Foliar Browse Index: A Method For MonitoringPossum Damage ... · 2.3.2 Representative sample 13...

..LIBRARYLANDCARE RESEARCH NZ·'.0. BOX 69, I.INCOI.N, NZ.

Foliar Browse Index:

A Method For Monitoring Possum

(Tricho5urU5 vulpecula) Damage To Plant Species

And Forest Communities

LJ. Payton!, CoJ. Pekelharing!, CoM. Frampton2

1 Landcare Research (NZ) Ltdp.o. Box 69, Lincoln, New Zealand

2 Centre for Computing and BiometricsLincoln University, P.O. Box 84, Lincoln, New Zealand

Manaaki WhenuaLandcare Research

PREPARED FOR:Director, Science and ResearchDepartment of ConservationP.O. Box 10-420, Wellington

DATE: October 1999

2

© 1999

This report has been produced by Landcare Research New Zealand Ltd for theNew Zealand Department of Conservation. All copyright in this report is the propertyof the Crown and any unauthorised publication, reproduction, or adaptation of thisreport is a breach of that copyright and illegal.

CATALOGUING IN PUBLICATION:

PAYTON, I. J.Foliar browse index: a method for monitoring possum (Trichosurus vulpecula)

damage to plant species and forest communities / I.J. Payton, CJ. Pekelharing, CM.Frampton. - Lincoln, N.Z.: Manaaki Whenua-Landcare Research, 1999.

ISBN 0-478-09337-3

I. Pekelharing, C J. II. Frampton, CM. (Chris Miles) III. Title. IV. Landcare ResearchNew Zealand.

UDC 581.9:599.223.1

Cover photographs by Caroline Thomson & John Barran.Printed by The Caxton Press, Christchurch on Ambassador Recycled Satin.

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Contents

3

Summary ....................................................................................................... 5

1. Introduction 6

1.1 Techniques for monitoring possum damage to forest canopies 6

1.1.1 Descriptive accounts 7

1.1.2 Permanent forest plots 7

1.1.3 Point-height intercept (PHI) 7

1.1.4 Photopoints 8

1.1.5 Hemispherical (fish-eye) photography 8

1.1.6 Canopy scoring 9

1.1.7 Remote sensing 9Aerial photography 9Airborne video 10Satellite imagery 10

2. Designing A Survey To Monitor Possum Damage To Vegetation 11

2.1 Objectives 11

2.2 Sampling strategies 11

2.2.1 Monitoring possum damage to forest communities or plant species 11

2.2.2 Monitoring possum-damaged Individuals 12

2.3 Sampling design 12

2.3.1 Stratification 12

2.3.2 Representative sample 13Locating transects and sample plots 14Selecting a representative sample 16

2.3.3 Sample size 16

3. The Foliar Browse Index Method 18

3.1 Choice of indicator species 18

3.2 Criteria for selecting sample trees 19

3.3 Assessment of sample trees 19

3.4 Emergent species 24

3.5 Epiphytes 25

3.6 Reassessment of sample trees 26

4

4. Technical Considerations 28

4.1 Observer variability 28

4.2 Seasonal variability 31

4.3 Background noise , 33

4.4 Relationship between foliage cover and leaf biomass 35

5. Data Analysis Options 37

5.1 Data classification 37

5.2 Data transformation 37

5.3 Data analyses 38

5.3.1 Statistical tests 38

5.3.2 Correlation 38

6. Data Storage ..................................................................................................... 40

7. Acknowledgements 41

8. References ..................................................................................................... 42

9. Appendices 49

9.1 Random number (1-50) sets for use in determining theplacement of transects 50

9.2 List of equipment required for establishing or remeasuringFoliar Browse Index plots 51

9.3 A.B.

Foliar Browse Index Plot Sheet - transect and plot data 52Foliar Browse Index Plot Sheet - indicator species data 53

9.4 Foliar Browse Index - Indicator Species Assessment Sheet 54

9.5 Foliage Cover Scale 56

9.6 Flowering and fruiting periods for a range of plant indicator species 57

9.7 Botanical names of plant species referred to in the text. 61

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5

Summary

Since their initial introduction in c.1840 Australian brushtail possums (Trichosurus

vulpecula) have colonised most forested areas on the main islands of New Zealand.Damage caused by this nocturnal arboreal marsupial includes the progressivereduction and elimination of preferred food species, and can lead to the collapse offorest canopies over large areas. Possum control is now a large and growing industryin New Zealand. Where the protection of vegetation is the primary reason for reducingpossum populations, managers need to be able to determine when control is required,which areas should have priority, whether control achieves its goals, and when furthercontrol measures will be necessary. Reliable inferences and predictions about possumdamage can only be obtained from robust, quantitative data. In this manual we reviewexisting techniques for monitoring possum damage to forests, discuss the design ofvegetation surveys to monitor possum damage, and describe a new method forassessing possum damage to plant species and forest communities. The Foliar BrowseIndex method uses ground-based assessment of plant indicator species to determinethe impact of possums on forests and/ or vegetation response to possum control. Itcan also be used to monitor the impact of possum browsing or other damaging agents(e.g., insects, wind, frost) on individual plant species. The method uses permanentlymarked individuals to determine trends in the foliar cover of tree canopies, and possumdamage to leaves and stems. Options for analysing data are outlined.

Keywords: conservation, forest, plant indicator species, possum damage, Trichosurus

vulpecula, vegetation monitoring.

1 Introduction

Brushtail possums (Trichosurus vulpecula) were first introduced from Australia c.1840 toestablish a fur trade. The subsequent spread of this nocturnal arboreal marsupial wasaccelerated by over 450 liberations (Pracy 1974), and today few forested areas on themain islands of New Zealand remain uncolonised (Cowan 1990).

Possums are opportunistic feeders, browsing the foliage, flowers and fruit of a widerange of plant species (Kean & Pracy 1953, Green 1984) and predating forest birds andinvertebrates (Cowan & Moeed 1987, Brown et al. 1993). However, a few key plantspecies characteristically form the bulk of the diet. Where these species are majorstructural components of forests, possum damage is extensive and may lead to completecanopy collapse over large areas (Batcheler 1983, Payton 1987, Rose et al. 1992).

Conversely, where browsing of the dominant tree species is minimal (e.g., Nothofagus

spp.), floristic composition but not forest structure is typically affected (Wardle 1984).

Possum control is now a large and growing industry in New Zealand. Where theprotection of indigenous forest vegetation is the main reason for control, we need tobe able to monitor possum damage to vegetation to help determine:

• When possum control is required.• Which areas should have priority for possum control.• Whether control operations achieve their goals.• When further control measures will be necessary.

Sound quantitative techniques are needed to make reliable inferences and predictionsabout the nature, severity and extent of possum damage. In this manual we reviewexisting techniques for monitoring possum damage to forests, discuss the design ofvegetation surveys to monitor possum damage, and describe a new method forassessing possum damage to plant species and forest communities.

1.1 Techniques for monitoring possum damage to forest canopies

The influence of introduced animal pests on native forest ecosystems has beenextensively monitored throughout New Zealand over the last 40-50 years (Stewart et

al. 1989). Most studies have focused on the impact of ground browsers (deer, goats) onforest understorey vegetation. Only in the last 25 years have attempts been made toquantify damage to forest canopies caused by possums (Meads 1976, Leutert 1988,Payton 1988, Pekelharing & Batcheler 1990, Atkinson 1992, Payton et al. 1997).

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INTRODUCTION 7

1.1.1 Descriptive accountsThere are numerous descriptive accounts of possum damage to indigenous forests,ranging from broad overviews (e.g., Kean & Pracy 1953, Batcheler & Cowan 1988)to detailed descriptions of possum impacts in specific areas or vegetation types (e.g.,Zotov 1949, Pekelharing & Reynolds 1983). These accounts are largely subjectiveand strongly influenced by the authors' observational skills and perceptions (e.g.,Kirk 1920, Batcheler 1983). The RECCE plot, a standardised non-area method ofvegetation description (Allen 1992), records animal (deer, goat, possum etc.) damageto plant species as light, medium or heavy. Data of this kind have been used todescribe the impact of ungulates on forest understorey vegetation (e.g., Wardle 1974)and could be used to assess the extent of possum damage to forest canopies.

1.1.2 Permanent forest plotsThe traditional approach to monitoring change in forest communities is to tag andmake repeated measures of trees, saplings, and seedlings on permanently markedplots (Allen 1993). This approach will give some information about changes takingplace in the forest canopy in that it will identify turnover in the canopy tier. However,because individual trees are normally only recorded as live or dead, changes inparameters such as stem density and basal area are not sensitive short-term indicatorsof canopy damage. In the longer term, permanent plots provide detailed informationon changes in both the composition and structure of the vegetation that is not readilyobtainable using other methods.

1.1.3 Point-height intercept (PHI)The point-height-intercept approach involves projecting a point or cylinder up (ordown) through the vegetation and recording what it intercepts. In short « 3m)vegetation a graduated pole is commonly used (Scott 1965, Dickinson et al. 1992). Fortaller vegetation a sighting device (usually a rifle scope or a monocular lens) isused, mounted on a gimble to ensure a vertical line of sight (Park 1973, Leathwicket al. 1983).

Point-height-intercept methods can provide a range of information on thecomposition and structure of forests (Park 1973). With repeat measurement thistechnique should be able to be used to detect changes in both forest canopyand understorey vegetation. However it will only provide information on

8 INTRODUGION

'browsing impacts if the foliar intercepts are recorded as browsed/unbrowsed andthe browser identified.

In forests the major problems with PHI techniques are the multi-layered nature of thevegetation and the accurate measurement of the height of intercepts. Data collectionis also time consuming. PHI has proved effective for monitoring non-forestcommunities (Scott 1965, Dickinson et al. 1992), but has little to recommend it as amethod for monitoring canopy vegetation in tall forest.

1.1.4 PhotopointsPhotopoints, usually an oblique photograph taken from a marked point, have beenused in many parts of New Zealand to record vegetation change. They can provide agood visual record of changes in the foliage cover and extent of dieback in tree canopiesbut are not readily ammenable to quantitative analysis (BeadeI1987), and cannot beused to assess the extent or severity of browsing. Their use is most appropriate wheretree canopies are clearly visible, and where there have been no changes in theunderstorey which would obscure the field of vision.

1.1.5 Hemispherical (fish-eye) photographyThe technique uses an extremely wide-angle (fish-eye) lens to take photographs lookingup through the forest canopy. In New Zealand it has been used to quantify understoreylight environments in conifer-broadleaved (Veblen & Stewart 1982) and mountainbeech1 (Hunt & Hollinger 1988) forests.

Fish-eye photography can provide an accurate measure of canopy cover and lightpenetration (Lasko 1980). It does not allow individual plant species to be identified orbrowsing to be quantified. The development of computerised analysis systems (Chanet al. 1986, Rich 1989) has largely eliminated the lengthy time required for imageanalysis, making fish-eye photography a practical means of assessing changes in forestcanopy cover. To monitor change (i.e., repeat measurement) the ability to relocate theexact position from which the original photo was taken is critical. As with otherphotographic techniques, it is easy to amass a much larger number of photographsthan are needed or can be readily processed.

1 See Appendix 7 for botanical names of plant species mentioned in the text

INTRODUCTION 9

1.1.6 Canopy scoringDirect observation of tree canopies has been widely used to assess the impact of insectpopulations (Wickman 1979, Fox & Morrow 1983, Landsberg 1989), nutrient deficiency(Will 1985, Hunter et al. 1991), and pollutants (Innes 1988) on forest health. Most studiesare of one or a few dominant, timber producing species. Tree canopies are assigned toone of a series of predetermined classes, often defined using diagrams or photographs(Innes 1990). Because it is a scoring methodology rather than one based on counts ormeasurements, careful attention needs to. be paid to questions of reliability andrepeatability (Innes 1988, Innes & Boswell 1990).

In New Zealand several studies have quantified possum damage to forest canopiesby direct observation of either individual leaf bunches (Meads 1976, Payton 1988) orwhole tree canopies (Leutert 1988, Atkinson 1992, Payton et al. 1997). The Foliar BrowseIndex method described in this manual combines a canopy-scoring approach withthe use of indicator species to provide an assessment of possum damage to forestcommunities.

1.1.7 Remote sensingAerial photographyThe use of aerial photographic images to assess damage and monitor change inNew Zealand forest canopies is still at an experimental stage. Trials using colourinfrared aerial photographs of pohutukawa forest on Rangitoto Island in theHauraki Gulf have shown a good relationship between remotely-sensed measuresof total green leaf biomass and field scores of percentage leaf cover (Trotter 1992).This work is being extended to more complex forests and steeper terrain (Trotter &

Brown 1999).

Visual aerial photographic interpretation has been used for some decades for largescale mapping of forest vegetation and damage to forest canopies (Avery 1966). InNew Zealand, Rose et al. (1988) have shown that the amount of dieback visible onaerial photographs is strongly correlated with ground-based measures of defoliation(d. section 1.1.4) in major canopy tree species such as southern rata and Hall's totara.Conspicuous dieback is mapped using a predetermined damage scale in spatial unitsdefined by topographic features such as creeks, ridges, or bluff systems. Data analysisand map production are achieved with the aid of a geographic information system.Recent studies include mapping possum damage to forests in central and southern

10 INTRODUCTION

Westland (Rose et al.1988, 1992, 1993) and a study of possum-vulnerable forests in theDepartment of Conservation's Nelson/ Marlborough Conservancy (Rose et al. 1995).

Airborne videoThis is a technology developed by the USDA Forest Service and recently evaluated inNew Zealand by the New Zealand Forest Research Institute (Hosking et al. 1992,Hosking 1995). It involves using a video camera fitted to a fixed-wing plane orhelicopter to obtain images of the forest canopy and includes a video image-processingsystem (Pywell & Myhre 1990). Advantages of the airborne video approach includethe low cost of video tapes, ability to acquire video images in weather conditions notsuitable for aerial photography, and the immediate availability of the video images.Disadvantages include difficulty with the accurate relocation of flight paths for repeatmeasurement, a lower resolution than for photographic film, and the potentiallyless permanent nature of video film as a storage medium. As with other remotesensing techniques the usefulness of airborne video hinges on the ability of theimage-processing system to resolve and quantify forest canopy characteristics fromthe video footage.

Satellite imageryRemote sensing of satellite images has major potential for the large-scale assessment ofchanges in forest canopy condition in New Zealand, once smaller areas are able to beresolved and the relationships between spectral signature, vegetation type, and degreeof damage have been established (Trotter & Brown 1999). In common with other remotesensing techniques, satellite image analysis must be accompanied by adequate groundsurvey to confirm the validity of the analyses and to determine the cause of the changesor damage observed (Payton 1990).

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2 Designing A Survey To Monitor Possum Damage To Vegetation

2.1 ObjectivesYou need to base the design of a vegetation monitoring programme on a clear statementof the problem to be investigated and the objectives of the study (Jongman et al. 1987).

This will form the basis for decisions about sampling strategies and design, and willhelp ensure that the data you collect can be reliably used to answer the questions yourstudy was set up to examine.

Where a study has multiple objectives care needs to be taken to ensure that the samplingstrategy and design can accommodate the requirements of each of the objectives.Similarly, where objectives change during the course of a long-term monitoringprogramme, it is important to ensure that any changes in sampling strategy, design ormethodology do not compromise the outcomes of both the original and the revisedmonitoring programmes.

2.2 Sampling strategiesA well chosen sampling strategy is importantbecause the results of your survey dependnot only on clearly defined objectives and appropriate methods of analysis, but alsoon the data you collect (Jongman et al. 1987).

Your purpose in monitoring possum damage to vegetation will usually be either tojustify possum control through documenting ongoing vegetation decline, or to judgethe success of control in improving vegetation condition. You need to determinewhether the objectives of the study require an assessment of the overall condition ofand degree of possum damage to forest communities or species, or the ongoing declineor recovery of possum-damaged individuals. These scenarios require differentsampling strategies (choice of species and individuals) if current and future possumimpacts are to be reliably determined. Whatever sampling strategy you adopt, besure it is clearly written down and available to everyone involved with the datacollection, analysis and interpretation of the study.

2.2.1 Monitoring possum damage to forest communities or plant speciesTo determine the extent and severity of possum damage to the health of forestcommunities, you need to include all or at least a representative selection ofcommonly occurring possum-preferred species in the monitoring programme. Thisshould include subcanopy, canopy, and where appropriate emergent species. When

12 DESIGNING A SURVEYTO MONITOR POSSUM DAMAGE TO VEGETATION

selecting individuals for inclusion in a monitoring programme you need toensure a representative sample of each species, which is not biased by current treehealth or the degree of past or present possum damage. These constraintsalso apply when the health of individual (e.g., rare or endangered) species is beingmonitored.

2.2.2 Monitoring possum-damaged individualsIf the objectives of the study relate to the ongoing decline or recovery of possumdamaged individuals, you can bias your sampling strategy towards plant specieswhich are highly possum-preferred and individuals that are currently beingbrowsed by possums. Note however that while this latter approach will allow youto assess the current and future status of the sample population of browsedindividuals, it will not allow you to make similar statements about the status ofthe species over the whole sample area. Before adopting this type of samplingstrategy think very carefully about whether it will enable you to fulfill the objectivesof your study, and whether it will limit the usefulness of the data for other (e.g.,comparative) studies.

2.3 Sampling designThe crucial issues you need to consider when determining the sampling design for amonitoring study are the selection (stratification, random representative sample) andnumber (sample size) of individuals to be monitored.

2.3.1 StratificationWhere you suspect factors such as landform, forest type, distance from the forestmargin, or the patchy nature of the possum population are systematically affectingthe level of possum damage to a plant species being sampled, you need to stratifythe sample of that species. Stratification serves two purposes. Firstly, it enablesseparate generalisations to be made about different parts of a larger study area.This is appropriate when, for whatever reason, the condition of the plant speciesbeing monitored differs markedly in different parts of the study area (e.g., a plantspecies that is browsed heavily in the gullies but is largely unbrowsed on theexposed ridges). The second purpose of stratificationis to reduce sample variability,and thereby increase the sensitivity of statistical testing. Stratification involves

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DESIGNING A SURVEYTO MONITOR POSSUM DAMAGE TO VEGETATION 13

dividing the study area into units or strata based on the factor(s) influencing thevariability in possum damage, and randomly selecting individuals to be monitoredfrom within each stratum. If the objectives of your study require an assessment ofpossum damage:

• Over the whole study area, base the sample size for each stratum on the relativeabundance of the species in that stratum.

• For each stratum, monitor a similar sufficiently large (see Section 2.3.3) samplein each stratum.

In the case of monitoring associated with possum control operations, where the stratarepresent control and non-control treatments, divide sampling resources equally.

2.3.2 Representative sampleTo draw valid conclusions about the extent and severity of possum damage to theplant species you are monitoring you need to:

• Sample throughout the study area or stratum.• Select the individuals to be monitored in a random (non-biased) manner.

The individuals sampled must be representative of the total population of the specieswithin the stratum or study area. If plants are selected non-randomly (e.g., those thatare close to a track and therefore readily accessible) the sample will not be representativeof the wider population, and cannot be used to validly estimate the parameters (e.g.,foliage cover, possum browse) of that population. The individual plants beingmonitored also need to be independent, to ensure the sample is representative and tosatisfy the underlying assumptions of the standard statistical tests. Where individualsare sampled non-independently (e.g., from within a small area) they are likely to beaffected by similar conditions (e.g., climatic, edaphic), and will therefore not representthe full range of variation present in the population.

Genuine random sampling is rarely practical. Because of the nature of the topographyin many indigenous forests, sampling is typically carried out along a series of transectswhich traverse the full extent of habitat types within the study area or stratum. Whereindividual species are not present within all plots or are confined to specific habitats(e.g., riparian or gully communities), additional transects may be needed within thosehabitats to ensure an adequate sample size is obtained.

14 DESIGNING A SURVEYTO MONITOR POSSUM DAMAGE TO VEGETATION

Locating transects and sample plotsThe following methods for determining the placement of transects are based onAllen(1992, 1993).

• Use a grid pattern based on the X and Y coordinates found on a NZMS 260metric map. Where monitoring is being carried out over a large area (> 200 km2

)

divide the survey area into 10 x 10 km blocks. Subdivide each block (or stratum)into 1 x 1 kIn squares, and use random X and Y coordinates or a set of randomnumbers (Appendix 1) to select one square for each of the transects you propose toestablish. Identify the point on a watercourse that is nearest to the centre of theselected 1-km square. This point becomes the transect origin. Randomly assignthe transect to one side of the watercourse. Draw a line from the origin to thenearest main ridge or timberline.

• Divide the survey area into blocks, catchments or strata and allocate the requirednumber of transects to each. Start at the head or mouth of a river and run aplanimeter along the main stream and all tributaries. Select a random number(usually two-figured) and stop when the planimeter reaches that number. Thispoint is the transect origin. Randomly assign the transect to one side of thewatercourse, and draw a line from the origin to the nearest main ridge ortimberline. Continue the process until all transects are allocated.

The compass bearing to be used in the field is determined from the transect on themap, with correction for magnetic declination.

Mark the origin of the transect with crossed pieces of "permolat" flagging containingthe block/line number and transect bearing, and the transect route with sufficienthorizontal "permolat" markers to make it easy to follow on future occasions. Make agenerous allowance for tree growth when attaching line markers and tree tags. Alongeach transect establish a maximum of 10 equally spaced sample plots. The first plotshould be a minimum of 20 m from the transect origin, and subsequent plots not lessthan 100 m apart. The minimum distance of 100m is set to ensure that adjacent plotsare independent, and not subject to very similar environmental influences. Mark eachplot with crossed pieces of "permolat" bearing the line / plot number, attached to thetree nearest the plot centre. The area of the plot is defined by a circle with a radius of20 m around the plot centre tree. A list of equipment required to establish a transect isgiven in Appendix 2.

Record the data you collect using the format in Appendix 3A.

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DESIGNING A SURVEYTO MONITOR POSSUM DAMAGE TO VEGETATION 15

For each transect record

Approach: a brief description of the terrain and vegetation. Include notes ondamage caused by wind, snow, insects, fire, or other influences.

Location:

Notes:

Birds:

a sketch of the transect and plot locations emphasising landscapefeatures (e.g., slips, gullies, creeks) that will help to relocate thetransect.

any other pertinent observations and impressions.

record only species positively identified by sight or sound.

For each plot record

Altitude: measured to the nearest 10 m. Barometric altimeters should be reseteach morning, or more frequently during changeable weather, frompoints of known altitude on the topographical map.

Aspect:

Slope:

measured to the nearest 5° at right angles to the general lie of the plot.

the average slope of the plot, measured with an Abney level orhypsometer. Describe the shape of the slope as convex, concave, orlinear.

Drainage:

Physiography: described using four categories - Ridge (including spurs), Face, Gully,or Terrace.

record as Good (little accumulation of water after rain), Moderate(water accumulates in hollows for several days), or Poor (water standsfor lengthy periods). More refined drainage scales (e.g., Taylor &Pohlen 1962) are available and may be used. They do not, however,overcome the problem that subjective point-in-time assessments ofsoil drainage are difficult to interpret at other than the extreme endsof the scale.

Canopy height: the average height of the dominant canopy species, recorded to thenearest metre. Observer accuracy should be checked regularly byactual measurement, using a rangefinder or a triangulation method(Goulding & Lawrence 1992, Goulding 1995).

Canopydominants: record the species which provide the majority of the canopy cover on

the plot, in order of importance.

16 DESIGNING A SURVEY TO MONITOR POSSUM DAMAGE TO VEGETATION

Browsing: record damage caused by species other than possums as light(< 10% browsed leaves), moderate (11 - 50% foliage browsed), or heavy(> 50% foliage browsed). Record the animal species responsible wherethis can be reliably determined, or state unknown.

Selecting a representative sampleIn selecting the trees to be sampled the two aims are to:

• Choose individuals in a random and independent manner.• Select a sample that is genuinely representative of the species throughout the

area being monitored.

For each species you sample select the individual nearest the plot centre that meetsthe criteria for the selection of sample trees (Section 3.2). All sample trees must bewithin a 20 m radius of the plot centre. Where indicator species are uncommon orhave highly clumped distributions more than one individual may be sampled oneach plot. In this case sample trees must be at least 10 m apart, and data from allindividuals must be combined (i.e., the average calculated) to give a single value foreach indicator species present on the plot.

Tag sample trees at breast height (104m) with the tag and a vertical "permolat" markerfacing the plot centre, and record the direction (degrees) and distance (m) of the treefrom the plot centre. This will make it easier to relocate the tree on future occasions.

2.3.3 Sample sizeIf you select a sample that is too small you may be unable to detect significant changesthat are taking place, and may have difficulties interpreting the results of the survey.Conversely, if you select a sample that is too large for the objectives of your study youwill waste effort, time, and money.

The data we have analysed during the course of our trials indicate that you require asample of 50 permanently marked individuals to reliably detect (with a probability of80%) whether a 10% change in the foliar cover score is statistically significant (P<0.05).When more than one individual of a species is sampled on a plot the individuals donot constitute independent replicates, and cannotbe used to reduce the number of sampleplots required for that species. Where trees are not permanently marked there is a loss

DESIGNING A SURVEY TO MONITOR POSSUM DAMAGE TO VEGETATION 17

of statistical sensitivity through not remeasuring the same individuals. To overcomethis the sample size should be increased to 70 individuals of each indicator species.

Where you are monitoring uncommon species or small patches of forest you may notbe able to find or clearly observe (Section 3.2) 50 individuals of a species. When thepercentage of individuals sampled exceeds c.lO% of the population, a modification tothe standard variance calculations is required. This reduces the width of the confidenceintervals for the estimated parameters (e.g., foliage cover), provides greater precisionfrom the sample, and may allow the sample size to be reduced. The modificationrequires the standard error to be multiplied by the square root of (1- n/N) where n isthe sample size and N represents the estimate of the population size. For example ifthe estimated population size is 100, and 50 of these individuals are sampled, the standarderror will be 70% of the value calculated without this correction. To work out therequired sample size in this situation use the following formula.

New sample size = 50 x N / (N + 50)

where N represents the estimated population size. For the example above this gives arequired sample size of 33.33, or 34 individuals. Where you sample all or nearly allthe individuals in the population, the sample of individuals ceases to be a sample andapproaches or becomes a census. The standard variance calculations required forstatistical comparisons no longer apply as a census determines parameters withoutsampling error. For a more detailed discussion of sampling-related issues see Jongmanet al. (1987) or Underwood (1994).

3 The Foliar Browse Index Method

The Foliar Browse Index method uses plant indicator species to assess the impact ofpossums on forest health and/ or vegetation response to possum control. It can alsobe used to monitor the impact of possum browse on individual (including rare orendangered) plant species. The method uses the assessment/reassessment ofpermanently marked individuals to determine trends in the foliar cover and possumdamage to leaves and stems. It can also be used to monitor canopy damage from awide range of other biotic (e.g., insects) and abiotic (e.g., wind, frost) agents.

We recommend you use two-person teams for establishing or remeasuring FoliarBrowse Index plots. A team can usually complete up to 10 plots along a transect within aday, although this will vary with the number of indicator species chosen and thenature of the terrain. A list of equipment you will need is given in Appendix 2.

3.1 Choice of indicator speciesSeveral factors need to influence your choice of plant indicator species. Whether theobjectives of your study are to monitor changes in forest health or possum damage,indicator species should be:

• Preferred possum food species.• Moderately common and well distributed through the study area.• Readily visible (multi-tiered emergent species such as northern rata are

frequently difficult to observe from the ground).

The use of epiphytes (e.g., mistletoe species) as indicator species is not a problem,provided you have a good pair of binoculars and can devise a method for tagging andrelocating the individuals being monitored (see Section 3.5). Appendix 6 contains alist of commonly used indicator species.

Browsing damage can be more readily distinguished in larger-leaved, shorter species(e.g., fuchsia, pate) than in small-leaved, tall species such as northern rata and totara.Where possums typically remove whole leaves or young shoots (e.g., totara) the severity ofbrowsing but not defoliation tends to be under-estimated or impossible to detect.

Where you suspect factors other than possum browsing (e.g., frost, salt spray, winddamage) are causing damage to forest canopies the use of a non possum-palatableindicator species can help determine the extent to which the observed damage ispossum-related.

THE FOLIAR BROWSE INDEX METHOD 19

3.2 Criteria for selecting sample treesTwo factors are important when selecting individuals to be monitored.

• Trees should have a canopy out of reach of ungulates (> 2m above groundlevel) and a stem diameter of at least Scm.

• The majority of the canopy needs to be clearly visible from ground level andunlikely to be obscured by understorey regrowth. Avoid individuals wherethe canopy is obscured by vines (e.g., supplejack, lawyer, Old Man's Beard) orepiphytes (e.g., Collospermum, Astelia).

3.3 Assessment of sample treesThe Indicator species assessment sheet (Appendix 4) summarises the data to beobtained from each sample tree. Using the format in Appendix 3B, for each tree record:

• Transect/plot number (Column 1), direction (compass bearing) (Column 2)and distance (m) (Column 3) from the plot centre.

• Species (Column 4), using the first three letters from both the generic andspecific names. For example Metrosideros robusta (northern rata) is recorded asMET rob (see Hall 1992).

• Tag number (Column 5) and stem diameter (em) (Column 6), measured atbreast height (104m), 1 em above the tree tag, with the diameter tape at rightangles to the axis of the stem.

• Abundance (Column 7) on and around the plot as Abundant (> 35%individuals), Common (11-35% individuals), Occasional (1-10% individuals),or Rare « 1% individuals). This can be expected to change over time wherepossums are progressively eliminating individuals of preferred food species(Campbell 1984).

• The Tier (Column SA), Emergent (canopy isolated and above that ofneighbouring trees), Canopy (formingpart of the main canopy), or Subcanopy(below the main canopy), and Segment (Column 8B), single Stem or wholeTree. Possums prefer foliage growing in full sunlight (Payton 1988). Inemergenttrees most foliage is exposed to direct sunlight, in canopy trees thisis reduced to the top third of the canopy, and subcanopy individuals usuallyhave little or no sun foliage.

20 THE FOLIAR BROWSE INDEX METHOD

• Foliage cover (Column 9) using the lO-point Foliage Cover Scale (Appendix 5).Wherever possible score foliage cover (and other parameters) from the side of thetree nearest the plot centre. Stand near the base of the tree where you have a goodview of the canopy foliage. Do not include the trunk and major branches, areas ofthe canopy that are dead, or foliage below two metres (i.e., within range of groundbrowsing animals) in your assessment. Using the scale first determine which of 5

broad classes (denoted by horizontal lines) best fits the foliage cover of the wholecanopy. Within that class select the square which most closely resembles the foliagecover of the canopy. From left to right, the columns on the scale represent a moreto less uniform distribution of foliage.

Where a tree canopy can be clearly divided into several discrete segments,each segment may be scored separately. Draw a sketch to enable reliableidentification of the canopy segments when the tree is reassessed, and also toscore the tree canopy as a whole. For analysis purposes canopy segments cannotbe regarded as being independent of each other (see Section 2.3.2).

We recommend that both the Foliage Cover Scale and the Indicator SpeciesAssessment sheets be laminated to reduce wear and tear during fieldwork.

• The extent of dieback in the upper third of the canopy (Column lOT), andthe average for the whole of the canopy (Column lOW). Dieback is theconspicuous presence of dead branches or branchlets, but not recently defoliatedlive twigs. It may be caused by a range of biotic or abiotic agents, includingpossums. Record the presence of dieback only if it is conspicuous, using thefollowing categories:

d

o No dieback1 Light2 Moderate3 Heavy4 SevereX Unable to estimate

affecting <5% of the canopyaffecting 5-25% of the canopyaffecting 26-50% of the canopyaffecting 51-75% of the canopyaffecting >75% of the canopy

Use X only when you cannot see the canopy sufficiently clearly to assess theparameter (e.g., dieback, recovery, browse, use), NOT when you suspect theparameter is present but cannot observe it.


Where dieback has been recorded, determine the extent of any conspicuousrecovery (Column 11) using the following categories:

NR No recovery no visible recoveryFlush of epicormic growth on the trunk and major branches in theU Upper upper of the canopyL Lower lower of the canopyW Whole throughout the whole canopyX Unable to estimate

Epicormic growth refers to the presence of new shoots on the trunk andmajor branches. It does not include twig regrowth which is assessed using theFoliage Cover scale.

• The proportion of possum-browsed leaves (or in the case of small-leaved speciessuch as totara, the severity of possum-related hedging) in the top third of thecanopy (Column 12T), and averaged over the whole canopy (Column 12W) usingthe following categories:

o Nil0.5 Some1 Light2 Moderate3 Heavy4 SevereX Unable to estimate

no possum-related hedging no browsed leavesminimal hedging <5% leaves browsedlightly hedged 5-25% leaves browsedmoderately hedged 26-50% leaves browsedheavily hedged 51-75% leaves browsedseverely hedged >75% leaves browsed

Scoring browse in canopy and emergent trees requires a good pair of binocularsand an ability to distinguish possum damage to leaves from that caused by insectsand other agents (e.g., wind, frost). For most indicator species possum-browsedleaves are characterised by torn edges and jagged leaf stubbs (Fig. 1). In speciessuch as five finger, mountain five finger and pate where possums may eat onlythe fleshy base of the leaf petiole, look for a carpet of freshly discarded leaves asevidence of possum foraging. Insect damage typically consists of holes and wavy,clean-edged patterns (caterpillars) or straight, finely milled edges (stick insects)(Fig.1, Meads 1976).

For totara, where the leaves are small and needle-like and browse is difficult todistinguish, hedging is used as a measure of browsing damage. Look for a hedged,

£ Q


Insect Damage

Northern Rata

Possum Damage

Kamahi

Mahoe

,

Tawa

Hinau

Figure 1. Possum and insect-damaged leaves ofnorthern rata, kamahi, mahoe, tawa and hinau.

THE FOLIAR BROWSE INDEX METHOD

windshorn appearance to the canopy on the leeward side of the tree and in treeson sheltered sites. Do not take into account hedging that occurs only on the exposedwindward side of trees. Where possum-induced hedging is heavy to severe few ofthe current season's light green shoots will remain, and the canopy will take on thedull green coloration characteristic of older totara leaves.

• Recent possum use of the lower 2 m of the trunk or stem (Column 13) using thefollowing categories:

o Nil1 Light2 Moderate3 Heavy

X Unable to estimate

no scratching or bite marks on the trunkoccasional scratch and bite marksnumerous clearly defined scratch and bite marksbark worn smooth and evidence of a welldeveloped possum "run".trunk obscured by epiphytes or moss.

0 Nil1 Rare

2 Occasional

3 Common4 Abundant

Evidence for possum use of trunks or stems typically takes the form of scratchingand bite marks. It is most readily visible on indicator species with soft, lightcoloured bark (e.g., mahoe), but may be difficult or impossible to detect wherestems are covered in vines (e.g., climbing rata) or where indicator species havehard (e.g., haumakaroa) or flaky (e.g., fuchsia) bark.

• The presence and abundance of flowers (Column 14A) and fruit (Column 14B).Flowering includes the presence of flower buds, and fruiting the presence of currentseason's seed capsules. Be sure you know how to recognise the flowers / fruit ofthe indicator species you have chosen and the period ofthe year over which flowering /fruiting occurs. Locally, flowering and fruiting peaks are frequently short (days,weeks) events. A list of flowering / fruiting periods for a range of plant indicatorspecies is given in Appendix 6. Use the following categories:

no flowers or fruit visiblefew flowers or fruit visible, often only in part ofthe canopysparse flowering / fruiting, usually throughoutthe canopyflowers or fruits common throughout the canopyflowering / fruiting heavy, highly visible, andpresent on most branchlets.

n


Flowering and fruiting are assessed because of the impact possums are suspected ofhaving on this food source for native bird species, and the regeneration potential ofspecies such as hinau (Cowan & Waddington 1990), kohekohe, supplejack, tawa(Atkinson 1985, 1992), mistletoe (Wilson 1984), and nikau (Cowan 1991).

Interpretation of data on flowering and fruiting is not straightforward. Possumbrowsing is not the only reason for poor flowering and fruiting. Rats and bird speciessuch as kereru can consume large amounts of fruit and seed (Atkinson 1992), andclimatic conditions may also contribute to variability in the data. For example, atCraigieburn in inland Canterbury; what would have been scored as abundant mistletoe(Peraxilla tetrapetala) flowering on 18 January 1995 rated only an occasional or commonscore the following week, after a southerly storm stripped many of the flowers fromthe plants.

Intermittent (often referred to as mast) fruiting or seeding, which is a characteristic ofmany potential indicator species, can also lead to large differences in flowering / fruitingbetween years, between areas, and between individuals within the same population.Despite these difficulties, records of flowering / fruiting from permanently markedindividuals can provide useful data for control vs. non-control comparisons(Pekelharing 1996), and to ascertain whether residual possum numbers are sufficientlylow to allow the production of viable seed.

3.4 Emergent spedesEmergent tree species are not ideal candidates for a ground-based scoring systemsuch as the Foliar Browse Index method. Their canopies are usually multi-tiered anddifficult to observe from ground level. They frequently have small leaves which makesit difficult to assess browsing damage, and they are often only present as isolatedindividuals. Nevertheless emergent species are conspicuous components of a widerange of native forest ecosystems and in some cases (e.g., northern rata, totara) arevulnerable to possum damage.

Unless an emergent indicator species is common in the study area, it is may bedifficult to get an adequate sample (Section 2.3.3) that meets the criteria forsample trees (Section 3.2) using the transect method described in Section 2.3.2. Inmost instances you will need to assess emergent tree species from a series ofsubjectively chosen observation points. You should locate these, as far as possible,

..

•


throughout the study area (Section 2.3.2). Treat each observation point as a transectcontaining between one and three plots. Where more than one plot is scored froman observation point, all plots must be a minimum of 100m apart to ensure theirindependence.

For a prominent tree at each observation point record• Species (Column 4), tag number (Column 5) and stem diameter (Column 6) of

the marker tree.

For each emergent tree record• Direction (compass bearing) (Column 2) and distance (m) (Column 3) from the

marker tree.

• Species (Column 4). Tag number (Column 5) and stem diameter (Column 6),where it is possible to get to the tree being assessed.

• Abundance (Column 7), tier (Column 8A), and segment (Column 8B) using thefollowing categories:

U upper third of the canopyM middle third of the canopyL lower third of the canopyW whole canopy

Draw a sketch or use a photograph to ensure each segment can be reliablyrelocated on future occasions, and also score the tree canopy as a whole (seeSection 3.3).

• Other vegetation parameters (Columns 9-14) as described in Section 3.3. Scoredieback (top-third), recovery, browse (top-third), and stem use for the whole treeonly (Appendix 3B).

3.5 EpiphytesEpiphytes are not inherently difficult to assess using the Foliar Browse Index method,provided they are clearly visible from a relocatable vantage point. As for canopyand emergent trees a good pair of binoculars is essential. Treat each host tree as asingle plot.

5 &


For the host tree record• Plot number (Column 1), direction (compass bearing) (Column 2) and distance

(m) (Column 3) from the plot centre.

• Species (Column 4), tag number (Column 5) and stem diameter (Column 6).

For each epiphyte (e.g., mistletoe) record:• Species (Column 4), and size of the plant (Column 6) using the following

categories

XS extra smallS smallM mediumL largeXL extra large

< 1/2m wide1/2-1 m wide1-2mwide2-3m wide>3m wide

• Abundance (Column 7), tier (Column 8A) as Epiphyte, and position on the hosttree (Column 8B) using the following categories:

B lower third of the host tree canopyM middle third of the host tree canopyU upper third of the host tree canopy

T epiphyte attached to the main trunkI epiphyte on the inner branchesa epiphyte on the outer branches

• Other vegetation parameters (Columns 9-14) as described in Section 3.3. Dieback(top-third), recovery, browse (top-third) and stem use will usually only be applicablefor large or extra large individuals (Appendix 3B).

3.6 Reassessment of sample treesRegular (annual, biennial etc) reassessment of Foliar Browse Index plots is notnecessary. However, because many of the parameters vary seasonally, plots shouldbe reassessed in the same month as earlier assessments (see Section 4.2). Based onexperience with other types of permanent plots (e.g., 20x20m forest plots), tree tagsand "permolat" markers need to be checked every few years to ensure that they havenot become overgrown (Allen 1993).

s


For each transect and plot, record the parameters set out in Section 2.3.2. This providesan update of any changes to the physical environment, and will hopefully improvethe ease with which future survey parties can relocate the vegetation plots.

For each tree being reassessed follow the procedures set out in Section 3.3.• Take a copy of species location data (Columns 1-5), to help locate the trees to be

reassessed. Do not take a copy of the vegetation data (Columns 6-14) from theprevious assessment, as this is likely to influence your assessment of parameterssuch as foliage cover, browse and stem use.

• Where trees have been completely defoliated since the previous'assessment checkthey have died by cutting the bark with a knife to determine whether sap is stillpresent and the cambium intact. Supposedly dead trees have been known to springback to life.

• Where a tagged tree is alive but completely defoliated, or has died, record thefoliage cover (Column 9) as:

o Completely defoliated living tree No leaves remaining.Bark is intact and contains sap.

1 Recently dead Fine twigs present.Bark intact but no sap present.

2 Long dead Fine twigs absent. Most largerbranches remain. Bark not intact.

Remove the tag from trees recorded as long dead.

• Where trees have died they may be replaced by another individual from the sameplot. In this case select the live individual that is closest to the plot centre. Notethat the replacement of dead trees will distort analyses of changes betweensampling periods (see Section 5). If tree mortality from possum damage or othercauses (e.g., windthrow) is severely reducing the size of your sample, select anothersample ofthe species (see Section 2.3) and treat this as a separate indicator species.

4 Technical Considerations

Because many of the components of the Foliar Browse Index method rely on individualassessment of a categorical variable rather than on counts or measurements, questions ofreliability and repeatability take on an increased importance (Strand 1996).

4.1 Observer variabilityAs part of the development of the method we tested variation between observersfor the assessed (as opposed to measured) variables (see Section 3.3) on theFoliar Browse Index plot sheet (Appendix 3B). Testing was carried out at 6 sitesusing indicator species from emergent (northern rata, totara), canopy(kohekohe, mahoe, pohutukawa, taro, towai), and subcanopy (five finger, heketara,pate) forest tiers.

Where 2 observers independently assessed indicator species abundance (Column 7),they agreed on 94% of occasions (n=676) that the species was either common, occasionalor rare on the plot (Fig. 2A). For tier height (Column 8) observers independentlyreached the same conclusion (emergent, canopy or subcanopy) on 79% (n=676) ofoccasions (Fig. 2B). Two-thirds of the disagreements related to whether the tree orstem was in the canopy or subcanopy tier.

Both observers agreed on the foliage cover score (Column 9) on 42% of occasions(n=707), were not more than one class (10%) different 85% of the time or two classes(20%) different 95% of the time (Fig. 2C). Variation between observers was lowestwhen foliage cover scores were high (> 75%) or low « 15%).

Observers agreed on the presence / absence of dieback (Column 10) in 83% (n=670) ofcases (Fig. 2D). Where dieback was recorded as being present (n=252), observers agreedon its' severity (light, moderate, heavy or severe) on 72% of occasions. Most instancesof disagreement related to whether dieback should be classed as light (affecting 525% of the canopy) or moderate (affecting 26-50% of the canopy). For trees affected bydieback, both observers agreed on the presence or absence of recovery (Column 11)(prominent epicormic shoots on the trunk and major branches) on 70% of occasions(Fig.2E).

Agreement on the proportion of possum-browsed leaves or possum-related hedging(Column 12) was obtained in 73% (n=432) of cases (Fig. 2F). Where both observerswere able to score damage, less than 3% of cases varied by more than 1 browse class.

TECHNICAL CONSIDERATIONS 29

O.L----

Fig2A

100 Agree

~

c 800

~2:~ 60-"0OJ

H 40cOJ~

20OJ0..

0

n ~ 676

Fig2B

100

80

60

40

20

n ~ 676

Agree

Abundance (Column 7) Tier (Column 8)

Fig2C 100

~

c 800

~>~ 60-"0OJ

H 40cOJ~

20OJ0..

o

n = 707

o 10 20 30 40

Foliage cover - percentage variation (Column 9)

Fig2D

100 n = 670 100 n ~ 252Agree

~

c 80 80 Agree0

~>~ 60 60-"0OJbJJ 40 40.r'lcOJ~

20 20OJ0..

0 0

Dieback - presence/absence (Column 10) Dieback - severity (Column 10)

Figure 2 Variation between observers for assessed variables in the Foliar Browse Index methodology

30 TECHNICAL CONSIDERATIONS

Figure 2 Variation between observers for assessed variables in the Foliar Browse Index methodology

-TECHNICAL CONSIDERATIONS 31

The main source of disagreement was between observers who were unable to estimatebrowse and those who decided there were no possum-browsed leaves or possumrelated hedging.

Assessment of possum damage to trunk or stem (Column 13) followed a similarpattern with 57% (n=434) of cases agreeing on the damage class, and less than 9%differing by more than 1 damage class (Fig. 2G). As with browsing the main source ofvariation was between the unable-to-estimate and no-damage classes.

Observer variability of flowering (Column 14A) and fruiting (Column 14B) was nottested, but can be expected to follow the pattern of variables such as browse or trunkdamage which have a similar number of categories.

Some observers did consistently assess variables above or below the mean valueobtained by a group of observers. The ideal means of ensuring consistency is to usethe same observer throughout (Meads 1976, Payton 1988). This is rarely practical,except where datasets are small and timeframes short. To minimise the possibility ofsystematic bias in Foliar Browse Index datasets, and to maximise consistency betweensampling periods we recommend that:

• Both members of a two-person teambe required to agree on the score of assessedvariables.

• Where there is more than one team, personnel are rotated between teams.• At least one member of each team should be present for successive

remeasurements.

4.2 Seasonal variabilityFoliage cover, flowering and fruit production (e.g., Burrows 1994, O'Donnell & Dilks1994), and possum browsing (e.g., Fitzgerald 1976, Coleman et al. 1985) varyseasonally. The optimal sampling time will depend on the objectives of the studyand the choice of indicator species. Foliage cover tends to peak in mid-late summerwhen leaf expansion has been completed (Fig. 3A) and possum damage is usuallymost visible during winter and spring, before it is masked by the new season's leafgrowth (Fig. 3B). Possum damage to stems did not show a seasonal pattern (Fig.3C). Flowering and fruiting times for a range of indicator species are given inAppendix 6. They are based on available reports and publications and will varybetween regions.


Fig3A

60

0····-0 pokaka (n ~ 50)0- -0 mountain fivefinger (n ~ 26)

- haumakaroa (n ~ 40)

a

0- - 0.

"'0- ..0

..-0 -0 .00

0 ..

/0-. ····0·····-0

/o

'0-0- 0 ......0--0'/

~

~ 40

.&oLL 20

0

Fig 383.0

L 2.0<li

~ea:l

1.0

Fig3C1.5

1.0~::J

Et;

0.5

A 0 D

1993

F A J A 0 0

1994

A J A 0

1995

Figure 3 Seasonal variation in foliage cover and possum browsing of three plant indicator species,Alfred River, Maruia. August 1993 - October 1995. (*see Section 3.3 for an explanation ofbrowse andstem use scales).

----------------TECHNICAL CONSIDERATIONS 33

Avoid periods of rapid leaf growth or foliage loss. This is especially important fordeciduous species such as fuchsia and wineberry, which at higher altitudes and insouthern latitudes may only be able to be monitored over a 3-4 month period in midlate summer.

Because seasonal changes may mask annual trends, reassessment of sampletrees (Section 3.6) should be carried out in the same month as previousassessments. If during the course of a long-running study it becomes necessary tochange the timing of the data collection, trees should be assessed at both theold and new times in the year the change is made. This will allow you todetermine the extent to which the change in the time of data collection is affectingthe results.

4.3 Background noisePersistent possum browsing defoliates tree canopies and leads to the death of someindividual trees. Possums, however, are not the only cause of damage to native treespecies. A wide range of biotic (e.g.,fungi, insects) and abiotic (e.g., drought, flooding,frost, salt spray, wind) agents have been reported as damaging or killing trees inNew Zealand forest ecosystems (Wardle 1991).

1r~

To determine the extent to which foliar cover estimates might be expected to varyfrom causes other than possum browsing we assessed annual changes over 4years in three indicator species (kohekohe, mahoe, pohutukawa) on possum-freeWaiheke Island in the Hauraki Gulf, and at two mainland North Island sites(Cape Brett, Northland and Te Tapui Scenic Reserve, Waikato) where non-controlledpossum populations were present (Fig. 4). In the absence of possum browsing,foliage cover estimates for these species changed by up to 15% betweenannual assessments. At sites where possums were present, annual variation infoliage cover was either similar to (Cape Brett) or greater than (Te Tapui ScenicReserve) that at the possum-free site. These results demonstrate that possumbrowsing is not the only factor influencing changes in foliage cover. To attributechanges in foliage cover to possum activity they need to be supported by evidenceof possum browsing.

For kohekohe and pohutukawa, but not mahoe, foliage cover values on Waiheke Islandwere significantly greater (P<O.OOl) than those at mainland sites.

ri


Kohekohe 80 Waiheke (n~49) Te Tapui (n~50) Cape Brett (n~50)

60g

">0 40u

'"b,()

.!!!0LL

20

092 93 94 95 92 93 94 95 92 93 94 95

Pohutukawa 80 (n~49) (n~51)

60

~~

'"> 400u

'"b,()

.!!!0 20LL

092 93 94 95 92 93 94 95

Mahoe 80(n~50) (n~48)

60

~

"> 400u

'".~0 20LL

092 93 94 95 92 93 94 95

Year

Figure 4. Annual changes in foliage cover for three plant indicator species on possum-free WaihekeIsland, and at two mainland North Island sites where possums are present.

TECHNICAL CONSIDERATIONS 35

4.4 Relationship between foliage cover and leaf biomassOne of the questions frequently asked about indicies such as foliage cover is how theyrelate to actual measurable quantities, in this case leaf biomass. We tested therelationship between foliage cover and leaf biomass using subcanopy individuals oftwo indicator species (mahoe, pate) at Granville Forest, Westland. Trees of each specieswere assessed for foliage cover by one observer, partially defoliated by a secondobserver, and reassessed for foliage cover by the first observer. They were then totallydefoliated and the foliage from both defoliations dried, and weighed.

For both species there was a significant linear relationship (mahoe, P<O.OO1; pate,P<O.Ol) between foliage cover (assessed using the foliage cover scale) and foliagebiomass, for foliage cover scores below 60 percent (Fig. 5A). There was also a significantlinear relationship (both species, P<O.OOl) between percentage changes in foliage coverand dry weight (Fig. 58).

While it would be unwise to extrapolate these findings to all plant indicatorspecies, the results do indicate that estimates of foliage cover can provide a reliableindex of foliage biomass. We would expect this relationship to hold best for indicatorspecies with relatively few leaf layers, and to be least reliable for multi-tiered (e.g.,emergent) species.


Fig5A Mahoe Pate

200 n ~ 21 • 200 n ~ 22

:§ 150 150

'".~ •:2 •..... 100 • 100.J::

b.O.;;; • •:;: •>. 50 50[)

•0 0

5 25 45 65 0 15 35 55

Foliage cover score (%) Foliage cover score (%)

Fig 58

100 n = 21 • 100 n ~ 22

~ •• 0

'" 75 75.~ •• • 0

:2 0 0

~ •C

50 50 0 0

• • 0""0 • • 0°0

.s0 00

'" 25 • .- 25b.O 8 0c •'".J:: 0U

0 00 25 50 75 100 0 25 50 75 100

Change in foliage cover score (%) Change in foliage cover score (%)

FigureS. Relationship between foliage cover score and foliage biomass in two plant indicator species(mahoe, pate), Granville Forest, Westland.

37

5 Data Analysis Options

Foliar Browse Index data are amenable to display and analysis by a wide range ofstandard graphics and statistics packages. The following section outlines analysesthat can be used to determine the significance of changes occurring within andbetween populations of an indicator species, and relationships between Foliar BrowseIndex parameters. It assumes that the data are a random, representative sample ofthe individuals in the stratum or study area (see Section 2.3.2).

5.1 Data classificationThe Foliar Browse Index parameters can be classified as:

• Numerical data (stem diameter).

• Numerical class score data- with equal interval classes (foliage cover)- without equal interval classes (browse, dieback, stem use, flowers, fruit).The browse and dieback parameters can be viewed as having equal intervalclasses if the nil-light categories are combined.

• Nominal (alphabetic) class score data (abundance, tier, segment, recovery,epiphyte diameter).

5.2 Data transformationNumerical data

• No transformation required for statistical analyses.

Numerical class score data with equal interval classes• Delete foliage cover values of 1 and 2 (dead trees) if analyses are to include

only living trees, or replace by 0 if the analyses are to include both living anddead trees. Unless your analysis is comparing changes in mortality, werecommend using only data from living trees.

Numerical class score data without equal interval classes• Remove X (unable to estimate) values.• For chi-squared tests replace non-zero class scores (e.g., 1,2,3 ... ) with a

value of 1.

Nominal (Alphabetic) class score data• Remove X (unable to estimate) values.

..,Ii

38 DATA ANALYSIS OPTIONS

• Convert to a numeric scale to produce frequency summaries.• For chi-squared tests replace non-zero class scores (e.g., 1,2,3 ... ) with a

value of 1.

5.3 Data analyses5.3.1 Statistical testsStatistical tests that can be used to compare changes between sampling periods andbetween sampled areas are outlined in Table 1. The list is not exhaustive. Rather it isintended as a guide, to help non-statisticians identify appropriate statistical proceduresfor each of the Foliar Browse Index parameters.

5.3.2 CorrelationWhen correlating Foliar Browse Index parameters (e.g., foliage cover and browse)use Pearson's correlation coefficient for parameters with equal interval classes, andSpearman's non-parametric correlation coefficient for parameters without equalinterval classes.

Table 1. Statistical tests for use with Foliar Browse Index data

Statistical comparison Data classification

No. of Numerical Numerical class score Nominal (Alphabetic)groups class score

Equal interval Unequal intervalclasses classes

Between 2 Paired t-test Wilcoxon signed rank McNemar'ssampling periods (1) Chi-square test

>2 Repeated measures Freidman'sANOVA non-parametric ANOVA

Polynomial regression(eg. linear)

Between 2 Independent t-test Mann-Whitney U-Test Chi-square testsampled areas (2)

>2 One-way ANOVA Kruskal-Wallis Chi-square testnon-parametric ANOVA

(1) Same group of individuals sampled on each occasion(2) Independent groups of trees

o?:1»»~!:<:V\Vio~6zV\

w

'"

6 Data Storage

a

Routine archiving of data is an essential part of any monitoring system. In the currentconstantly changing employment environment, we need to plan for the possibilitythat Foliar Browse Index plots will be remeasured by people who were not involvedwith their establishment or earlier remeasurement. Because data from previoussampling periods form part of a time-series, it is not possible to recover data that havebeen lost.

For these reasons it is important to ensure that a copy of all Foliar Browse Indexplotsheets, maps, aerial photos, location diagrams, and computer datafiles is archivedin secure, preferably fire-proof storage. Unless this procedure becomes an integralpart of a monitoring programme, datasets will rarely outlive the employment tenureof the person responsible for their collection.

Landcare Research, in conjunction with the Department of Conservation and otherexternal agencies, holds and manages the National Vegetation Survey database. Thisincorporates data from indigenous vegetation surveys carried out by the former NewZealand Forest Service (now Department of Conservation and Landcare Research),the National Forest Survey (1946-55), and the Protected Natural Areas Programme.The database, which is located at Lincoln near Christchurch, comprises hardcopy,computerised datafiles and analysis software (Hall 1992, 1994a,b) for a range ofstandardised monitoring methodologies. Persons wishing to make use of this facilityshould contact Dr Ian Payton at Landcare Research, PO Box 69, Lincoln.

41

7 Acknowledgements

A wide range of people contributed to the development of the Foliar Browse Indexmethodology. The project was conceived by Ian Payton, Chris Frampton and RichardSadleir. Case Pekelharing took primary responsibility for method development andfield testing. Chris Frampton devised the sampling strategy and determined the dataanalysis options. Ian Payton managed the project and wrote the manual.

We express our appreciation to Hamish Owen, Mark Smale, Phil Suisted and otherDepartment of Conservation and Landcare Research staff who helped test the method.Ngaire Bruerton computerised the datasets, Robert Lamberts took the photographs,Megan Ogle-Mannering, Geoff Rogers and Mark Smale critically reviewed themanuscript, Thomas Pearson and Kirsty Cullen drew the figures, and Kirsty Cullenprepared the manual for publication. Development of the method and publication ofthe manual were funded by the Department of Conservation.

r8 References

Allan, B.H. 1961. Flora ofNew Zealand. Vol. 1. Government Printer, Wellington.1085 p.

Allen, RB. 1992. RECCE: an inventory method for describing New Zealandvegetation. Ministry of Forestry, Christchurch. Forest Research Institute bulletinno. 181. 25 p.

Allen, RB. 1993. A permanent plot method for monitoring changes in indigenousforests. Manaaki Whenua - Landcare Research Ltd, Christchurch. 35 p.

Atkinson, LA.E. 1985. Botanical reasons for eradicating possums from KapitiIsland. Botany Division, DSIR, internal report no. 518. 16 p.

Atkinson, LA.E. 1992. Effects of possums on the vegetation of Kapiti Island andchanges following possum eradication. DSIR Land Resources contract reportNo. 92/52 (unpub!.). 68 p.

Avery, T.E. 1966. Foresters guide to aerial photo interpretation. U.S. Department ofAgriculture, Forest Service, Agricultural Handbook No.304, Washington D.C., U.S.A.

Batcheler, c.L. 1983. The possum and rata-kamahi dieback in New Zealand. Pacificscience 37: 415-426.

Batcheler, CL, Cowan, P.E. 1988. Review of the status of the possum (Trichosurusvulpecula) in New Zealand. Contract report commissioned by the TechnicalAdvisory Committee (Animal Pests) for the Agricultural Pests DestructionCouncil, Department of conservation and MAFQual, Ministry of Agricultureand fisheries (unpub!.). 129 p.

Beadel, S.M. 1987. Assessment of vegetation condition using permanentphotographic points, Horomanga Catchment, Urewera National Park. RegionalReport Series No.2. Department of Conservation, Rotorua, 48 p.

Best, B.A., Bellingham, P.I. 1991. A detailed habitat study of North Island kokakoin Puketi Forest, Northland. Science and Research Internal Report No. 103.Department of Conservation, Wellington. 371 p.

Brockie, R. 1992. A living New Zealand forest. David Bateman, Auckland. 172 p.

Brown, K., Innes, J., Shorten, R 1993. Evidence that possums prey on and scavengebird's eggs, birds and mammals. Notornis 40: 169-77.

Burrows, c.J. 1994. Seed trapping in Ahuriri Summit Bush Scenic Reserve, PortHills, western Banks Peninsula, 1985-86. New Zealand journal ofbotany 32: 183-215.

Campbell, D.J. 1984. The vascular flora of the DSIR study area, lower OrongorongoValley, Wellington, New Zealand. New Zealand journal ofbotany 22: 223-270.

REFERENCES 43

Chan, S.S., McCreight, R.w., Walstad, J.D., Spies, T.A. 1986. Evaluating forestvegetative cover with computerised analysis of fisheye photographs. Forestscience 32: 1085-1091.

Coleman, J.D., Green, W.Q., Polson, J.G. 1985. Diet of brushtail possumsover apasture-alpine gradient in Westland, New Zealand. New Zealand journal ofecology 8: 21-35.

Connor, H.E., Edgar, E. 1987. Name changes in the indigenous New Zealand Flora,1960-1986 and Nomina Nova IV, 1983-1986. New Zealand journal ofbotany 25:115-170.

Cowan, P.E. 1990. Brushtail possum. In King, CM. (ed.), The Handbook ofNewZealand Mammals, pp. 67-98. Oxford University Press, Auckland.

Cowan, P.E. 1991. Effects of introduced Australian brushtail possums (Trichosurusvulpecula) on the fruiting of the endemic New Zealand nikau palm (Rhopalostylissapida). New Zealand journal ofbotany 29: 91-93.

Cowan, EE., Moeed, A. 1987. Invertebrates in the diet of brushtail possums,Trichosurus vulpecula, in lowland podocarp /broadleaf forest, OrongorongoValley, Wellington. New Zealand journal ofzoology 14: 163-177.

Cowan, P.E., Waddington, D.C 1990. Suppression of fruit production of theendemic forest tree, Elaeocarpus dentatus, by introduced marsupial brushtailpossums, Trichosurus vulpecula. New Zealand journal ofbotany 28: 217-224.

Dickinson K.J.M., Mark, A.F., Lee, W.G. 1992. Long-term monitoring of non-forestcommunities for biological conservation. New Zealand journal ofbotany 30: 163-179.

Fitzgerald, A.E. 1976. Diet of the opossum Trichosurus vulpecula (Kerr) in theOrongorongo Valley, Wellington, New Zealand, in relation to food-plantavailability. New Zealand journal ofzoology 3: 399-419.

Fox, L.R., Morrow, EA. 1983. Estimates of damage by herbivorous insects onEucalyptus trees. Australian journal ofecology 8: 139-147.

Goulding, CJ. 1995. Measurement of trees. In Hammond, D. (ed.), ForestryHandbook, pp.104-107. The New Zealand Institute of Forestry, Christchurch.

Goulding, CJ., Lawrence, M.E. 1992. Inventory practice for managed forests.Ministry of Forestry, Rotorua. Forest Research Institute bulletin 171. 52 p.

Green, W.Q. 1984. A review of ecological studies relevant to management of thecommon brushtail possum. In Smith, A.P. & Hume, LD. (eds.), Possums andgliders, pp. 483-99. Surrey Beatty & Sons, Chipping Norton, NSW.

44 REFERENCES

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Hall, G.M. 1994a. PC-DIAM: stem diameter data analysis. Manaaki WhenuaLandcare Research Ltd, Lincoln. 88 p.

Hall, GM. 1994b. PC-USTOREY: seedling and sapling data analysis. ManaakiWhenua - Landcare Research Ltd, Lincoln. 44 p.

Hosking, Gp. 1995. Evaluation of forest canopy damage using airbornevideophotography. New Zealand forestry 40: 24-27.

Hosking, GP., Firth, I.G, Brownlie, RK., Shaw, W.B. 1992. Airbornevideophotography evaluation in New Zealand: opening a present from UncleSam. Proceedings, Resource Technology 92 Conference 5: 137-144. Washington D.C.,U.S.A.

Hunt J., Hollinger, D. 1988. Understorey light environment in a mountain beechforest. Ministry of Forestry, Christchurch. Forest Research Institute contractreport (unpub!.). 9 p.

Hunter, LR, Rodgers, B.B., Durringham, A., Price, J.M., Thorn, A.J. 1991. An atlasof radiata pine nutrition in New Zealand. Ministry of Forestry, Rotorua. ForestResearch Institute bulletin 165. 24 p.

Innes, J.L. 1988. Forest health surveys: problems in assessing observer objectivity.Canadian journal offorestry research 18: 560-565.

Innes, J.L. 1990. Assessment of tree condition. Forestry Commission Field Book 12.96 p.

Innes, J.L., Boswell, RC. 1990. Reliability, presentation, and relationships amongdata from inventories of forest condition. Canadian journal offorestry research20:790-799.

Jongman, RH.G, ter Braak, C.J.E, van Tongeren, O.ER 1987. Data analysis incommunity and landscape ecology; Wageningen, Pudoc. 299 p.

Kean, RL, Pracy, 1. 1953. Effects of the Australian opossum (Trichosurus vulpeculaKerr) on indigenous vegetation in New Zealand. Proceedings 7th Pacific ScienceCongress 4: 1-8.

Kirk, H.B. 1920. Opossums in New Zealand. In Appendix H-28 to the Journals ofthe House of Representatives, pp 1-12. Government Printer, Wellington.

Landsberg, J. 1989. A comparison of methods for assessing defoliation, tested oneucalypt trees. Australian journal ofecology 14: 423-440.

REFERENCES 45

Lasko, AN. 1980. Correlations of fisheye photography to canopy structure, lightclimate, and biological responses to light in apple trees. Journal of the AmericanSociety ofHorticultural Science 105: 43-46.

Leathwick, J.R. 1984. Phenology of some common trees, shrubs, and lianes in fourcentral North Island forests. New Zealand Forest Service, Rotorua. ForestResearch Institute bulletin 72. 46 p.

Leathwick, J.R., Hay; J.R., Fitzgerald, AE. 1983. The influence of browsing byintroduced mammals on the decline of North Island kokako. New Zealandjournal ofecology 6: 55-70.

Leutert, A 1988. Mortality, foliage loss, and possum browsing in southern rata(Metrosideros umbellata) in Westland, New Zealand. New Zealand journal ofbotany26: 7-20.

Meads, M.J. 1976. Effects of opossum browsing on northern rata trees in theOrongorongo Valley, Wellington, New Zealand. New Zealand journal ofzoology 3:127-39.

Moore, L.B., Edgar, E. 1970. Flora of New Zealand. Vol. 2. Government Printer,Wellington. 354 p.

O'Donnell, c.F.J., Dilks, P.J. 1994. Foods and foraging of forest birds in temperaterainforest, South Westland, New Zealand. New Zealand journal ofecology 18:87-107.

Park, G.N. 1973. Point height intercept analysis: a refinement of point analysis forstructural quantification of low arboreal vegetation. New Zealand journal ofbotany 11: 103-114.

Payton, LJ. 1987. Canopy dieback in the rata (Metrosideros)-kamahi (Weinmannia)forests of Westland, New Zealand. In Fujimori, T. & Kimura, M. (eds.), Humanimpacts and management ofmountain forests, Proceedings 4th IUFRO WorkshopPl.07-00, 1987, pp 123-36. Forestry and Forest Products Research Institute,Ibaraki, Japan.

Payton, LJ. 1988. Canopy closure, a factor in rata (Metrosideros) - kamahi(Weinmannia) forest dieback in Westland, New Zealand. New Zealand journal ofecology 11: 39-50.

Payton, LJ. 1990. Methodology for vegetation monitoring in relation to possumcontrol. Ministry of Forestry, Christchurch. Forest Research Institute contractreport FWE 90/33 (unpubl.) 13 p.

46 REFERENCES

Payton, I.J., Forester, L., Frampton, CM., Thomas, M.D. 1997. Response of selectedtree species to culling of introduced Australian brushtail possums (Trichosurusvulpecula) at Waipoua Forest, Northland, New Zealand. Biological Conservation81: 247-255.

Pekelharing, CJ. 1996. Response of some indicator plant species in Otari nativebotanic garden, Wellington, following possum control. Proceedings of aworkshop organised by the Possum and Bovine Tuberculosis Control NationalScience Strategy Committee, 25-26 October 1995, Wellington. The Royal Society ofNew Zealand miscellaneous series 35: 4-6.

Pekelharing, CJ., Reynolds R.N. 1983. Distribution and abundance of browsingmammals in Westland National Park in 1978, and some observations on theirimpact on the vegetation. New Zealand journal offorestry science 13: 247-65.

Pekelharing, CJ., Batcheler, CL. 1990. The effect of control of brushtail possums(Trichosurus vulpecula) on condition of a southern rata/kamahi (Metrosiderosumbellata/Weinmannia racemosa) forest canopy in Westland, New Zealand. NewZealand journal ofecology 13: 73-82.

Pracy, L. T 1974. Introduction and liberation of the opossum (Trichosurus vulpecula)into New Zealand. Information Series No. 45. New Zealand Forest Service,Wellington. 28 p.

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Rich, P.M. 1989. A manual for analysis of hemispherical canopy. Manual LA-1l733M. Los Alamos National Laboratory, Los Alamos, New Mexico, U.S.A

Rose, AB., Pekelharing, CJ., Hall, G.M. 1988. Forest dieback and the impact ofbrushtail possums in the Otira, Deception, and Taramakau catchments,Westland. Ministry of Forestry, Christchurch. Forest Research Institute contractreport (unpubl.). 27 p.

Rose, A.B., Pekelharing, CJ., Platt, K.H. 1992. Magnitude of canopy dieback andimplications for conservation of southern rata-kamahi (Metrosideros umbellata Weinmannia racemosa) forests, central Westland, New Zealand. New Zealandjournal ofecology 16: 23-32.

Rose, A.B., Pekelharing, CL Platt, K.H., Woolmore, CB. 1993. Impact of invadingbrushtail possum populations on mixed beech-broadleaved forests, SouthWestland, New Zealand. New Zealand journal ofecology 17: 19-28.

REFERENCES 47

Rose, AB., Platt, K.B., PekeIharing, CJ., Moore, TJ., Suisted, P., Savage, T.J. 1995.Forest dieback and the impact of possums in Nelson/MarlboroughConservancy. Manaaki Whenua - Landcare Research, Lincoln. contract reportLC9596/12 (unpubl.). 42 p.

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Scott, D. 1965. A height frequency method for sampling tussock and shrubvegetation. New Zealand journal ofbotany 3: 253-260.

Strand, G-H. 1996. Detection of observer bias in ongoing forest health monitoringprogrammes. Canadian journal offorestry research 26: 1692-1696.

Stewart, G.B., Johnson, P.N., Mark, A.F.1989. Monitoring terrestrial vegetation forbiological conservation. In Craig, B. (ed.), Proceedings ofa symposium on .environmental monitoring in New Zealand with emphasis on Protected Natural Areas,pp 199-208. Department of Conservation, Wellington.

Taylor, N.H., PoWen, I.I. 1962. Soil survey method. Soil Bureau bulletin 25. 242 p.

Trotter, CM. 1992. Estimating possum browse damage to lowland indigenousforest using remote sensing - a feasibility study. Proceedings of the 6thAustralasian Conference on Remote Sensing 1: 400-405.

Trotter, CM., Brown, L.J. 1999. Monitoring forest canopy condition by remotesensing. Department ofConservation technical series 14. 44 p.

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Veblen, TT, Stewart, G.H. 1982. On the conifer regeneration gap in New Zealand:the dynamics of Libocedrus bidwillii stands on South Island. Journal ofecology 70:413-436.

Wardle, J. 1974. Influence of introduced mammals on the forest and shrublands ofthe Grey River headwaters. New Zealand journal offorestry science 4: 459-86.

Wardle, J. 1984. The New Zealand Beeches: ecology, utilisation and management.New Zealand Forest Service, Christchurch. 447 p.

Wardle, P. 1991. Vegetation ofNew Zealand. Cambridge University Press,Cambridge. 672 p.

Ii

I

illI

48 REFERENCES

Wickman, B.E. 1979. Douglas-fir tussock moth handbook: How to estimatedefoliation and predict tree damage. United States Department of Agriculture,Agriculture Handbook No.550, Washington D.C. u.s.A. 15 p.

Will, C.M. 1985. Nutrient deficiencies and fertiliser use inNew Zealand exoticforests. New Zealand Forest Service, Rotorua. Forest Research Institute bulletin97. 53 p.

Wilson, H.D. 1982. Field guide. Stewart Island plants. Fieldguide Publications,Christchurch. 528 p.

Wilson, P.R. 1984. The effects of possums on mistletoe on Mt Misery, Nelson LakesNational Park. In: Dingwall, P.R. (compiler) Protection and Parks, pp. 53-60.Department of Lands and Survey information series no. 12.

Zotov, V.D. 1949. Forest deterioration in the Tararuas due to deer and opossum.Proceedings and Transactions of the Royal Society ofNew Zealand 77: 162-5.

49

9 Appendices

Appendix 1.

Appendix 2.

Appendix 3.

Appendix 4.

Appendix 5.

Appendix 6.

Appendix 7.

Random number (1-50) sets for use in determining the placementof transects.

List of equipment required for establishing or remeasuring FoliarBrowse Index plots. Spares should be carried in case of loss orbreakage.

A. Foliar Browse Index plotsheet - transect and plot data

B. Foliar Browse Index plotsheet - indicator species data

Indicator species assessment sheet

Foliage cover scale

Flowering and fruiting periods for a range of plant indicator species.

Botanical names of plant species referred to in the text.

50 APPENDICES

9.1 Random number (1-50) sets for use in determining the placement of transects

1 41 2 17 15 23 12 6 29 32 252 47 20 36 12 24 30 42 4 26 123 28 33 50 45 9 39 14 14 25 274 22 48 35 4 50 42 4 37 36 425 42 44 12 13 16 8 27 13 42 396 31 7 10 50 34 1 23 25 33 507 35 28 27 31 10 41 5 31 15 408 14 8 30 35 1 15 13 47 1 189 34 45 22 40 36 21 32 6 40 15

10 45 15 13 27 38 47 9 21 16 2811 17 22 14 23 40 18 31 23 43 4612 43 10 26 20 44 29 18 36 13 3313 49 3 37 42 14 34 44 15 24 4814 19 32 6 1 19 16 34 40 38 1715 8 46 33 39 25 44 25 5 41 4116 33 14 18 36 15 50 15 11 27 4717 15 49 48 16 46 38 10 33 ·11 4418 46 17 31 29 27 25 43 18 35 719 4 13 28 30 18 4 40 42 23 520 50 31 39 5 30 6 16 20 18 1121 13 4 47 47 37 35 45 35 14 3422 24 42 42 26 11 28 50 45 30 2923 12 23 1 11 21 27 39 46 17 2024 6 24 15 3 47 33 47 50 48 425 27 26 5 38 33 26 20 43 2 4326 36 37 23 10 20 31 48 10 9 2127 30 34 46 8 31 48 35 7 45 3028 39 12 24 33 7 11 49 27 6 829 23 38 40 32 45 10 37 28 20 1630 3 35 11 48 5 23 46 12 4 331 2 5 16 25 28 45 41 49 50 132 40 36 41 41 12 13 38 30 31 4933 38 30 21 22 13 37 36 8 7 3834 48 43 32 44 22 2 26 26 21 1435 32 25 49 17 39 3 17 9 37 3236 26 19 2 7 35 49 19 2 28 2237 11 50 9 28 4 19 30 16 19 3138 16 6 43 2 6 20 33 41 8 37

iiLa

APPENDICES 51

39 21 40 3 46 49 24 28 24 10 240 44 29 19 9 29 43 1 38 49 2641 9 1 45 19 43 46 2 22 44 3642 20 47 8 6 32 36 21 32 34 2343 5 11 4 21 26 9 11 44 12 644 7 16 25 43 17 7 29 1 47 1045 18 27 20 24 3 40 12 17 29 2446 37 18 7 18 42 14 3 48 3 947 29 21 44 37 8 22 22 34 39 1348 10 9 38 34 2 32 24 3 46 3549 1 39 29 49 48 5 7 19 22 4550 25 41 34 14 41 17 8 39 5 19

9.2 List of equipment required for establishing or remeasuring Foliar BrowseIndex plots. Spares should be carried in case of loss or breakage.

• Topographic map and aerial photo

• Foliar Browse Index plotsheet

• Indicator species assessment sheet

• Foliage Cover scale

• Pen and pencil

• Abney level or clinometer

• Altimeter

• Binoculars

• Compass

• Diameter tape

• Rangefinder

• Tape -20m

• Tape or nylon cord - 100m

• Tree tags (numbered)

• Nails (flathead, galvanised)

• Hammer

• Flagging ("permolat")

52 APPENDICES

9.3A Foliar Browse Index Plot Sheet - transect and plot data

FOLIAR BROWSE INDEX PLOT SHEET

Transect Number ~ _. . _ _

Survey ']~~tL&.~~_~+.". ..lf.q(:IbJ.~d....----.---.----

::::::::::~~-~~~~~~~~;;~:-::::::::::-:-:. ~eb::r::: _-_-_-_t~~~~;.;~~~~-_-_-_-_-_-_-~_-_-~Transeel Origin (grid ref.) & Bearing ~]d-.7.'?Q-~?,~- ..~J.'i.· Dale: [,_./Jj:.~. ._....._

APPROACH LOCATION DIAGRAMI . (.04- 'I1.k.!-;AWU5 f!-....... "/Wuti...Jij",..,r If dJ ,s v'" -fL,

rt'vltf..... 1("d.~_ /0...• .- -.t. -'--"", ope aJ- ~<;;I-II~-If.., ---r.1:lJlOM-IrMk." "51-r~2- sf-..k .:!Do~ uJl stf<~ .r;",. I~I-,~--jk.;, -+r...J~ t~ 'P..lW-,-,,- Sih•.-... ~J k~'-.--~ tlla,;" ""....foul ""'- "-- 1,,-, ,,"".... C-l

1, ---({6"" . 1 5

el-- -fL. ...;...tl;. cl- Co. ""'"-- 0<- -M<k '" \~ \ ~'

---Ik st-~. -J k"",; -----:. 1'W(.rU\.'Sf""'''''''1'1•.J<.

'I1ot- 1_ '-5 .:f<J"" ./lr-e.v. -lk twlin~ o.~

~ -Iu..."""-J I

a.. 1I

INOTES

()«.~ ~M~,.,J;;:. Non possum·related browse

::4---<>f"""-'>~"k;.k ... rw--J. ~.,o:J<. PJotNi! Plant species l.w Medium HecV)' Animal

......J<..s +.........1 ,l..i1L, 'Jf- " I SCJl ,{;. if ~...l:-

" 2- t1'sto .& V ":J.. fI£L.,.... J In5<ut-

BIRDS f'.-+.i/ +v..:.

PlotN.II. Altitude "'pod S~". Phy$iugmphy ORJimtge CcnopyHt Canopy dominants

/ U,O /lro #)/'010.,. -,-- 11 1.(, J)Ae. "",P. 'Po1)hoJ I.JE'sil D'fS SIlL-:J.. :Lqo J'i?5" Olo-o\t<wl F" !.i :J..'i{ \.)Els'l ])l(SSo<' 'P0ll1t.i NErfob3 3/0 111-'5" 5r-u. F ~ .15" WEls;1 tt1lh.:l tlrr."b

9.3B Foliar Browse Index Plot Sheet - indicator species data

APPENDICES 53

Indicator species data

Species totals 1. 7<iD kcJ = 5"2.V'iSsr- - :L3. \,)£1 ,;/ ~-5""

4. GlEr"" = I5. ,,,11::,,,,6 -"-6. J>EJ!..t.J- - 3

1 1 3 4 5 6 7 BA BB 9 lOT lOW 11 12T 12W 13 l4A 14BI"', "3/0 I. I'QilkJ 7'l7).. 1/·0 0 S T )..'0 , I Mil.. 3 ;:. I a 1)

30 If r"JJ'I5so.. 7'773 13.3 L c... 0 3, 3 3 Nt.. I I 0 a 0

3'5"0 'i? ""])'1S si>4- 79:7/J- H c... 5 J 11-, .:t. .L Nfl. I I 0 0 '0

:lo If WEI sil 79:7" If"-: t. c.. ,. -55" 0 () 0 0 0 3 0

Ill", OLEf...... ,9:71, 13·.L 0 S T 7'S I I U. 0 0 , 3 0

330 :5 WE,,;',I 7'l77 1.7·' c.. c... T 7., .2- I Nfl. 0 () 0 3 0

131,- :liD '5" 1'Q))h..1 7"7<;{ 3/·7 c.. c... I 1'.'5" .2- J IJI.. ;J.. , :J.. 0 "I 'ira ~ I.)E:I s" 7';579 1'5'0 0 c T '5"5 0 0 0 0 0 :J.. 0

l'So /'- 7Q})W 7 .....'0 .2•.1 c... c... .., 7., :1- I !oJ 1- I I 0 0

I.1Ja 110 7 \,)Els',1 7~"" 1.2." 0 C. ,. 7,> 0 0 0 0 () :L 3/30 I 'if l,.)E/s:,' "7'i'."'g"J.. 1.2. 0 c... T '5"., 0 0 " " 0 3 2.-

.:uo I:L ~J)h...I 7...1r3 b/j.·f 0 c. -r ~S" 0 0 :J.. .L 0 1) 0

3iJJ> II/- l?all~ 79:'t1l- "'H 0 G 1 7'5' 0 1> 0 0 0 0 0

b", ..... .,J f"....3/r "0 0 llAG<.u.p j,}'i!"'b 10104 H{).~ Iw f«-)

.1{,o 3;1. NElfo\, :J.ill) /fl." 0 IE U. If'> 3 ;J.. L 0

M 75" I , I 0

L.. 75" I 0 , 0W (,5 "3 .:t. Nfl. :L , I f ()

I 'ira 1'5" Her,-.I> .1J~ ~'H 0 E: W 7'5" I I Nfl. I I 0 .L 0

£t" ok.t, stiJl 0 1) N"-sol 3= ~"l lIos m ,)

'l'~-l-J- s 0 I.q, IlT 3'S 0 , :t 0

'YE:I::ht M 0 £p 110 if'S" 0 2 0 0

~S" '1 N..rso' 3'8'.1.... /." (Ho ;ff: ~t)

'PaM s 0 Ep lAo if'S" I / I 0

54 APPENDICES

9.4 Foliar Browse Index - Indicator Species Assessment Sheet

FOLIAR BROWSE INDEXINDICATOR SPECIES ASSESSMENT SHEET

Assess only trees/plants with canopies above 2m (above the level of ungulate browse) and(excepting epiphytes) stems> Scm dbh.

ABUNDANCE (Column 7) Abundance of the species on and around the plot as:A Abundant > 35% individualsC Common 11-35% individualso Occasional 1-10% individualsR Rare < 1% individuals

TIER (Column SA) Tier height class of the tree as:E Emergent canopy isolated above that of neighbouring treesC Canopy forming part of the forest canopyS Subcanopy below the forest canopy

SEGMENT (Column SB) Segment of the tree being assessed as:5 Stem one of a group of stems or branchesT Tree the whole tree canopy

FOLIAGE COVER (Column 9)From the Foliage Cover Scale select the square which most closely resembles the foliage cover ofthe canopy. Where a tagged tree is alive but completely defoliated, or has died, record the foliagecover as:

Completelydefoliated living treeRecently deadLong dead

no leaves remaining. Bark is intact and contains sap.fine twigs present. Bark intact, but no sap present.fine twigs absent. Most larger branches remain.Bark not intact. Remove the tag from trees recordedas long dead.

affecting < 5% of the canopyaffecting 5-25% of the canopyaffecting 26-50% of the canopyaffecting 51-75% of the canopyaffecting >75% of the canopy

DIEBACK (Column 10)The conspicuous presence of dead branches or branchlets (but not recently defoliated live twigs)in the upper third of the canopy(Cohinm lOT), and over the whole of the canopy (ColumnlOW). Record dieback as:o Nodieback1 Light2 Moderate3 Heavy4 SevereX Unable to estimate

RECOVERY (Column 11)Where dieback has been recorded detennine the extent of any conspicuous recovery as:NR No recovery no visible recoveryFlush of epicormic growth on the trunk and major branches in theU Upper upper of the canopyL Lower lower of the canopyW Whole throughout the whole canopyX Unable to estimate

no browsed leaves< 5% leaves browsed5-25% leaves browsed26-50% leaves browsed51-75% leaves browsed76-100% leaves browsed

APPENDICES 55

BROWSE (Column 12)The proportion of possum-browsed leaves (or in the case of small-leaved species such astatara, the severity of possum-related hedging) in the top third of the canopy (Column 12T),and averaged over the whole canopy (Column 12W) as:

o Nil no possum-related hedging0.5 Some minimal hedging1 Light or lightly hedged2 Moderate or moderately hedged3 Heavy or heavily hedged4 Severe or severely hedgedX Unable to estimate

STEM USE (Column 13)Recent possum use of the lower 2 ill of the trunk or stem as:o Nil no scratching or bite marks on the trunk1 Light occasional scratch and bite marks2 Moderate numerous clearly defined scratch and bite marks3 Heavy bark worn smooth, evidence of a well developed

possum "run".X Unable to estimate trunk obscured by epiphytes or moss.

FLOWERING AND FRUITING (Colnmn 14)The presence and abundance of flowers (Column I4A) and fruit (Column I4B) as:o Nil no flowers or fruit visible.1 Rare few flowers or fruit visible, often only in part of the canopy.2 Occasional sparse flowering/ fruiting, usually throughout the'canopy.3 Common flowers or fruits common throughout the canopy.4 Abundant flowering/ fruiting heavy;. highly visible, and

present on most branch1ets.

EMERGENT TREESSegment (Column SB) U upper third of the canopy

M middle third of the canopyL lower third of the canopyW whole canopy

EPIPHYTESPlant size (Column 6) XS extra small

S smallM mediumL largeXL extra large

<1/2mwide1/2-1mwideI-2m wide2-3m wide>3mwide

Position on the host tree (Column SB)B lower third of the host tree canopyM middle third of the host tree canopyU upper third of the host tree canopy

T epiphyte attached to the main bunkI epiphyte on the inner brancheso epiphyte on the outer branches

9.5 Foliage Cover Scale

% • •95_

85. • II75. • II651; i) Ell55 ii ~ ~

45~ W ~

35 ~ ~ [i

~ ~ ~25 .:.:-..

• • •

~ ~15 .: •• • • -e-..-. .-

D 0 05 •• •

Indicator Species Area & information source

(a) NZ NZ Northland C. North Is. Wellington Canterbury Westland Stewart Is.(1) (3,b) (4) (5) (6) (7) (8) (9)

Emergent treesMetrosideros robusta northern rata Fl. Nov-Jan Nov-Jan Dec-Feb Dec-Jan

Fr. Dec - Jan Jan-Apr May

Podocarpus totara lowland totara (c) Sep-Oct Dec Oct-Dec& P. hallii & Hall's totara (d) Apr-May Jan-Dec Jan-Dec Feb-May Jan-Jun Feb-Aug

Canopy treesAlectryon excelsus titoki Fl. Oct-Dec Oct-Nov

Fr. Oct-Dec

Beilschmiedia tawa tawa Fl. Sep-Dec Sep-Nov Nov-Dec Dec-FebFr. Oct-Feb Jan-Dec Dec-May

Dysoxylum spectabile kohekohe Fl. Mar-Jun Apr-May May-Jul Apr-JulFr. Apr-Aug Jul-Aug Jan-Dec Aug-Feb

Elaeocarpus dentatus hinau Fl. Oct-Feb Oct-Feb Nov-Dec Oct-Jan Nov-AprFr. Dec-May ~ Jan-Jul Dec-Jun Mar-Aug

E. hookerianus pokaka Fl. Oct-Jan JanFr. Nov-Mar Feb-Jun Mar

Melicytus ramiflorus mahoe Fl. Nov-Feb Sep-Apr Oct-Mar Oct-Mar Oct-Apr Dec-JanFr. Nov-Mar Feb-Jun Nov-Jun Nov-Jul Jan-Jul Feb-Aug

~

a--

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§p..

[-~."':l~....ol:l<

'".....o...ll>

~

~rDo.....

"':l

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Canopy treesMetrosideros excelsa pohutukawa Fl. Dec-Jan Dec-Jan

Fr. Jan-Feb

M. umbel/ata southern rata Fl. Nov-Mar Nov-Jan Oct-Feb Nov-FebFr. Dec-Apr Mar-May

Weinmannia racemosa kamahi Fl. Dec-Jan Nov-Jan Oct-Jan Sep-May Sep-Feb Oct-DecFr. Jan-Apr Dec-Mar Jan-Apr

W. si/vico/a towai Fl. Sep-Dec Sep-Dec Jan-AugFr. Nov-Feb Dec-Oct

Subcanopy trees & shrubsAristote/ia serrata wineberry, Fl. Sep-Dec Sep-Dec Oct-Nov Oct-Dec Oct-Nov Nov-Dec Sep-Dec Nov

makomako Fr. Nov-Jan Jan-Feb Nov-Mar Nov-May Dec-Mar Jan-Apr Jan-Mar

Fuchsia excorticata fuchsia Fl. Aug-Dec Aug-Dec Jun-Nov Sep-Apr Sep-Jan Jul-Dec Jul-DecFr. Sep-Feb Dec-Mar Jul-Feb Oct-May Dec-Aug Dec-Feb Dec-Apr

Myrsine sa/icina taro Fl. Aug-Jan Jul-SepFr. Sep-May Oct

O/earia rani heketara Fl. Aug-Nov Sep-Oct Sep-Nov Nov-Dec Oct-AprFr. Nov-Jan Nov-Dec Jan-Feb Jan

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Subcanopy trees & shrubsPennantia corymbosa kaikomako Fl. Nov-Feb Sep-Oct Nov-Feb Nov-Dec Dec-Jan Nov-Dec Dec-Jan

Fr. Jan-May Feb-May Jan-May Mar Jan-Apr Feb-Apr

Pseudopanax arboreus five finger Fl. Jun-Aug Jun-Aug Feb-Sep Jul-Dec Dec-Feb Jul-NovFr. Aug-Feb Jan-Dec Jan-Dec Jan-Dec

P. colensoi mountain Fl. Jun-Mar Sep-Dec Jan-Decfive finger Fr. Jun-Mar Dec-Oct Jan-Dec

P. crassifolius lancewood Fl. Jan-Apr Jan-Apr Mar-May Mar Dec-Feb Jan-Feb FebFr. Jan-Apr Mar-Oct Mar-Jan Jan-Oct Apr-Oct Jan-Dec

P. edgerleyi raukawa Fl. Nov-Mar Dec-Feb Nov-Dec Nov-FebFr. Nov-Mar Jan-Mar Jan-Dec Feb-Oct Feb-Mar

P. simplex haumakaroa Fl. Jun-Mar Dec-Feb Dec-FebFr. Jun-Mar Apr-Dec Jan-Dec

Schefflera digitata pate Fl. Feb-Mar Jan-Mar Mar-Apr Dec-Mar Dec-Jan Feb-MarFr. Feb-Mar Mar-Aug Jan-Oct Mar-Aug Jan-Sep Jan-Dec Mar

Lianes -Metrosideros fulgens climbing rata Fl. Feb-Jun Feb-Jun Mar-Aug Jan-Dec Jan-Dec

Fr. Oct-Dec Mar-Feb Jan-Oct

Ripogonum scandens supplejack Fl. Dec-Jan (2) Oct-Nov Oct-Dec Nov-Feb Dec-Apr Dec-FebFr. Jan-Dec (2) Jan-Dec Jan-Dec Jan-Dec Jan-Dec Jan-Dec Jan-Dec

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Epiphytes

Alepis flavida mistletoe Fl. Dec-Feb

Fr. Jan-Jun

Peraxilla colensoi mistletoe, Fl. Nov-Feb Oct-Jan Nov-Markorukoru Fr. Dec-Mar Feb-Aug

P. tetrapetala mistletoe, Fl. Oct-Jan Oct-Jan Nov-Febpirirangi Fr. Dec-Feb Feb-Jun

(a) Fl. flowers; Fr. fruit (unripe & ripe); (b) ripe fruit only; (c) male cones; (d) female cones

(1) Allan 1961; (2) Moore & Edgar 1970; (3) Salmon 1967; (4) Best & Bellingham 1991; (5) Leathwick 1984; (6) Brockie 1992; (7)

Burrows 1994; (8) O'Donnell & Dilks 1994; (9) Wilson 1982.

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-APPENDICES 61

9.7 Botanical names of plant species referred to in the text. Nomenclature followsAllan (1961), Moore & Edgar (1970), Connor & Edgar (1987).

Emergent treesDacrydiu111 cupressinumMetrosideros robustaPodocarpus halliiP. totara

Canopy treesAlectryon excelsusBeilschmiedia tawaDysoxylum spectabileElaeocarpus dentatusE. hookerianusMelicytus ramiflorusMetrosideros excelsaM. umbellataNothofagus solandri var cliffortioidesWeinmannia racemosaW silvicola

Subcanopy trees and shrubsAristotelia serrataFuchsia excorticataMyrsine salicinaOlearia raniPennantia corymbosaPseudopanax arboreusP. colensoiP. crassifoliusP. edgerleyiP. simplexRhopalostylis sapidaSchefflera digitata

rimu

northern rata

Hall's totara

lowland totara

titoki

tawa

kohekohe

hinau

pokaka

mahoe

pohutukawa

southern rata

mountain beech

kamahi

towai

wineberry

fuchsia

toro

heketara

kaikomako

five finger

mountain five finger

lancewood

raukawa

haumakaroa

nikau

pate

62 APPENDICES

LianesClematis vitalbaFreycinetia baueriana subsp. banksiiMetrosideros fulgensRipogonum scandensRubus spp.

EpiphytesAlepis flavidaAstelia spp.Collospermum spp.Peraxilla colensoiP. tetrapetala

Old Man's Beardkiekieclimbing rata

supplejacklawyer

mistletoe

mistletoe / korukorumistletoe / pirirangi

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