Potential forest cover of New Zealand as determined by an ecosystem process model

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Potential forest cover of New Zealandas determined by an ecosystem processmodelGraeme M.J. Hall a & Matt S. McGlone aa Landcare Research , PO Box 69, Lincoln, 8152, New ZealandPublished online: 17 Mar 2010.

To cite this article: Graeme M.J. Hall & Matt S. McGlone (2006) Potential forest cover of NewZealand as determined by an ecosystem process model, New Zealand Journal of Botany, 44:2,211-232, DOI: 10.1080/0028825X.2006.9513019

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New Zealand Journal of Botany, 2006, Vol. 44: 211-2320028-825X/06/4402-0211 © The Royal Society of New Zealand 2006

211

Potential forest cover of New Zealand as determinedby an ecosystem process model

GRAEME M. J. HALL

MATT S. McGLONELandcare ResearchPO Box 69Lincoln 8152, New Zealand

Abstract A forest biome map for New Zealandis presented, based on the ecosystem process mod-el LINKNZ. Climate surfaces, landforms, slope,and soil types defined 88 933 homogeneous land-scape units covering the North and South islands(264000 km2). Forest development (2000 years) wassimulated on each unit with 78 individually param-eterised species selected by ecological importance.Forest biomes for the units were assigned by therelative biomass predicted for 21 plant functionaltypes, categorised from the available species. An as-sessment of model performance against a systematicsample of 559 measured forest plots was satisfac-tory (dissimilarity index = 0.25). Direct validationwas not possible over most of the landscape wherenative forest has been removed, but performancewas sensible against data from 136 pre-deforesta-tion pollen sites (dissimilarity index = 0.20). Themodelled biome map reproduced the main char-acteristics of the current forest distribution in NewZealand and departures from the observed forestdistribution were generally explained by omissionof whole-stand disturbance effects from simula-tions. The model reproduced lowland areas of thestriking "beech gap" in the west-central zone ofthe Southern Alps, but not the distribution of Noth-ofagus species in montane areas of this zone. Themost likely explanation supported previous conclu-sions that the absence was due to a slow reinva-sion of Nothofagus after exclusion during the LastGlaciation. Predictions of Nothofagus across someregions of the south-eastern South Island where itwas nearly absent before settlement suggested that

B05033; Online publication date 4 May 2006Received 2 7 July 2005; accepted 20 March 2006

the ecological knowledge of some competing coniferspecies was incomplete.

Keywords biome; forest ecosystem model; Noth-ofagus; Agathis; direct extrapolation; New Zealand;biogeography; biogeochemical modelling

INTRODUCTION

New Zealand was almost entirely covered in for-est or tall woody vegetation immediately beforePolynesian settlement in the 13th century (McGlone1989). Widespread burning by early Maori resultedin the loss of between one-third and one-half of theoriginal forest cover by the time of European settle-ment in the mid 19th century. Subsequent burningand logging further reduced the natural forest coverto about one-third of the land area by the mid 20thcentury (Hinds & Reid 1957; Newsome 1987). Thealmost total removal of the original forest coverfrom some fire-prone climatic regions and also fromparticular landscape types poses a major problemfor ecologists. Forests of warm, dry, fertile low-land sites are now only a minute fraction of theiroriginal extent (Hinds & Reid 1957; Wardle 1991;Leathwick et al. 2003). Composition, structure, andsuccessional sequences in these prehuman forestsare poorly known because research has tended toconcentrate on existing forests and species that growin cooler, wetter, and less fertile sites. Whereas thiswas once a scientific issue of little practical import,the rise of public and local government interest inindigenous restoration (Meurk & Swaffield 2000;Spellerberg & Given 2004) has given it a great dealmore significance.

Modelling the previous forest cover through useof observed relationships between forest speciesand present-day environments can contribute a greatdeal towards understanding the potential vegeta-tion cover, and complement field data from extantforest cover and its written and physical traces inthe historical record. There are numerous ways ofmodelling forest cover and these can be categorised

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212 New Zealand Journal of Botany, 2006, Vol. 44

into three main approaches: "correlative" models;"biome-plant functional type" models; and "forestecosystem" models. Correlative models developstatistical relationships between environmental orclimatic parameters and measures of species per-formance such as sampled stem density across alandscape, and use these relationships to assemblepredictions of the overall forest composition. Biomemodels use a small (typically fewer than 20) set ofplant functional types (PFTs) to represent groups ofspecies with similar tolerances and ecological char-acteristics and use the functional types as the basicunits in a biogeochemical model (Cramer 1997).Correlative models have been used to make detailedpredictions of potential forest cover over New Zea-land (Leathwick 2001; Leathwick et al. 2004), andglobal biome models have depicted the potentialnatural vegetation of New Zealand at coarse scales(typically 0.5-grid-cell resolution; e.g., Melillo et al.1993; Haxeltine & Prentice 1996). Both approacheshave strengths and limitations. Correlative modelspredict potential species distributions from fieldrecords of species abundances, and can perform wellwithin the environments defined by those records.Their predictions are less robust over environmentswhere there is a paucity of extant data, where theremnant forests have been modified, or when deal-ing with future scenarios. Biome-type models, incontrast, are process-based and can incorporate theeffects of changing temperatures and CO2 levelson vegetation. The coarse spatial scale usually em-ployed corresponds with the output of atmosphericGeneral Circulation Models, with which they weredesigned to work (Haxeltine et al. 1996), and theyare commonly employed to test past and futurescenarios. However, the loss of possibly importantinformation when defining functional types makesthese models less suited for finer-scale applicationswhere individual species may be central. In thisstudy we explore the capability of a forest ecosys-tem model, LINKNZ, which uses a process-basedstochastic approach to simulate ecosystem behaviourfrom individual species.

Forest ecosystem models such as LINKNZ simu-late the life cycle of individual trees on small sampleplots (typically c. 0.01 to 0.1 ha). The underlying keyconcept is derived from results of various studies ofplant succession showing that the dynamics of a for-est can be reproduced from the species replacementsequences on a sufficient number of sample plots(e.g., Watt 1947; Forman & Godron 1981). The pa-rameters required in these individual-based modelsvary according to the range of ecosystem processes

incorporated, but usually include parameters for thepotential stature, maximum age, and climatic rangesof the species and their particular physiologicallimits to the scarcity of vital resources required forgrowth. These parameters have to be determined forthe most ecologically important forest species at allstages of succession for the models to perform well.Once a dataset has been assembled, the models canestimate the forest composition for any site, predictspecies replacement sequences, and calculate at-tributes such as species biomass, basal areas, andstem density.

Forest ecosystem models have been used suc-cessfully to predict forest succession in NorthernHemisphere temperate forests (e.g., Botkin et al.1972; Shugart 1984; Bugmann et al. 1996) and inNew Zealand (Hall & Hollinger 2000). Advantagesof these spatially implicit models are the produc-tion of a clear successional pathway and their flex-ibility (Peters et al. 2004). Species can be added orsubtracted from the list available to the model andnew areas of parameter space explored. Because ofthis, novel situations can be examined such as thosepredicting the effects on composition, biomass, andproductivity on an ecosystem from an introducedtree species, variation in harvesting regimes, and theeffects on the forest when a species is eliminated bydisease, animal browse, or an inability to adapt tofuture climate change (Shugart & West 1977; Aber etal. 1979; Solomon 1986; Pastor & Post 1988; Lind-ner et al. 1997; Hall 2001). These models attemptto incorporate the key ecosystem processes withoutbecoming overly complex and avoid inclusion ofmany parameters of unknown importance (Peters etal. 2004). However, the models can be vulnerable tooversimplification of the ecological processes, andthe results can be sensitive to inaccurate parametersettings, especially for climatic variables (Bugmannet al. 2000). Their simulations therefore need carefulindependent verification.

The main objective of this study is to generatea map of the potential native forest/scrub biomesunder present climate conditions for the North andSouth islands of New Zealand. The credentials ofLINKNZ have been established in other studies ofNew Zealand forest on a small scale, having beensuccessfully evaluated against New Zealand studiesof biomass and species successional sequences atsites distributed along environmental gradients (Hall& Hollinger 2000). The model has also produced anestimate of the mean biomass in native forest overNew Zealand similar to that derived from an inven-tory-based study, by simulating forest development

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Hall & McGlone—Potential forest cover in NZ 213

on the same set of 559 plot sites associated with a4-km grid (Hall et al. 2001). However, LINKNZ hasnot been used to generate a forest cover map for theentire New Zealand mainland.

There are several reasons why this is now anappropriate next step. While simulating forest suc-cession on limited geographic areas or simulatingparticular aspects of the forest cover of a regionare an important part of demonstrating the utility ofa model, countrywide maps demonstrate whetherparameterisation and performance are adequate forthe full range of environments. Moreover, any re-gional divergences of the modelled forest biomesfrom existing forest provide an opportunity to ex-plore the influence historical effects, disturbanceregimes, or dispersal processes might have on for-est composition. As future climate predictions arerefined, it is hoped that such a map will provide abaseline for comparison of predicted major effectsof climate change on the potential forest distribution.Working within a biome framework simplifies theinterpretation and permits inter-comparability withother modelling efforts on an international scale thatinclude New Zealand. At a practical everyday level,it is expected that this map will, in combination withthe other historical and modelling approaches, givebetter guidance and greater certainty for those plan-ning for restoration of forest cover.

METHODS

Model descriptionLINKNZ, the forest ecosystem model used in thisexercise, shares a common structure with an earlyclass of simulation models (Botkin et al. 1972; Shu-gart&West 1977; Aber et al. 1979; Solomon 1986).Because these models are based on studies of gap-phase dynamics (Watt 1947), they are often referredto as forest gap, or gap-phase models (Bugmann1996). LINKNZ is an extension of the LINKAGESmodel (Pastor & Post 1986; Post & Pastor 1996),which explicitly simulates feedbacks between wa-ter and nutrient availability and their influence ontree growth. The incorporation of these additionalecosystem processes without excessive data require-ments removed the need for species-specific sigmoidequations that reach maximum values at the highestreported basal area or biomass in a given region. Theelimination of this awkward site-specific estimateallowed forest succession to be modelled on areasthat had been previously cleared of forest.

LINKNZ simulates ecosystem carbon and ni-trogen storage and cycling by modelling the inter-actions between individual tree growth, based onphysiological processes governed by environmentalfactors, demographic processes resulting from tree-population dynamics, and environmental processesgoverning water availability. It includes soil waterbalance, litter return, and decomposition. LINKNZassembles a forest ecosystem, at a patch scale of/12 ha (c. 833 m2), by simulating the establishment,growth, and mortality of individual stems fromamong 78 species of indigenous trees on 50 sampleplots for each site. The chosen species include thosethat contribute significantly to the biomass of NewZealand forests and those representing groups ofspecies that collectively play an important role. Pa-rameters for the modelled species have already beendetermined (Hinds & Reid 1957; Wardle 1984; War-dle 1991; Hall & Hollinger 2000). All species wererepresented in the seed rain at all sites. Althoughthe model can incorporate large-scale disturbanceregimes in order to reproduce the major effects ofslips, fire, windthrow, and mass movement, thesewere not invoked because the types and rates ofdisturbance are poorly understood in most regions.

Spatial data sourcesSite soil parameters for the LINKNZ forest modelwere obtained from a spatial database of soil classeswith estimates of soil moisture capacity and soil Ncalculated from sample data (New Zealand SoilBureau 1954, 1968). Estimates for monthly meantemperature and total monthly rainfall were derivedfrom a climate surface estimated from meteoro-logical station data and assigned to a 1 × 1 km gridover New Zealand (Busby 1991; Leathwick et al.2003).

The New Zealand Land Resource Inventory da-tabase (NZLRI; Lynn & Crippen 1991; Newsome1992) provided the base spatial data for the map ofpotential biomes. The NZLRI maps the North andSouth islands of New Zealand into nearly 90 000landscape units or elements covering 26428 000 haor >97% of the total New Zealand land area in unitsranging from <1 ha to 61266 ha (mean size 299 ha)(Table 1). Stewart Island was excluded because ofthe lack of satisfactory soil data, and its absence fromthe NZLRI. The NZLRI employed a multifactor ap-proach based on rock- and soil-type classifications,slope and erosion classes, and a land-use descriptorto define the boundaries of the many landscape units(Newsome 1992). Species successional sequenceswere modelled for 85 344 sample points located at

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the centre of each mapped NZLRI landscape ele-ment. Areas covered by ice, lakes, riverbeds, sanddunes, dredge tailings, and towns/cities (3589 units,4.03% of total land area) were not modelled.

Species level modellingLINKNZ incorporates seasonality and monitorssoil water balance, features which Leemans (1997)found necessary to achieve acceptable performance.Seasonality is represented by the monthly precipi-tation and temperature parameters obtained fromthe climate surfaces (Leathwick et al. 2003). Themodel adjusts the soil moisture balance monthlyaccording to the field storage capacity of the soil,summing gains from precipitation and losses fromevapotranspiration. Inter-annual variation in climateis simulated by random variation of these parametersaccording to their means and standard deviations.Physiological processes related to recruitment andmortality are triggered by specific temperature andmoisture thresholds. The model simulates the nutri-ent cycle by monitoring changes in annual cohorts offoliage litter, coarse woody debris (twigs, branches,stems), and fine root litter. Foliage litter mass is esti-mated by summing each tree's annual litter fall, andthe decomposition and mineralisation of each annualcohort is tracked (Pastor & Post 1985). Differing de-composition rates are reproduced by assigning eachspecies, according to its initial foliage litter lignin:nitrogen ratio, to 1 of 12 decay classes ordered bydecreasing decay rates. The decay of twig, root, andwoody litter is assumed to be the same for all spe-cies. The model dynamically monitors the amount ofsoil nitrogen available for plant growth, increasing itby the amount of nitrogen released by decompositionand drawing it down by the amount taken up by treegrowth (Aber et al. 1982; Pastor & Post 1985). Theperiod simulated (2000 years) is longer than mostNorthern Hemisphere gap-phase modelling studiesin order to accommodate the longevity of the domi-nant New Zealand forest species (Wardle 1991).Species composition and above-ground biomass

(Mg ha-1) were reported at 10-year intervals on eachof the 50 plots simulated at each sample site. Tostandardise values across all the site simulations,the overall relative biomass of each species wascalculated from the annualised biomass sum on allplots throughout the simulation period.

Derivation of plant functional typesPlant functional type (PFT) approaches, in whichindividual species are assigned to a broader groupbased on their ecological tolerances, physiologicalattributes, and growth forms, summarise an other-wise unmanageable species diversity (Cramer 1997;Leemans 1997; Cramer et al. 1999). There are manypossible ways in which sets of PFTs can be assem-bled from the list of species attributes available tothe forest model and each requires testing againstobserved data (Smith & Huston 1989; Bugmann1996).

Each species was first assigned to one of thefour major tree groups that form the basis of NewZealand forest/scrub classifications (Wardle 1984):broadleaved, beech (Nothofagus), podocarp, andkauri (Agathis australis). This primary categorisa-tion permitted comparisons to be made with otherwoody vegetation classifications, while still takingadvantage of the physiologically based PFT schema(Table 2). Three key species traits (minimum annualdegree-day sum; tolerance to low soil-moisture;potential species height) that represent the mostimportant facets of plant physiognomy and physiol-ogy were chosen to define PFTs within these broadgroups. These traits were also considered to be pri-mary influences on species composition in NorthernHemisphere forests (Bugmann 1996; Haxeltine &Prentice 1996).

Annual temperatures largely dictate where spe-cies establish and grow (Larcher 1975; Leathwick1995; Shao & Halpin 1995; Sykes et al. 1996).Annual degree-day limits for each species wereestimated from latitudinal and altitudinal points atthe limits of its recorded distribution and assigned

Table 1 Areas covered by the New Zealand Land Resource Inventory(Newsome 1992).

LandmassArea

(1000 ha)Landscapeelements

Landscape elementsmodelled

North Island 11457South Island 15117Total modelled 26574New Zealand total 26 743

5512333 810

530943225085344

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Table 2 Classification of the 78 New Zealand tree, tree fern, and liane species available to the forest ecosystem modelat all sample sites. Species were chosen because of their ecological significance in New Zealand forests. Vegetationclass and type were assigned from species physiognomy (Wardle 1984). Temperature and drought tolerance classeswere derived from species distributions, annual degree-day sum limits, and annual soil moisture deficit limits.

Species

Agathis australisAristotelia serrataAscarina lucidaBeilschmiedia tarairiBeilschmiedia tawaBrachyglottis repandaCarmichaelia grandifloraCarpodetus serratusCoprosmafoetidissimaCoprosma rotundifoliaCoprosma rugosaCoriaria arboreaCyathea dealbataCyathea medullarisCyathea smithiiDacrycarpus dacrydioidesDacrydium cupressinumDicksonia squarrosaDracophyllum traversiiDysoxylum spectabileElaeocarpus dentatusElaeocarpus hookerianusFreycinetia banksiiFuchsia excorticataGeniostoma rupestre

var. ligustrifoliumGriselinia littoralisHalocarpus biformisHebe salicifoliaHedycarya arboreaHoheria glabrataHoheria lyalliiIxerba brexioidesKnightia excelsaKunzea ericoidesLagarostrobos colensoiLaurelia novae-zelandiaeLepidothamnus intermediumLeptospermum scopariumLibocedrus bidwilliiLitsea calicarisMelicytus ramiflorusMetrosideros excelsaMetrosideros robustaMetrosideros umbellataMyrsine australisMyrsine divaricataMyrsine salicinaNeomyrtus pedunculataNestegis cunninghamiiNothofagus solandri

var. cliffortioides

Vegetation

Class

KauriBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafPodocarpPodocarpBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafBroadleaf

BroadleafBroadleafPodocarpBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafPodocarpBroadleafPodocarpPodocarpPodocarpBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafBroadleaf

Beech

Type

ForestScrubScrubForestForestScrubScrubForestScrubScrubScrubScrubForestForestForestForestForestForestForestForestForestForestForestForest

ForestForestForestScrubForestForestForestForestForestScrubForestForestForestScrubForestForestForestForestForestForestScrubScrubScrubForestForest

Forest

TemperatureM. Villl/V-liVtUJV

class

WarmCoolTemperateWarmTemperateWarmTemperateTemperateCoolTemperateTemperateTemperateTemperateTemperateTemperateTemperateTemperateTemperateCoolWarmTemperateTemperateTemperateCool

WarmTemperateCoolTemperateTemperateCoolTemperateWarmTemperateTemperateWarmTemperateTemperateCoolTemperateWarmTemperateWarmTemperateTemperateTemperateTemperateTemperateTemperateTemperate

Cool

Drought-toleranceclass

MediumMediumIntolerantIntolerantIntolerantMediumTolerantMediumMediumMediumMediumMediumMediumMediumMediumMediumMediumMediumMediumMediumMediumMediumMediumMedium

TolerantMediumMediumMediumMediumMediumMediumTolerantMediumTolerantMediumIntolerantMediumTolerantMediumMediumMediumMediumMediumMediumMediumMediumMediumMediumMedium

Tolerant

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Table 2 (continued)

Species

Nothofagus fuscaNothofagus menziesiiNothofagus solandri

var. solandriNothofagus truncataOlearia arborescensOlearia avicenniaefoliaPhyllocladus alpinusPhyllocladus trichomanoidesPittosporum eugenioidesPlagianthus regiusPodocarpus halliiPodocarpus totaraPrumnopitys ferrugineaPrumnopitys taxifoliaPseudopanax arboreusPseudopanax crassifoliusPseudopanax simplexPseudowintera axillarisPseudowintera colorataQuintinia acutifoliaQuintinia serrataRipogonum scandensSchefflera digitataSophora microphyllaSyzygium maireVitex lucensWeinmannia racemosaWeinmannia silvicola

Class

BeechBeech

BeechBeechBroadleafBroadleafPodocarpPodocarpBroadleafBroadleafPodocarpPodocarpPodocarpPodocarpBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafBroadleafBroadleaf

New Zealand Journal of Botany, 2006, Vol. 44

Vegetation

Type

ForestForest

ForestForestScrubScrubForestForestForestForestForestForestForestForestForestForestForestForestForestForestForestForestForestForestForestForestForestForest

Temperatureclass

TemperateCool

TemperateTemperateTemperateTemperateCoolTemperateTemperateTemperateCoolTemperateTemperateTemperateTemperateTemperateCoolTemperateCoolTemperateWarmTemperateTemperateTemperateWarmWarmTemperateWarm

Drought-tol eranceclass

MediumMedium

MediumMediumMediumMediumTolerantMediumTolerantMediumTolerantTolerantMediumTolerantTolerantMediumMediumMediumMediumMediumMediumMediumMediumTolerantMediumMediumMediumMedium

to a temperature class (Table 2) determined by theestimated minimum annual degree-day sum (abovea 5°C base). A species was assigned to the "warm"class if its minimum degree-day sum was >2200,to the "cool" class if its minimum degree-day sumwas <1060, or to the "temperate" class otherwise.Of the 78 species modelled, over two-thirds (53)belonged to the temperate class, around one-sixthwere assigned to the warm class (12), and aroundone-sixth to the cool class (13). Cool species oftengrow at the same sites as warm and temperate spe-cies, but not the reverse. Similarly, temperate speciesmay grow on sites suited to warm species, but notthe reverse.

Available soil moisture is an important determi-nant of tree growth (Larcher 1975; Cramer 1997).The tolerance of a species to low soil moisture wasdefined as the proportion of a growing season overwhich it can withstand a daily soil moisture tensionless than the wilting point of-15 × 105 p.a. (Pastor

& Post 1986). A drought tolerance value was esti-mated for each species by comparing its geographicdistribution against mapped annual soil moisturedeficits. Species expected to survive near wilting-point conditions over most of the growing seasonwere classified as "tolerant". Species that could notwithstand low soil moisture conditions for a tenthof the season were classified as "intolerant". The re-maining species were classified as having "medium"tolerance (Table 2). Of the 78 species modelled,nearly 80% were assigned to the medium class,17% were drought tolerant, and 3% were droughtintolerant.

Each species was assigned to a forest or scrubvegetation class based on potential species height.

In total, 21 plant functional types were recognisedfrom the 78 woody species modelled. The mostcommon PFT, "Temperate-Medium-Broadleaved-Forest" type, represented over 30% of the species(Table 3). On each site, the relative biomass of each

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PFT was calculated from the sum of its constituentspecies values. Based on the "direct interpolation"method of King (1991), the predicted compositionof PFTs on a sample site was scaled up to representthe typical forest cover on its homogeneous NZLRIlandscape unit.

Vegetation biomes

Following Sykes et al. (1999), "biomes" were de-fined here as large-scale vegetation units charac-terised by the predominance of one or more PFTs.The relative biomass values of the constituent PFTsrepresenting an individual NZLRI spatial landscapeunit (Table 2) were summarised by vegetation type,temperature class, and drought-tolerance class.Within the vegetation type the relative biomassof each vegetation classification group ("Beech","Broadleaved", "Podocarp", and "Kauri") was sum-marised separately so that mixed vegetation typescould be identified and allow the resulting biometo be indirectly related to the forest classes definedby Nicholls (1976) and the forest vegetation coverclasses defined by Newsome (1987). A preliminaryvegetation biome for each spatial unit was definedby the predominant biomass of each of the PFTattributes. In constructing the biome name, vegeta-tion classification groups were ordered by biomassdominance and recognised down to a minimum of10% total biomass. A final shortened list of vegeta-tion biomes was created from the preliminary biomelist by merging biomes of similar composition. Thisfinal list was used to label the map.

Validation of the predicted biome map

The biome map was compared against mapped clas-sifications of existing native forest. Some elementof subjectivity in quantifying differences betweenvegetation maps was unavoidable as there is nogiven scale for comparing different attributes ofvegetation cover (Sykes et al. 1999). Mapped classesand the biome classifications were combined inorder to create directly comparable units follow-ing the methods used to evaluate other vegetationcover models (Monserud & Leemans 1992; Lindneret al. 1997; Bugmann et al. 2000). Each modelledbiome was equated with a forest type based on thepublished type description, and then matched withthe particular PFTs defining the biome.

The relationship between two maps is normallymeasured by the fraction of cells that change classbetween maps, or by the kappa coefficient, whichis corrected for the fraction of cells that would beexpected to remain the same between two random

maps with the same class frequency (Haxeltine& Prentice 1996; Leemans 1997). Because thesemeasures assume that all biome changes are equiv-alent, they underestimate structural and floristicdissimilarities and, furthermore, saturate when allgrid cells have undergone a change, ignoring thefact that further degrees of change are usually pos-sible. Sykes et al. (1999), therefore, introduced thedissimilarity coefficient ΔV, which is based on therelative importance of different plant forms and aweighted series of attributes of each life form. Thecoefficient ranges from 0, for complete contrast, to1, in the case of identity between two biomes. Theimportance values, attribute values, and attributeweights range from 0 to 1, and are set so that thesum of importance values equals 1 and the sum ofattribute weights is 1. The most basic life forms forthe biomes in this study are differentiated into forestor scrub vegetation with a setting of 1.0 for a matchand 0.0 otherwise. The life-form values are scaleddown by a reduction factor constructed as follows.The relative weights among the life-form attributeswere set to 0.25 for the temperature and drought-tolerance classes, and 0.5 for the species group.

Table 3 List of 21 plant functional types derived fromthe chosen list of New Zealand tree, tree fern, and lianespecies. Composition of these plant functional typesdefined the vegetation biomes covering the North Islandand South Island of New Zealand.

No. Plant functional type Species

1 Cool-Dry-Beech-Forest 12 Cool-Dry-Podocarp-Forest 23 Cool-Dry-Podocarp-Scrub 14 Cool-Medium-Beech-Forest 15 Cool-Medium-Broadleaved-Forest 56 Cool-Medium-Broadleaved-Scrub 27 Cool-Medium-Podocarp-Forest 18 Temperate-Dry-Broadleaved-Forest 39 Temperate-Dry-Broadleaved-Scrub 2

10 Temperate-Dry-Podocarp-Forest 211 Temperate-Medium-Beech-Forest 312 Temperate-Medium-Broadleaved-Forest 2413 Temperate-Medium-Broadleaved-Scrub 914 Temperate-Medium-Podocarp-Forest 615 Temperate- Wet-Broadleaved-Forest 216 Warm-Dry-Broadleaf -Forest 217 Warm-Medium-Broadleaved-Forest 718 Warm-Medium-Broadleaved-Scrub 119 Warm-Medium-Kauri-Forest 120 Warm-Medium-Podocarp-Forest 121 Warm-Wet-Broadleaved-Forest 2

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Fig. 1 Potential forest biomes for New Zealand. The 18 primary forest biomes have been clustered into eight primarygroupings identified by sharing the same colour. A, North Island; B, South Island.

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Attribute dissimilarity values within the temperatureand drought classes were set to 0.0 for an exactmatch, 0.5 for the difference of a single class, and1.0 otherwise. Attribute dissimilarity values withinthe species group were set to 0.0 for an exact matchand 0.33 was added for each difference, with an up-per limit of 1.0. Relative weights for matching eachspecies group between biomes were set to 0.5 forthe first comparison, 0.35 for the second, and 0.15for the third.

We took 599 permanent forest plots, which hadreliable biomass estimates and were selected toconstruct a statistically acceptable carbon biomassestimate for New Zealand forests (Hall et al. 2001).These plots were compared via ΔV with estimatesof biomass from the model simulations on thesame sites over simulation periods of 500 and 2000years.

Because forest cover has been cleared frommany areas field sample data were not available tocompare the model's predictions. To evaluate themodel's performance over such regions we con-structed biomes from pollen frequency data. AsHall & McGlone (2001) demonstrated in a regionof south-eastern South Island now largely depletedof forest cover, under current climate conditionspollen frequency data recorded at sample sites wasclearly related to modelled biomass. Consequently,we took a dataset of 136 pre-deforestation pollensites, simulated forest development at each site for2000 years, and compared the pollen-derived biomeswith the model-derived biomes by the dissimilaritycoefficient ΔV.

RESULTS

The potential forest biome map (Fig.1) depicts theextent of our defined biome classes across the Northand South islands. Because extensive deforestationhas reduced the forested area of New Zealand toc. 23% of the land cover (Taylor & Smith 1997),visual comparison of our total-area forest map withthe current forest maps is difficult. We therefore usedthe outline of existing forests as a template to permitan easier comparison of modelled to observed. Wehave mapped and colour-coded equivalent classifica-tions of existing forest areas (Ecological Survey ofNew Zealand's Indigenous Forests: New ZealandForest Service Mapping Series 6) based on fieldobservations and survey data (Nicholls 1976; New-some 1987) alongside the forest biomes for themodel using the same outline (Fig. 2, 3).

Comparisons with biomes based on the forestplot sample gave a dissimilarity coefficient ΔV =0.23 for the 500-year simulation, and ΔV = 0.25 forthe 2000-year simulation (n = 559). The comparisonof biomes created from the pre-deforestation pollensample with biomes constructed from the 2000-yearsimulation on the 136 sample sites gave a dissimi-larity coefficient ΔV = 0.20. When compared withvalues for ΔV given in Sykes et al. (1999), thesecoefficient values provided quantitative supportthat the model could produce an acceptable biomeclassification at a national level.

DISCUSSION

The evaluation of complex vegetation models is notstraightforward at either a conceptual or practicallevel (Rastetter 1996). Effects of human activities onexisting forests, problems in differentiating naturalforest from modified forest, and a general lack ofsuitable data for validation commonly restrict theevaluation of most broad-scale modelling exercises(Shugart & Smith 1996; Cramer 1997; Sykes et al.1999). Maps of current observed vegetation distribu-tion are often used for comparison but, being formu-lated with the overriding considerations of simplicityin representation and fitness-for-purpose, have theshortcoming of being highly abstract interpretationsof complex and variable data sources (Cramer 1997;Leemans 1997). New Zealand vegetation covermaps are no exception and display broad foresttypes with very wide environmental and geographi-cal distributions (Nicholls 1976; Newsome 1987;New Zealand Forest Service Mapping Series 6).As a result, broad, taxonomically based groupingsobscure a great deal of environmental variation. Asan example, beech (Nothofagus) species make upc. 46% of the existing forest as pure types (Wardle1984), and collectively account for nearly 60% oftotal live biomass (Hall et al. 2001), with individualspecies exhibiting strong niche separation alongenvironmental gradients (Wardle 1984; Ogden et al.1996). However, both the Vegetative Map of NewZealand (Newsome 1987) and the New ZealandForest Service Mapping Series 6 map the areas onwhich beech species dominate any other canopyspecies into a single forest type. Maps could thusbe interpreted as providing a useful but rough guideas to how well model simulations match observedvegetation cover.

Measured forest plots were considered moresuited for a formal validation as they offered an

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abundance of detailed, spatially distributed data andcan be systematically sampled to provide statisticallyacceptable results. However, there are still manypossible sources of variation between predictionsand data, and even an ideal model that could predictthe response of forest tree species to the prescribedclimate and soils with 100% accuracy would notnecessarily generate a perfect match to the measuredforest plots. At the most basic level, the prescribedclimate variables were unlikely to match the actualclimate at a site over the simulation period, and thegeneralised soil parameters may differ significantlyfrom the site soil conditions. Furthermore, the localhistory of the site is almost certain to make condi-tions differ from those assumed by the model. Thesimulation began at a prescribed time with a bareplot; such an event on the measured forest plot mayhave occurred at a quite different time. The variationin the local climate may have been more extremethan the stochastic range in the model. Geomorphicevents, such as siltation through flooding, volcanicash deposition, and increased rates of soil turnoverthrough tree blow-down, may have locally influ-enced outcomes, as may biological events such asinsect attack, or disease and browsing pressure.Finally, there is the much discussed possibility thatmajor events such as glaciation, catastrophic volcan-ism, and tectonism have permanently affected thedistribution of trees on a regional basis (e.g., Wardle1984; Ahmed & Ogden 1987; Clarkson 1990; Har-combeetal. 1998).

Many of the above effects that might cause pre-dicted forest cover to depart from that observedcould be accommodated in a model. Disturbance ef-fects on the forest could be incorporated by directlymodelling the type and frequency of disturbanceand its effect on the ecosystem, or more indirectlyby introducing species later in successions ratherthan initially, or even omitting species from thelist available to the model altogether. For example,the historical absence of Nothofagus species fromregions of New Zealand, which were otherwise ap-parently suited to them, was dealt with by Leathwick(2001) by incorporating observed presence-absencedata to refine model predictions. However, suchadjustments could limit a model's applicability:the resulting maps may match the current patternof vegetation cover well but at the risk of obscur-ing the actual predictions. Since in this study wecould not be sure if the lack of agreement betweenpredicted and observed patterns at a broad scalewas a result of model failure per se, it made senseto closely examine a range of possible explanations

for mismatches, as was done, for instance, for beechspecies by Leathwick (1998).

The LINKNZ model performed well at the ba-sic level of matching recorded forest plot biomasswith simulations. Given the variety of factors thatcould have reduced the likelihood of an acceptablematch, it could be argued that the model may haveapproached theoretical expectations. At a broaderlandscape scale, the LINKNZ PFT-derived biomemap for New Zealand matched observed vegetationclasses reasonably well (Fig. 2,3). The north-southand altitudinal biome changes were clearly repro-duced, as was the precipitation-driven east-westcontrast. Over much of the country, the generaloutline of beech-dominated uplands versus podo-carp-broadleaved lowlands was reproduced withdifferentiation into a number of discrete vegetationgroups. However, there were regions and speciesfor which the modelled biomes did not correspondas well with observations and these are discussedbelow.

Forest in central south-eastern South IslandbasinsLINKNZ predicted forest biomes (cool-dry-po-docarp) dominated by drought-tolerant podocarpspecies: matai (Prumnopitys taxifolia) and mountaintotara (Podocarpus hallii) in the driest, frostiestbasins of the south-eastern South Island, mountain-totara-dominant forest on the surrounding lowerslopes, and mixed beech-totara forests that blendedinto pure beech forest with elevation (Fig. 1B).However, the model's prediction for these now-deforested inland areas is at variance with fossilevidence, which suggests a small-leaved angiospermshrubland and grassland on the basin floors (Mc-Glone 2001). Generalised additive models (GAMs)based on species ranges and mapped environmentalsurfaces also reconstructed low forest and shrublandwith little conifer presence (Walker et al. 2004a), asmay be expected from a model based on a patternof severely fire-censored modern vegetation. TheGAM model made similar predictions to LINKNZfor the wetter montane slopes, as did the fossil evi-dence. The reason for the basin floor discrepancywas not immediately clear. The current climate ofthe basin floors certainly does not exclude hardytrees and shrubs (Walker et al. 2004b), and treessuch as matai and mountain totara should have beenable to establish. Perhaps infrequent fire or climaticdisturbances, such as the exceptional frosts of July1996 (Bannister 2003), were sufficiently frequentto maintain the presence of small tree and shrub

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A

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Fig. 2 Comparison ofNorth Island model biome results with a forest classificationbased on New Zealand Forest SeriesMapping Series 6. NZFS6 forest classes have been clustered into eight groupings approximating those represented bythe same colour in the model biome map. A, NZFS6; B, modelled biomes on the NZFS6 template.

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pioneering species and prevented succession by thetaller long-lived podocarp tree species.

Kauri forestsThe biome map captured many aspects of the dis-tribution of kauri-dominated forest (Fig. 1A, 4A).LINKNZ indicated that areas now without forestwere more likely to have been dominated by kauri,and this is supported by soil-derived evidence forpast kauri presence in North Auckland (Taylor &Sutherland 1953). However, the biome map differedfrom current distribution in two ways. First, it pre-dicted the occurrence of kauri outside of its knownrange (Ecroyd 1982), extending the species downto approximately latitude 38.5°S in coastal districts.To some extent this predicted pattern could be con-firmed by palaeodata: McGlone & Jones (2004)suggested that kauri occurred in central Bay ofPlenty before coastal deforestation in the 13th cen-tury, and the same may be true for patches south ofthe current limit on the west coast of the North Is-land. However, kauri was unlikely to have beenpresent in the east coast Gisborne region wherepalaeodata indicated that it was absent in the 13thcentury (Wilmshurst et al. 1999). Other models havealso extended kauri distribution southwards: Mitch-ell (1991) used a BIOCLIM-approach based on aclimate profile with 21 parameters, all of which hadto be satisfied to have kauri present, and generateda similar distribution. Mitchell ( 1991) suggested thatthe discrepancy was related to the decreasing prob-ability of all the growth and dispersal requirementsof kauri being met in increasingly small patches ofhabitat in the south. Our depiction of an increas-ingly fragmented suitable habitat towards the south-ern end of the range agrees with thisinterpretation.

The biome map also predicted that fewer areaswould be dominated by kauri and more areas wouldbe dominated by temperate podocarp and broad-leaved species than is currently the case. A reason forthis discrepancy is that the surviving areas of forestare overwhelmingly concentrated in steep, uplandareas such as the Coromandel Peninsula. As kauriis a "long-lived pioneer" (Ogden & Stewart 1995),the greater levels of disturbance at such sites wouldfavour it over competing angiosperm and podocarptrees. An evaluation of the LINKNZ model at River-head, Auckland (Hall & Hollinger 2000), showedthat, in the absence of any major disturbance, kauriwould initially establish and eventually be replacedby broadleaved species, which in turn would finallybe succeeded by podocarp species. When forest

succession was interrupted by disturbance with amean return time of c. 300 years, based on studiesof the kauri population structure by Ahmed & Ogden(1987), episodic regeneration ensured the continueddominance by kauri.

Nothofagus: anomalous presences in the modelThe model predicted that upland areas to the westof Lake Taupo in the North Island, and areas in theSouth Island around upland central Westland, theinland north-east, and the south-east (aside fromcoast, inland basins, and the Southland Plains) wouldbe dominated by cool temperate beech forest biomes(Fig. 1, 2, 4D). Current forest remnants and fossildata indicate that these areas are or were covered bypodocarp- or angiosperm-dominated forest (Wardle1984; McGlone & Basher 1995; McGlone 2001).

The minor west Taupo beech anomalies are mostlikely to be a direct consequence of the Taupo Te-phra, a 2nd century AD ignimbrite eruption fromLake Taupo that devastated nearby forest (Wilson& Walker 1985; Clarkson et al. 1988; Wilmshurst& McGlone 1996). The more rapidly bird-dispersedpodocarp and broadleaved species are likely to haveoutpaced the much slower spread of beeches (Notho-fagus) in the west, while in the east, survival ofbeeches in the adjacent high mountain ranges per-mitted a more complete recolonisation.

The question of why beech is nearly completelyabsent from large areas of the South Island thatappear eminently suited for it has focused on theWestland beech "gap" as, being in an almost com-pletely forested part of the South Island, this gap isstrikingly obvious and, moreover, appears to lackany clear geographic or environmental correlatives.Although there has been a vigorous debate in whichcurrent environmental factors have been promotedas the underlying cause (McGlone 1985; Haase1990) the consensus favours historical explana-tions, such as elimination of beech from centralWestland by ice during the last glaciation and dis-persal limitations preventing subsequent recolonisa-tion (Cockayne 1926; Wardle 1988; McGlone et al.1996; Leathwick 1998). Our results indicate that thebeech gap has some climato-edaphic basis, in thatLINKNZ correctly predicted podocarp-angiospermdominance in lowland sites from northern Fiordlandthrough to the Grey Valley. On the other hand, italso predicted podocarp-dominant forest north ofthe gap where the forests are rimu (Dacrydium cu-pressinum)-beech mixtures. In a simulation of forestdynamics at Reefton, invoking a windthrow-typedisturbance regime (mean 300-year return time), the

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model correctly predicted a rimu-dominated forestwith a major beech component (Hall & Hollinger2000) because beech species, particularly red beech(N. fusca), tend to be favoured by some forms ofdisturbance (Wardle 1984; Ogden et al. 1996). It istherefore conceivable that disturbance regimes areinvolved in setting the boundaries of the lowlandWestland beech gap.

LINKNZ also suggested that beech forest shouldbe dominant throughout upper montane and alpineareas of the Westland beech gap. Many of theseareas are currently occupied by slow-growing smallto moderate-sized tree species such as mountaintotara, cedar (Libocedrus bidwillii), kamahi (Wein-mannia racemosa), rata (Metrosideros umbellata),Dracophyllum species, Olearia species, and others,which are subdominant to or excluded by beech insimilar environments both north and south of thegap. Rather than beech species being restricted byunfavourable upland environments, the most likelyexplanation for the domination of upland areas bylong-lived small conifer and broadleaved trees ap-pears to be the slow spread of beech after the glacia-tion.

Although it is not as often remarked on, theSouthern Alps beech "gap" also extends east. Sev-eral strands of evidence drawn from forest remnants,soil charcoal, and pollen (Wardle 1984; Burrows &Russell 1990; Burrows et al. 1993; McGlone et al.1996) suggest that the inland Canterbury valleys andmountain ranges from south of the Rakaia River tothe Tasman River were largely covered by podo-carp-broadleaved forest. Remnant forest patchesand historical studies indicate that the now virtuallyforestless interior of the south-eastern South Islandwas once almost entirely in podocarp-broadleavedforest, dominated by matai, totara, and toatoa (Phyl-locladus alpinus) (McGlone 2001; Wardle 2001).LINKNZ assigns most of the area, outside of thecoastal hills and plains and the driest inland in-termontane basins, into some version of a beechor beech-podocarp forest, as do regression-basedcorrelative models (Leathwick 1998). Two pointscan be made about this variance. First, the scatteredstands of mountain and silver beech bordering thebeech-free region (Wardle 1984) suggest that exclu-sion of beech during the glaciation may not be theentire explanation. Second, there is little currentphysiological or distributional information for forestspecies relevant to this area, which is the driest, mostdrought-prone, and most seasonal in New Zealand. Itis therefore possible that the parameters available tothe model failed to represent the climatic tolerances

for the beech, matai, mountain totara, and toatoaspecies for this region.

Nothofagus: anomalous absences in the modelThe modelled results underestimated the occurrenceof beech in the Marlborough Sounds region andinland Taranaki (Fig. 1, 2, 4D). Both regions sharea mild, moist climate, which is usually more condu-cive to the growth of other species, but where beechappears to be favoured by edaphic factors. Regres-sion-based GAMS modelling had a similar problemin depicting beech distribution in Taranaki but wassuccessful in the Marlborough Sounds (Leathwick1998). In inland Taranaki, beech stands are virtuallyconfined to narrow ridges, spurs, cliff tops, and peakscapped by low-fertility sandstones (Wardle 1984),and in the Marlborough Sounds the beech-dominateduplands are characterised by leached, low-nutrientsoils (Molloy 1988). It would appear, therefore, thateither the soils in the underlying dataset were notadequately characterised by the NZLRI, a particularproblem in the intricate inland Taranaki situation, orthat the model may not be properly recognising thosesoil factors which favour beech dominance.

CONCLUSIONS

LINKNZ performed well in reproducing the broadoutline of the forest biomes of New Zealand and instatistical tests based on individual plots and pollendata. The use of a single set of parameters for thenearly 80 forest species throughout the entire coun-try, with no species excluded on a priori grounds atany site, testified to the robust basis of the model.While producing a generally acceptable biome map,differences were evident in several areas from theobserved forest distribution. This was not surpris-ing because New Zealand forests in many areas areexposed to frequent disturbances at a range of spatialscales. In general, the absence of whole-stand dis-turbance from the simulations tended to favour thepresence of shade-tolerant long-lived podocarp spe-cies, and their dominance in some areas could havebeen further exaggerated by the long time frame setfor the simulation period. Obtaining a closer matchof model forest predictions to observed forest coverat many sites would depend on estimates of the dateand type of the last disturbance. While this may bepossible at some sites, determining the disturbanceregime for the landscape as a whole would dependon the efficiency of the spatial sampling strategyand the resources available to sample and produce

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reliable estimates for each point. This factor is likelyto remain a barrier to any model that attempts toreproduce current forest cover.

There were several possible reasons for the dis-crepancies evident in the distribution of beech spe-cies. The model's placement of beech in some areasof the South Island that were originally in conifer-dominant or broadleaved forest could be attributed todispersal processes, absent from the model, that werelikely to have slowed recolonisation by beech afterclearance by glaciation. The mismatch in the inlandsouth-eastern district of the South Island may reflecta lack of data reflecting the resilience of competitorspecies such as mountain totara, mountain cedar,and toatoa. Finally, in some mild, wet regions wherebeech is present (e.g., Marlborough Sounds; inlandTaranaki) the model predicted a near absence. It islikely that the general soil-class parameters did notadequately depict the small-scale pattern of leachedsoils that would have favoured beech.

The LINKNZ map highlights the vast amount ofpodocarp forest that has been destroyed. In particularthe conifer forests of dry regions were almost entire-ly destroyed by Maori fire, while the beeches provedmuch more resilient. As a result, most ecologicaldata and understanding centre on well-representedforest types, mainly tall conifer-dominant forest inwet, mild, lowland situations and beech forest incool, wet regions. Understanding of New Zealandforest ecosystems in drier regions must be improved,otherwise the ability to evaluate predicted restorationoutcomes of the original forest cover will be weak.

The potential of forest-gap models such asLINKNZ has been little explored in New Zealand.They are suited for a number of applications. Wherethe climatic regime either has been different to thatof today, or is predicted to be different in the future,process-based models can predict vegetation coveror climate. For instance, Hall & McGlone (2001)were able to quantify climatic estimates for earlyHolocene Southland by using LINKNZ to matchpollen-derived forest reconstructions. As the reso-lution of general circulation models is improved,predictions of past and present climatic regimes overNew Zealand will become more detailed, and mod-els like LINKNZ will be able to help determine theconsequences for forest cover. Restoration projectsare the focus of much attention (Meurk & Swaffield2000) and can benefit from a more robust abilityto predict forest succession outcomes and possibleconsequences of future climate change. The effectsof invasive tree species on indigenous ecosystemsare of concern and, provided suitable parameters

can be obtained for exotic species, their potential forsuppression and replacement of indigenous trees andlikely successional trajectories could be predicted.Finally, if parameters for rates of dispersal wereincorporated, forest-gap models might better depictchange at the landscape scale.

ACKNOWLEDGMENTS

The Forest Service maps used for our analysis werelargely the result of many years of fieldwork and carefulobservation by John Nicholls, and all forest ecologists inNew Zealand are in his debt. We thank John Leathwick forproviding the climate surface estimates for the North andSouth islands, and James Barringer for his assistance withthe New Zealand Land Resource Inventory database. Wethank Sarah Richardson and John Hunt for their helpfulcomments on a draft of this manuscript. This project wasfunded by the Foundation for Research, Science andTechnology. We thank two unnamed referees for theirconstructive comments.

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Documents

Potential forest cover of New Zealand as determined by an ecosystem process model