the multidimensional approach.A consequence of the sequential nature of the analyses isthat the organization of the remaining sections of this reportdeviates from the traditional data-methods-results sequence.Instead, following the Data section, methods and results arereported sequentially for the respective phases.

DataStudy Area

The study area is the portion of Bailey’s (1976) ecolog-ical province 212 in the northcentral region of the UnitedStates (Figure 3). The study area extends approximately 700km north to south and approximately 775 km east to westand includes 265,000 km2. Most of the province has lowrelief with rolling hills, lakes, poorly drained depressions,and glacial features. Elevations above sea level are mostlyin the range of 175 to 550 m, mean annual precipitationranges from 560 mm in the northwest to 850 mm in thesoutheast (National Oceanic and Atmospheric Administra-tion 1974), mean minimum winter temperature ranges from�20 °C in the northwest to �9 °C in the southeast, andmean maximum summer temperature ranges from 21 °Calong the Great Lakes on the northern boundary of theprovince to 28 °C in the southeast. The province lies be-tween boreal and broadleaf deciduous forest zones withsoils that vary greatly and include peat, muck, marl, clay,silt, sand, gravel, and boulders in various combinations. Theforests in ecological province 212 were first harvested byEuropean descendants in the mid to late 1800s with earlierdates in the east and later dates in the west. Tree species inthe province generally have rapid early growth and reacheconomic maturity in approximately 50 years. Thus, theprovince is experiencing its third and fourth cycle of forestharvest.

Forest Inventory Data

The Forest Inventory and Analysis (FIA) program of theUS Forest Service conducts the NFI of the United States.The FIA program has established field plot centers in per-manent locations across the country using a sampling designthat is assumed to produce a random, equal probabilitysample (Bechtold and Patterson 2005, McRoberts et al.2005). The sampling design is based on a tessellation of theUnited States into 2,400-ha (6,000-ac) hexagons and fea-tures a permanent plot at a randomly selected location


within each hexagon. The average distance between plots isapproximately 5 km, and over a complete 5-year measure-ment cycle, the sampling intensity is approximately 1plot/2,400 ha. Each plot consists of four 7.31-m (24-ft)radius circular subplots for a total area of 672 m2. Thesubplots are configured as a central subplot and three pe-ripheral subplots with centers located at 36.58 m (120 ft)and azimuths of 0, 120, and 240° from the center of thecentral subplot.

FIA data were acquired for all plots on forestland inecological province 212 (Figure 3) that were wholly in theaspen forest type group. The FIA requirements for forest-land are at least 0.4 ha (1.0 ac) in size, at least 10% stocked(Husch et al. 2002), at least 36.58 m (120 ft) in crown-to-crown width, and use as forestland. Observations of species,height, and dbh were acquired for 82,059 trees on 5,771 NFIplots in the aspen forest type group and were augmentedwith distances from all trees to plot centers. FIA field crewsmeasure trees with 2.54 cm � dbh � 12.70 cm on 2.07-mradius subplots and trees with dbh � 12.7 cm on 7.31-mradius subplots. Trees of 74 species were observed withQuaking aspen (Populus tremuloides), Balsam fir (Abiesbalsamea), Bigtooth aspen (Populus grandidentata), Redmaple (Acer rubrum), and Paper birch (Betula papyrifera)being the most common species. Three species (Norwayspruce, [Picea abies], Scotch pine [Pinus sylvestris], andRussian olive [Elaeagnus angustifolia]) with 121 total ob-servations were species introduced from another continent,three species (Eastern redcedar [Juniperus virginiana],Black walnut [Juglans nigra], and Blackgum [Nyssa bi-flora]) with 27 total observations were species observed

beyond their original North American natural geographicrange, and one species (Ailanthus [Ailanthus altissima])with two observations was introduced from China and isnow considered invasive because of its rapid colonizationproperties. All plots were measured between October 1999and September 2004.

Phase 1Methods

Based on the literature review and the survey of Euro-pean NFIs conducted by COST Action E43, 10 categories ofvariables with potential as indicators of forest naturalnesswere considered. The variables were further categorizedwith respect to the assessment approach, hemeroby or eco-system processes, for which they are used (Figure 4). Twocategories of variables relate primarily to the hemerobyapproach: (1) signs of harvest such as cut stumps and slash,and (2) signs of silvicultural management such as site prep-aration, artificial regeneration, and thinning. Six additionalcategories of variables relate primarily to the ecosystemprocesses approach: (3) number of microhabitats, (4) spe-cies diversity including native versus non-native species andspecies mixes, (5) tree age, (6) numbers of large trees, (7)vertical structural diversity, and (8) horizontal structuraldiversity. Two additional categories, (9) growing stock vol-ume and (10) deadwood, have been proposed for both thehemeroby and ecosystem processes approaches. For thelatter two categories, greater relative growing stock volumehas been identified as a characteristic of managed stands,whereas the presence of deadwood indicates more natural

Figure 3. Bailey’s (1976) ecoprovince 212 (black) where the NFI plots of the aspen forest type group arelocated.


forests because absence indicates either removal or har-vest before death, both of which characterize humaninfluence.

Of the variables comprising these categories, several areless than ideal as indicators of narturalness. Amonghemeroby variables, signs of harvest and signs of silvicul-tural management only partially satisfy the reliability crite-rion. Although these signs clearly indicate human influence,absence of them does not necessarily indicate lack of humaninfluence (Anderson 1991). With the possible exception ofstands of short-lived species, the time necessary for stumpsand slash to disappear is much less than the time necessaryfor the effects of human influence to disappear from theecosystem. von Oheimb et al. (2005) asserted that severalgenerations, possibly comprising hundreds of years, arenecessary for a beech forest with an initial highly differen-tiated age structure to exhibit characteristics of virgin for-ests. Rademacher and Winter (2003) conducted 5,000-yearsimulations of natural processes in beech forests for aninitial stand structure reflecting current management prac-tices. They found that the influence of the initial standstructure persisted in the ecosystem for as long as 2,000years, an extremely long interval for a species whose naturalmaximum lifespan is approximately 450 years.

Multiple variables associated with the ecosystem pro-cesses approach are also less than ideal as indicators ofnaturalness when they are used with NFI data. First, thenumber of microhabitats is measured by the NFI of only onecountry participating in COST Action E43. Second, becausetree age is difficult to determine accurately for many speciesand because tree age is highly correlated with both dbh andheight, it can be eliminated in favor of tree size (Lahde et al.1999). Third, comparisons of degree of naturalness based onthe amount of growing stock volume are unreliable becauseof the effects of site, climate, and phenology on plot-levelgrowth dynamics. Further, whereas Kuuluvainen et al.

(1998) and Uotila et al. (2002) report less volume on un-managed stands in Finland and northeastern Europe, Meyeret al. (2003) found greater volume on unmanaged Albanianbeech (Fagus sylvatica) stands. Although regional differ-ences are to be expected, inconsistency in general trendsmakes a variable less desirable for use as an indicator whoseestimates are to be harmonized across regions.

The remaining variables all relate to either deadwood orforest structural diversity. Although the presence and/oramount of deadwood is regarded as an important feature ofmore natural forests, few reports of specific amounts re-quired for a forest to be characterized as more natural or lessnatural have been published. Some researchers simply referto the presence of either deadwood in general or largeamounts of deadwood (Gustafsson 2002, Uotila et al. 2002,Liira et al. 2007), some refer to “large snags and logs anddeep forest litter” (McComb et al. 1993), and some are quitespecific, referring to proportions of standing trunks that aredead and proportions of dead trunks that are standing(Nilsson et al. 2002). Of the few reports of specific thresh-olds, Winter (2006) characterized near-natural beech forestas having more than half the standing and fallen deadwoodpieces with length �10 m. Nevertheless, von Oheimb et al.(2007) reported that information gained for some regionsand forest types do not transfer to other regions or foresttypes, that deadwood volumes in the virgin forests of Eu-rope vary greatly, and that climate, site productivity, andcompetition all produce considerable differences in dead-wood volume. This finding is consistent with the strongrecommendation by Larsson et al. (2001) that the manage-ment and assessment of all components of biodiversity beconducted by forest type using a classification such as thatproposed by the European Environment Agency (2006).Thus, despite its important contribution to naturalness(Laarmann et al. 2009), deadwood was not used in thisstudy because development of harmonization bridges wasbeyond the scope of the objectives. However, Chirici et al.(2011) reported a similar study that does use deadwood.

Species diversity, vertical structural diversity, and hori-zontal structural diversity together comprise forest struc-tural diversity (Pommerening 2002, Varga et al. 2005)which, in turn, constitutes one of three primary componentsof biological diversity (Noss 1990b). Fortunately, the pri-mary variables necessary to assess forest structural diversity(tree species, height, and dbh) are the most commonlyobserved NFI variables (Winter et al. 2008). Of these threetree-level variables, height may be less useful because it isdifficult to measure precisely, particularly in closed canopydeciduous forests, it is not measured on all trees by all NFIs,and model predictions for trees not measured are impreciseand may also be subject to model bias. However, heightmeasurements are usually obtained for the tallest trees, andsufficient measurements are usually available to determinenumbers of height layers, a measure of vertical structuraldiversity. Horizontal structural diversity is usually assessedusing estimates of parameters of dbh distributions, and treespecies diversity is frequently assessed using any of severalcommon indices.

Figure 4. Potential indicators.


Results and Discussion

The results of the phase 1 analyses are threefold. First,hemeroby variables may be useful as indicators of managedstands but not necessarily as indicators of more naturalstands. Second, although the presence of deadwood may bepotentially useful as an indicator of more natural forest, itsabsence is not necessarily useful as an indicator of lessnatural forest. In addition, quantitative thresholds foramounts of deadwood necessary to distinguish betweenmore natural and less natural forest are generally not avail-able. Further, even if they were available, they would beexpected to vary greatly with climate, topography, and otherfactors, even within the same forest type (Larsson et al.,2001). Third, for the ecosystem processes approach, theavailability of observations of tree species, height, and dbhhave the potential to make indicators related to species andvertical and horizontal diversity good candidates for assess-ing natural forest. Thus, tree species, height, and dbh satisfythe criteria for indicators and are commonly measured byNFIs, and their selection for further evaluation in phase 2constitutes satisfaction of the phase 1 objective.

Phase 2Methods

The phase 2 analyses focused on evaluating the effects oftwo NFI sampling features, minimum dbh and plot size, onpotential indicators for distinguishing less natural and morenatural forests. For the hemeroby approach to assessingforest naturalness, proposed indicators have been basedprimarily on dichotomous variables: presence/absence ofcut stumps, presence/absence of slash, presence/absence ofdeadwood, and presence/absence of signs of management.With the exception of deadwood, these variables are notsensitive to size thresholds such as minimum dbh. However,they are extremely sensitive to plot size; in particular, theprobabilities of detecting signs of human influence anddeadwood are proportional to the square of the plot radius.Therefore, their utility is limited when one is attempting toharmonize estimates of naturalness for NFIs that use sampleplots of different sizes and configurations.

For the ecosystem processes approach to assessing forestnaturalness, indicators related to species and vertical andhorizontal diversity were constructed using observations oftree species, height, and dbh. As a measure of tree speciesdiversity, the Shannon index is calculated as

H� � �j�1

nsp

pjln(pj),

where nsp is the number of species observed on a plot and pj

is the proportion of total plot basal area in trees of the jthspecies (Shannon 1949). For this study, pj was based onbasal area to circumvent problems with large numbers ofvery small trees (Staudhammer and LeMay 2001). TheShannon index is 0 when all trees are of the same species,increases when more species are observed, and is maxi-mized for a given number of species when the proportionsare equal. The Shannon index has been used to estimate

species diversity for diptocarp and nondiptocarp stands inIndonesia (Onal 1997): P. sylvestris-dominated forests inFinland (Kuuluvainen et al. 1998); montane broadleaf-conifer- and conifer-dominated stands in Austria (Neumannand Starlinger 2001); and single- and mixed-species coni-fers in western Canada (Varga et al. 2005). H� has beenselected as a measure of species diversity by WG3 of COSTAction E43 and has been used to assess tree species diver-sity for a large region of the United States (McRoberts et al.2008).

Vertical structural diversity was assessed using numbersof layers and numbers of tall trees. For each NFI plot, heightlayering was assessed in four steps: (1) the mean height ofthe proportion ptall � 0.005 tallest trees represented in thedataset was determined, where ptall � 0.005 is acknow-ledged to be an arbitrary choice; (2) three height layers,which is the most commonly used number by NFIs (Chiriciet al., 2011), were defined by dividing the mean height ofthe ptall tallest trees into thirds; (3) each tree on each NFIplot was assigned to a height layer; and (4) the specificheight layers represented on each plot were determined.More natural forests are expected to include multiple heightlayers; thus, a plot was characterized as satisfying the LAY-ERS criterion if it included trees in multiple height layers ofwhich one was the tallest layer. In addition, an NFI plot wascharacterized as satisfying the TALL criterion if it includedat least one tree among the ptall tallest trees.

Measures of horizontal structural diversity were based ontwo parameters of dbh distributions, SD and skewness. TheSD, �, of the distribution of dbh observations on a plot isestimated as

sdbh �1

n � 1��i�1

n

(dbhi � dbh)2 ,

where i indexes trees measured on the plot, n is the numberof trees measured, and dbh is the mean dbh of measuredtrees. The advantages of sdbh are that it is easy to calculateand easy to interpret. Minimum values of sdbh are realizedwhen all dbh observations are similar, whereas maximumvalues are realized when half the observations are at thelower extreme and half are at the upper extreme. In addition,sdbh has been selected as a measure of forest structuraldiversity by WG3 of COST Action E43 and has been usedto assess horizontal structural diversity for a large region ofthe United States (McRoberts et al. 2008).

Diameter distributions for unmanaged or natural foreststands are often characterized as having a reverse J-shape,i.e., large numbers of small diameter observations to the leftof the mean and smaller numbers of trees with large, scat-tered diameter observations to the right of the mean (Koopand Hilgen 1987, Korpel 1995, Kuuluvainen et al. 1996,Linder et al. 1997, Uotila et al., 2002, Siipilehto and Sii-tonen 2004, von Oheimb et al. 2005, Westphal et al. 2006).The essence of this characteristic is a positive skewness inthe diameter distribution with an extended upper tail. Acommon and easily computed measure of the skewness of adistribution is its third moment which is defined as


� �1

n �i�1

n

(dbhi � �dbh)3,

where �dbh is mean plot dbh (Snedecor and Cochran 1967).Large, positive values of � indicate a reverse J-shapedistribution (Figure 5). For this study, the cube root of theestimate of skewness,

gdbh � �3 1

n �i�1

n

(dbhi � dbh)3 ,

where dbh is the mean dbh of measured trees, was used asa transformation of skewness in the same manner as SD isused as a transformation of variance.

The effects of minimum dbh and plot size on the LAY-ERS and TALL criteria, H�, sdbh, and gdbh were estimated.Each indicator was calculated for each plot using levels ofminimum dbh and plot radius in the ranges reported by NFIsto COST Action E43. In addition, for each indicator andeach combination of minimum dbh and plot radius, theeffects of the minimum number of trees per plot, ntree, wereevaluated.


For purposes of harmonized estimation of naturalness viaNFI variables, the utility of the three indicators, H�, sdbh,and gdbh, cannot be directly assessed because so little nat-ural forest exists for direct comparison purposes. Neverthe-less, the three indicators are based on variables reported inthe literature, satisfy indicator criteria, and are the mostcommonly assessed NFI variables. Thus, this indirect ap-proach is the best that can currently be achieved.

However, the results of the phase 2 analyses suggest thatharmonization of estimates of the indicators may be difficultapart from a common minimum dbh, a common plot size,

and a common ntree. H�, sdbh, gdbh, and satisfaction of theLAYERS and TALL criteria were all somewhat sensitive tominimum dbh, quite sensitive to plot radius, and oftensensitive to ntree (Figures 6A–D, 7A–D, and 8). The TALLcriterion does not depend directly on minimum dbh, al-though the latter affects the total number of trees observedand indirectly the number of trees represented by ptall.

Harmonization to accommodate differences in some NFIfeatures can be accomplished using bridges that convertestimates based on national definitions and features to es-timates that conform to common definitions and features(McRoberts et al. 2009, Ståhl et al. 2012). With reductivebridges, some data are simply discarded. For example, har-monization to accommodate differences in minimum dbhcan be accomplished by simply disregarding all trees withdbh less than a common dbh. Likewise, a requirement for acommon ntree can be satisfied by disregarding all plots withnumbers of trees less than ntree. However, a reductive bridgeto accommodate differences in plot size is more difficult. Inthis case, the essence of the bridge would be to disregard alltrees whose distances from plot centers are greater than thecommon minimum radius. Unfortunately, the bridge re-quires ancillary data in the form of distances from plotcenters to individual trees, information that is often notcollected or is not readily available from NFIs.

Pairwise correlations among H�, sdbh, and gdbh are gen-erally small, indicating that none of these three indicatorscan easily be replaced by one or a combination of the othertwo. In addition, although large values of sdbh and gdbh

suggest at least some large dbh observations, trees satisfyingthe TALL criterion were not necessarily found on plots withthe largest values of sdbh or gdbh. The relative independenceof the three indicators suggests that each addresses a differ-ent dimension of naturalness and that, when combined intoa single assessment approach, they may provide a morecomprehensive assessment than any of them separately.Thus, the phase 2 objective of determining the degree towhich variables selected in phase 1 can be harmonized foruse in a common approach for assessing naturalness hasbeen achieved, although the results could have been morepositive.

Phase 3Methods

The emphasis of the third phase was development of aprocedure for identifying plots representing conditions ex-pected to be characteristic of more natural forest. Becauseso few natural forest areas remain for use as references andbecause hemeroby may be difficult to recognize, the bestthat can be achieved is to identify plots with characteristicssimilar to those that would be expected for natural forest(Freedman 1989).

The procedure for identifying plots representing morenatural forests is based on a proposal by Loidi (1994) thatthe degree of naturalness can be expressed in terms of aconceptual distance between observed plot conditions andconditions regarded as not measurably influenced by hu-mans. Given the consensus from the literature that natural-ness has multiple components or dimensions and that the

Figure 5. Proportion of trees per ha versus dbh based onaggregated observations for the five plots with greatest skew-ness (gdbh) reveals reverse J-shape dbh distribution.


phase 2 analyses identified three relatively independentindicators, Loidi’s proposal was investigated by construct-ing a distance measure in the multidimensional space of theindicators from phase 2.

Before construction of the distance measure, plot-levelvalues of H�, sdbh, and gdbh were scaled to a [0-1] interval toweight them equally when used in combination. The scalingprocedure for H� consisted of three steps: step 1, the plotswere ordered with respect to H�; step 2, the mean of theproportion pH � 0.005 largest values was calculated, wherepH � 0.005 is acknowledged to be an arbitrary choice; andstep 3, for each plot, the ratio, RH, of H� and the mean of thepH largest values from step 2 was calculated with theconstraint that RH � 1. Similar procedures were used forsdbh and gdbh with ps � 0.005 and pg � 0.005 and Rs and Rg

defined in a similar manner. Plots with more natural forestconditions would be expected to have ratios closer to 1; i.e.,distances from (RH, Rs, Rg) to (1, 1, 1) would be smaller.

The procedure for identifying plots representing condi-tions expected to be characteristic of more natural forestconsisted of two primary steps, each with subordinate steps:

1. Eliminate specified classes of plots with less naturalforest:a. Eliminate plots with one or more trees of non-native

species;b. Eliminate plots that fail to satisfy the LAYERS

criterion;c. Eliminate plots that fail to satisfy the TALL criterion;d. Optionally, eliminate plots with signs of human in-

fluence such as thinning, harvest site preparation,human damage, non-natural regeneration, cut stumps,slash piles, and others;

e. Optionally, eliminate plots with no observeddeadwood.

2. For the remaining plots:

Figure 6. (A) Mean Shannon index (H�) versus minimum dbh for plot radius of 14.5 m. (B) Mean SD(sdbh) versus minimum dbh for plot radius of 14.5 m. (C) Mean skewness (gdbh) versus minimum dbh forplot radius of 14.5 m. (D) Proportion of all plots satisfying LAYERS criterion for plot radius of 14.5 mptall � 0.005.


a. Eliminate all remaining plots with fewer than ntree

trees to assure reliable values for H�, sdbh, and gdbh;b. Calculate the Euclidean distance, Dplot, from the

three-tuple (RH, Rs, Rg) to the three-tuple (1, 1, 1);small distances indicate that the ratios RH, Rs, Rg, arecloser to their maximum values, which representmore natural forest;

c. for selected distances, D, determine the percentage,pD, of all plots for which Dplot � D.

For this study, the optional steps 1d and 1e were not usedbecause deadwood information was available for only asmall percentage of plots and distances from plot centers tosigns of human influence were not available.


The cumulative effects of small values for ptall, pH, ps,and pg reflect the assumption that few plots may be foundthat represent natural forest. Further, pD was not particularlysensitive to values of ptall, pH, ps, and pg. The proportions of

the total number of plots remaining after step 1 ranged from0.100 to 0.175, depending on minimum dbh and plot radius.Of the remaining plots, none had Euclidean distance fromthe three-tuple (RH, Rs, Rg) to the three-tuple (1, 1, 1) lessthan 0.1, meaning that no plots had maximum naturalnessvalues for all three indicators. This result is consistent withthe previous finding that H�, sdbh, and gdbh were not highlycorrelated. Relationships between pD and D were affectedby plot radius more than by minimum dbh, although differ-ences were negligible for D � 0.40 (Figure 9). The specificplots identified as having the most natural forest conditionson the basis of values of Dplot were not the same fordifferent dbh minima, plot sizes, and choices for ntree (Table1). However, despite the effects of minimum dbh and plotsize on the selection of plots in step 1 and Dplot in step 2, thevalues of H�, sdbh, and gdbh for the plots with the smallestvalues of Dplot were similar, regardless of minimum dbhand plot size (Table 1). These generally favorable resultssuggest that the multidimensional approach for assessing

Figure 7. (A) Mean Shannon index (H�) versus plot radius for minimum dbh of 2.5 cm and pH � 0.005.(B) Mean SD (sdbh) versus plot radius for minimum dbh of 2.5 cm and ps � 0.005. (C) Mean skewness (gdbh)versus plot radius for minimum dbh of 2.5 cm and pg � 0.005. (D) Proportion of all plots satisfyingLAYERS criterion versus plot radius for minimum dbh of 2.5 cm and ptall � pH � ps � pg � 0.005.


naturalness as originally conceived by Loidi (1994) per-formed as expected, thus satisfying the phase 3 objective.Regarding this approach, Chirici et al. (2011) comment that,“Even if the number and particular subindicators usedchange, the general approach and the manner in which thedata are aggregated to derive a complex naturalness indexhave future potential for operational applications.”

Conclusions

Six primary conclusions may be drawn from the study.First, forest naturalness lacks objective assessment methods.

Second, hemeroby indicators of naturalness such as signs ofharvest and management are sensitive to plot size. There-fore, although they may be good indicators of managedforest, they are not necessarily good indicators for distin-guishing less and more natural forest when the objective isharmonization over a large range of plot sizes. Third, eco-system process indicators based on tree species, height, anddbh observations are useful for developing indicators re-lated to species and vertical and horizontal components offorest structural diversity. However, the selected indicators(Shannon index, H�, for species diversity; LAYERS andTALL criteria for vertical diversity; and SD, sdbh, and skew-ness, gdbh, of dbh distributions for horizontal diversity) weresensitive in varying degrees to minimum dbh, plot size, andntree.

The fourth conclusion is that few plots were identified asreflecting all conditions expected to be associated with morenatural forest. The proportion of plots remaining after step1 of the procedure was less than 0.175. In addition, theproportion of remaining plots whose three-tuples(RH, Rs, Rg) were within a Euclidean distance of 0.1 fromthe three-tuple (1, 1, 1) was less than 0.001, and the pro-portion within a Euclidean distance of 0.4 was less than0.005. These results are consistent with the general consen-sus from the literature that few areas of natural forest remainin the study area (Penna 1999). Chirici et al. (2011) reportedsimilar results for a study using European NFI data.

The fifth conclusion is that the procedure for identifyingplots with attributes that are similar to those expected for

Figure 8. Proportion of plots satisfying TALL criterion (atleast one tree among ptall tallest trees) versus proportion, ptall,of trees defined as tall trees.

Figure 9. Proportion, pD, of all plots with Dplot < D and thatsatisfy LAYERS and TALL criteria; ntree � 10.

Table 1. Characteristics of plots with smallest Dplot1.

Rank2Plotno.

No.trees Dplot H� sdbh gdbh

Minimum dbh � 2.5 cm;plot radius � 14.5 m

1 1,848 29 0.14 1.8 13.5 17.52 288 21 0.21 1.7 12.8 19.43 2,230 50 0.29 1.9 13.3 13.24 31 55 0.33 1.8 12.3 13.25 742 17 0.34 1.5 13.5 14.1


1 1,637 27 0.11 1.8 14.5 17.62 259 20 0.19 1.7 13.4 19.93 1,767 35 0.32 1.6 13.0 14.34 1,971 49 0.33 1.9 13.0 12.55 80 18 0.36 1.7 11.2 13.8


1 1,491 13 0.18 1.6 13.8 19.42 546 23 0.20 1.6 13.2 17.43 1,015 22 0.31 1.5 12.2 15.54 200 25 0.34 1.3 15.8 16.15 229 14 0.34 1.4 11.6 19.5


1 1,175 11 0.17 1.6 16.4 20.62 447 20 0.19 1.6 15.2 17.23 806 21 0.34 1.5 12.7 15.64 188 13 0.34 1.4 12.3 20.25 1,103 15 0.35 1.4 13.3 15.9

1 Minimum trees per plot � 10; ptall � pH � ps � pg � 0.005.2 Rank based on Dplot.


natural forest selected plots with similar values for H�, sdbh,and gdbh, regardless of minimum dbh, plot size, and ntree.However, although their attributes were similar, the specificplots selected varied greatly for different minimum dbh,plot size, and ntree. Thus, harmonization would be difficultapart from standardized values for those NFI features orreductive bridges to accommodate the differences.

The sixth conclusion is that construction of reductivebridges to accommodate differences in minimum dbh andntree is fairly easy, but construction of bridges to accommo-date differences in plot size requires ancillary data in theform of distances from trees to plot centers, information thatis usually not readily available. Further, naturally function-ing ecosystem processes that have not been subject to hu-man disturbance should be expected to produce rather dif-ferent conditions that could all be characterized as naturalforest, particularly in different forest types, a factor that wasnot included in this study (Larsson et al. 2001).

Finally, we note that Chirici et al. (2011, Section 5.8)document multiple recommendations that would facilitateharmonization of all biodiversity estimates, not just thosepertaining to naturalness.

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Assessing Forest Naturalness

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