Monitoring and Assessing Ground Vegetation Biodiversity in National Forest Inventories

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DOI 10.1007/s10661-009-0919-4

Review of monitoring and assessing ground vegetation biodiversity in national forest inventories

I. Alberdi·S. Condés·J. Martínez-Millán

Received: 15 July 2008 / Accepted: 6 April 2009 / Published online: 7 May 2009

Abstract Ground vegetation (GV) is an important component from which many forest biodiversity indicators can be estimated. To formulate policies at European level, taking into account biodiversity, European National Forest Invento- ries (NFIs) are one of the most important sources of forest information. However, for monitoring GV, there are several definitions, data collection methods, and different possible indicators.

Even though it must be considered that natural conditions in different countries form very different understory types, each one has its own cost- efficient monitoring design, and they can hardly be compared. Therefore, the development of general guidelines is a particularly complex issue.

This paper is a review of data collection methods and consequently a selection of the best available methods for the set of indicators with an em- phasis on GV sampling methodologies in NFIs.

As a final result, recommendations on GV definitions and classifications, sampling methodologies, and indicators are formulated for NFIs. Different sampling areas are recommended for each life form (shrubs, herbs, etc.). Inventory cycles and

I. Alberdi (

B

⁾^·^{S. Condés}^·J. Martínez-Millán ETSI Montes, Universidad Politécnica De Madrid, Ciudad Universitaria s/n. 28040 Madrid, Spain e-mail: [email protected]

sampling seasons (depending on the phonological stages) should be specially considered and evaluated in the results. The proposed indicators are based on composition at different levels of sampling intensity for each life form and on coverage measurements.

Keywords Ground vegetation·Ground vegetation components·Ground

vegetation attributes·Forest biodiversity· National forest inventory·Sampling methods· Volume calculations

Introduction

There are many biodiversity definitions; the Ar- ticle 2 of the Convention on Biological Diversity (UNEP1992) offers an objective and clear one:

...the variability among living organism from all sources including, alia, terrestrial, marine and other aquatic ecosystems and the eco- logical complexes of which they are part;

this includes diversity within species, between species and of ecosystems.

The quantification of biological diversity is an important objective in the assessment of nontim- ber resources in forest surveys (Groombridge and Jenkins1996). Forest inventories have principally been aimed at estimating the standing volume

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of wood in forests and for monitoring changes in growth. Nevertheless, supplemented with rel- evant measurements and observations (Newton and Kapos2002), forest inventories can also be a useful tool to estimate forest biodiversity.

As most forest plant diversity occurs in the ground layer, it is particularly important to sample this community (Johnson et al.2006).

The ComMon project—Comparison and eval- uation of methods for monitoring of dead wood, vegetation, epiphytic lichens, and stand structure in European forests—has been carried out by a consortium of people from Austria, Belgium, Italy, Spain, and Sweden under the umbrella of the “European National Forest Inventory Net- work” (ENFIN). The ENFIN comprises most of the European NFIs. Four determinant topics for biodiversity estimation were defined (stand structure, dead wood, ground vegetation, and epiphytic lichens), and each member country was the responsible for each one. This review of data collection methods aims at identifying the best available methods for the set of indicators. Can- didate inventory methods are selected as a final result. The candidate methods are evaluated in terms of their costs and reliability in plot level assessments.

This paper is focused mainly on National Forest Inventories needs and limitations, and the classifications, data collection methods, and biodiversity estimation will be chosen based on them.

Importance of the ground vegetation and its role in the forest ecosystem

Ground vegetation information is a key parameter for biodiversity. It can provide information about the forest types and structure. Each forest type usually has associated a specific understory. The diversity of plant communities offers an opportu- nity to efficiently collect data on the community that provides the structure and productive base for all other organisms (Johnson et al.2006). Be- sides the distribution and indicator, species can be used to estimate site conditions like drainage and fertility. They can be also applied to provide information about the forest stage, maturity, etc.

Ground vegetation has been identified as a specific target for the calculation of critical loads or levels. It has been used to detect changes in the ecosystem as air pollution, particularly nitrogen deposition, and climate change. These vegetation studies have the advantage of their low cost comparing with analyzing air or soil chemistry (Thimonier et al.2003).

Individual species can be very important indicators of a size potential productivity, economi- cal value, wildlife forage, and shelter. Changes in composition and spatial arrangement of vascular plants in a forest may indicate the presence of chronic stresses such as discrete site degradation (COST E43, technical report,2005).

Ground vegetation in the surroundings of the forest stands could also provide information about the history of the place like natural forests in the area, harvesting intensity and methods, main ecological alterations, etc.

Definitions of ground vegetation and its components

To describe major terrestrial plant communities, a definition of different life forms is needed (Bonham1989). Major life forms are usually des- ignated by “trees,” “shrubs,” and “grasses.” Also,

“ferns” and “bryophytes” are sometimes monitored. There are different classifications depending on the plant ecology needs and the objectives of each inventory.

It is quite difficult to achieve a common definition for ground vegetation. The main problem is that “ground vegetation” is a vague expression, and it may include many different life forms and each project considers a different one.

One of the clearest “ground cover” definitions that include life form concept (Hubbard et al.

1998) is “all herbaceous plants and low growing shrubs in a forest or open area.” The main objec- tion is that not all the strata are integrated in the definition. Also bryophytes, lianas, bigger shrubs, or even small trees (as some riparian trees) or tree regeneration could be considered as ground vegetation. For this reason, most of the references to ground vegetation are related to the different vertical strata under the tree cover.

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Two points of view are usually chosen, the botanical definition or a practical one, the aggre- gation of different life forms into layers.

The European projects BIOFOREST and FOREST BIOTA define layers according to height and life forms.

The FOREST BIOTA report (Granke 2006) defines three different vertical strata (below tree layer) based on height: shrub layer, herb layer, and moss layer. This criterion is practical for analysis and assessment purposes. The BIOFOREST PROJECT (Smith et al.2005) dis- tinguishes six different layers considering the growing habits and also the height: large shrub layer, subshrub layer, bramble layer, graminoid layer, forb layer, and bryophyte layer.

Referring to shrub, in BIOFOREST definitions, they distinguish between large shrub layer (“woody vegetation below the upper tree stratum and in the 2–5-m height range”) and subshrub layer (“woody vegetation under 2 m tall. Such vegetation includes tree seedlings, small shrubs, bramble and climber species are not included”);

while in FOREST BIOTA, only one shrub layer is defined (“ligneous, including climbers >0.5 m height”).

The FAO report (2005) defines shrub as

“woody perennial plants, generally more than 0.5 meters and less than 5 meters in height at maturity and without a definite crown. The height limits for trees and shrubs should be interpreted with flexibility, particularly the minimum tree and maximum shrub height, which may vary between 5 meters and 7 meters”. Others life forms are not mentioned. No allusion to any ground vegetation life form is made when defining forest.

There are also many differences when comparing herb layers definition; the FOREST BIOTA herb layer definition is “all non-ligneous and ligneous <0.5 m height” but in the BIOFOREST PROJECT, two different layers could be integrated in the herb layer: graminoid layer (grasses, rushes, and sedges) and forb layer (vascular herbs, excluding graminoids, climbers, woody species, or ferns).

The Forest Terminology from the University of Florida differentiates between shrub layer and herbaceous layer. The herbaceous layer is defined as “all the non-woody plants, for example, grasses,

forbs, wildflowers and ferns”. Plants lignification is considered to distinguish groups.

The Australian Biocondition (Eyre et al.2006) defines shrubs as “woody plant multi-stemmed from the base (or within 200 mm from ground level) or if single stemmed, less than 2 m”. This report makes a differentiation between ground cover, where grasses, herbs, and forbs are included, and weed cover. For field purposes, it would be necessary to use trained personnel due to the need to distinguish weeds from native herbs.

Ecological Monitoring and Assessment Net- work (EMAN; Roberts-Pichette and Gillespie 1999) provides accuracy in its definition based on diameter at breast height and growing form criteria. Shrubs are defined in this document as

“usually multi-stemmed woody plants less than 4 cm dbh with most of the stems originating at or near the ground. Saplings under 4 cm dbh will be measured with the shrubs in the shrub and small tree stratum. Shrubs are usually present in forests, they dominate in heath communities, they may form the canopy in riparian strips in the ecotone between forests and alpine or arctic tundra, and in bogs or other high-water-table communities.

Shrubs may also be found scattered in large or small clumps in grassland communities”. For the herb layer, they use the term field layer vegetation (“comprising all herbaceous vegetation regardless of height, and all woody plants under 1 m in height”).

The bryophytes and lichens are also considered in some reports as a layer of ground vegetation. Mosses and liverworts are considered as one layer in the BIOFOREST project (Bryophyte layer: Mosses and liverworts while lichens are not included) and in FOREST BIOTA and EMAN projects, mosses are considered together with lichens. In FOREST BIOTA the moss layer is the

“terricolous bryophytes and lichens”. In EMAN, this layer is called ground layer vegetation, and it is defined as “layer comprising mosses, lichens, and fungi growing on the ground, together with small trailing and rosette plants.”

The different strata and substrata considered in the detailed reports and its height thresholds if considered are described in Table1.

According to the different purposes when defining ground vegetation, it is necessary to

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Table 1 Ground vegetation layers thresholds definition in different reports

Small trees Shrubs Herbaceous Bryophytes Lichens

FRA 2005 0.5–5 m

BIOFOREST Large shrub Graminoid layer Mosses and liverworts layer: 2–5 m

Subshrub layer: Forb layer 0.5–2 m

FOREST >0.5m Non-ligneous and Moss layer (terricolous Moss layer (terricolous

BIOTA ligneous<0.5 m bryophytes and lichens) bryophytes and lichens)

FLORIDA No height Herbaceous

threshold vegetation defined (non-ligneous)

EMAN 4<dbh <4cm dbh Non-ligneous and Ground layer vegetation Ground layer

<10 cm ligneous<1 m (mosses, lichens, fungi, vegetation and small trailing

and rosette plants)

BIOCONDITION Multistemmed Ground cover:

or<2 m grasses, herbs, and forbs Weed cover

describe the included layers. There are many variations between the considered ones; how they are grouped and its thresholds. To be able to standardized layers, it is necessary to analyze and com- pare also the NFIs definitions (see “Definition” in NFIs). A proposal of GV definition and structure is at the end of this article (“Conclusions and recommendations for NFIS”).

Photointerpretation and geographic information systems

Forest biodiversity is a very complex concept; on one side, it is not possible to measure all the forest components, and on the other, there are several dimensions which may characterize it as genes, species, structural aspects, or landscape components (Vanclay1998).

In order to ensure a good inventory in terms of precision, remote sensing provides relatively cheap objective information in many inventories to classify the different habitats or vegetation types as a previous phase to sampling design.

However, the use of remote sensing (Poso et al.

1995) is an increasingly useful tool. As indirect measurements are needed, it is necessary to focus on key variables and habitats in order to quantify and qualify biodiversity (for example, study of the

relations between the structure of standing mate- rial and other species such as ground vegetation, insects, fungi, and mosses; Rondeux1999).

Remote sensing techniques provide an efficient tool for assessing and monitoring forest biotypes and their structural diversity (Holopainen and Guangxing 1998). They propose indicators that are measurable directly from the images as frag- mentation indexes and others when truth data are available (rarity of biotopes, key biotopes, etc.).

Also, a methodology for assessing and monitoring European forest biodiversity, using sat- ellite remote sensing, has been developed as a part of the Forest Information from Remote Sensing Project (McCormick and Folving 1998):

determines indicators of forest biodiversity for composition (identity, distribution and relative proportions of forest patches), structure (spatial pattern of forest patches), and development (temporal changes in forest composition and structure). The main objective of this study is to detect areas at high risk of deterioration.

There are several studies related with ecosystem changes at large scales (e.g., Gould2000), but they are usually applied for grasslands or shrub land, not for forest (e.g., Qi et al.2000).

It is also very important that the available information on biodiversity be stored within geo- graphically referenced databases if it is to be made

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quickly accessible for mapping, analysis, or modeling purposes. For this information to be really usable, it must also be integrated with a great deal of other information on environments, so- cioeconomic conditions, types of natural resource, potential risks of degradation, etc. Geographic information systems can be keys to integrating information with the desired degree of detail (Jeffers1996).

Sampling design: field methods

The first step for sampling design is always to define the different homogeneous strata. It is very useful to relate ground vegetation strata with forest types. There are European Forest types ap- proved by the European Environmental Agency (Barbati et al.2006) which are appropriated for a European common reporting.

The number of sampling plots and their sizes will mostly determine the accuracy of the inventory. The main variable in NFIs is usually wood volume so this inventory design normally prede- termines the monitoring of other variables such as ground vegetation attributes.

The plot features are defined for each inventory. To define correctly the location, plot number, size and shape, or transects is essential. The location in the forest will depend on the inventory method (systematic, stratified, etc.). To mon- itor long-term changes in plant biodiversity in different ecosystems, permanently marked sam-

pling areas are essential (Roberts-Pichette and Gillespie1999).

There are a great variety of different shapes and sizes of the plots. There is not a unique size or shape useful to measure the cover vegetation.

Shape and size of the plots are fixed by the measurements, field works, data analysis, and costs involved.

If the aim of the inventory is to delimit units defined by plant associations, the plot size will be conditioned by the minimal area. This minimal area can be defined as the minimal area in which the qualitative composition (number of species) has an adequate representation (Aguilo et al.

1992). Therefore, to analyze a homogeneous plant community, the function relating the number of species with the monitoring area should culminate in an asymptote. This minimal area must be decided for each inventory, but there are indicative values for temperate ecosystems areas (Ellenberg and Mueller-Dombois1967b) shown in Table2.

The Canadian Central National Agency for Inventory and Monitoring, EMAN has prepared biodiversity monitoring protocols for terrestrial vegetation (Roberts-Pichette and Gillespie1999).

This protocol is recommended for intensive research projects. There are for each layer two or more sizes. They may substitute for one another or may be used together depending on the forest ecosystem (Table2).

Small tree and shrub layer:

5×5 m (25 m²)

2×2 m (4 m²)for densely packed shrubs

Table 2 Ground vegetation monitoring area

Vegetation associations Area (m²)

Ellenberg and Mueller Forest stands (understory) 50–200

Dombois (1967b) Grasslands 50–200

Dwarf shrub 10–25

Mosses communities 1–4

Liquens communities 0.1–1

EMAN (Roberts-Pichette Small tree and shrub layer Standard 25

and Gillespie1999) Dense packed 4

Woody plants (h<1m), Standard 1 herbaceous lichens Numerous individuals 0.25

and fungi and dense packed

0.0625

Oosting (1956) Shrubs h<3m 16

Herb layer 1

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Ground vegetation (all herbaceous vegetation and all woody plants under 1 m in height, mosses, lichens and fungi growing on the ground, together with small trailing and rosette plants):

1×1 m (1 m²)

0.50 × 0.50 cm (0.25 m²) or 0.25 × 0.25 cm (0.0625 m²), smaller quadrats for numerous and densely packed individuals.

If the aim is to measure quantitative variations of the present plant species, the minimal area concept is not usually used. Instead, in the estimation of variables such as number of individuals, abundance, frequency, cover, height, diameter, and biomass, the sample size is decided following traditional inventory methodologies depending on characteristics as the distance between the individuals, their size, or the density (Aguilo et al.

1992). Oosting (1956) determined in 1956 some recommendations:

Shrubs h<3 m: square plots of 4 × 4 m (16 m²)

Herbs layer: square plots of 1×1 m (1 m²)

The Expert panel on Ground vegetation assessment (Dan Aamlid et al. 2002), in the “Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of the effects of air pollution on forest. Part VIII.

Assessment of ground vegetation” defines a minimal area of 400 m² to achieve the comparability of results between countries. They describe two different sampling designs that could be used for monitoring ground vegetation in Europe. These two cases lead to a more qualitative or quantitative characterization:

• In the first case, the dynamics are assessed by monitoring changes in the species composition of a large number of species over a large area, utilizing sampling units around or greater than 100 m², with a low to medium accuracy in estimates of changes in cover for each of these species.

In the same way, if the study includes phyto- sociological grasslands data is recommended a plot size around 100 m²(Dierschke1994; Chytrý and Otýpková2003; Dulamsuren et al. 2005) because a standard plot size was preferred for both woodlands and grasslands to ensure unlimited comparability in statistical analyses (Dulamsuren et al.2005)

• In the second case, the study concentrates on population dynamics (expansion or regression) on a smaller area. Small sampling units (in general fewer than 10 m²) are used for a more accurate estimation of species cover.

In these studies, it is normal to use subplots, especially in surveys of ground-bryophytes and ground-lichens. The shape can be different circles (Mäkipää and Heikkinen 2003; Hokkanen2006) or squares (Saetre 1999) with a surface around 1–3 m² (Ford and Newbould 1977; Kühlmann et al.2001).

Circular plots are usually cheaper than rectangular ones. However, it should be noted that rectangular shapes allow more species to be found (Stohlgren1994).

Usually, when monitoring different life forms, plots of different sizes are nested.

Permanent plots are recommended for use in plant communities where no obvious vegetation gradients are present, but where vegetation gradients are obvious, permanent transects are recommended (Roberts-Pichette and Gillespie 1999).

Sometimes, transects are designed as a comple- ment to the plots (Kupferschmid and Bugmann 2005). Transects can establish plots along them or to use only the width.

The EMAN protocol on ground vegetation recommends the following permanent transect dimensions:

Transects organized as contiguous 5 × 5-m plots (when trees, small trees, or large shrubs are dominant)

Transects organized as contiguous 1 × 1-m plots (when low-shrub and ground vegetation dominate)

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Transects have got a special significance in riparian conditions (Jansen et al.2004). These transects can be parallel or perpendicular to the stream.

Inventory cycles and year seasons

Ground vegetation is highly variable. A distur- bance (e.g., drought) can change in a few years the complete scenario. The understory vegetation is also affected by silvicultural treatments, such as forest drainage, fertilization, thinning, or final harvesting (Hotanen and Vasander1992; Olsson and Staaf1995; Brunet et al.1996; Brakenhielms and Liu 1998). This point is important because changes in the tree layer often lead to changes in ground flora compositions and distributions (Bergstedt and Milberg2001).

As vegetation has that high rate of temporal variation the first step is to specify the cycles and year seasons recommended for sampling the different vegetation types. Dan Aamlid et al. (2002) recommend that the vegetation studies must be undertaken at least every 5 years. But to differentiate between short-term fluctuations from long-term vegetation dynamics, they recommend that vegetation assessment should be undertaken every year.

The number of annual visits should be decided depending on the existing seasons or phenologi- cal stages. One of the most important pieces of information in the ground vegetation inventory is the recorded species composition. Especially for nonwoody species, the researchers should go at least two times a year if there is an important blooming season, commonly the spring. Any- how, the moment of the research depends of the recorded data; if it is surface cover, the best time is the flowering season, but for maximum of biomass, it is necessary to use the last moments of the growing season (Odum 1960; Zavitkovski 1976)

In order to facilitate comparisons, it is recommended to measure at least once every year at the same season. But actually, in NFIs, cycle is a fixed established parameter very difficult to change due to by logistics and national inventory budget.

Characterization of measurable attributes

Three key components of biodiversity can be recognized in forest ecosystems (Schulze and Mooney 1993): composition, structure, and function. The main focus to describe them is on attributes or indicators relating to structure and composition as these are more feasible to measurement (Ferris and Humphrey1999). Plant life forms, functional groups, species groups, or plant species can be described by composition characteristics as frequency, density, and structural attributes as cover or biomass, all of them related to land areas. All these attributes can be estimated by plots, points, and lines depending of the different objectives of the inventory.

The vegetation attributes that can be analyzed for the different life forms are described in the followings points (7.1 and 7.2).

Composition

Occurrence: species presence/absence

To determine main composition of ground vegetation may be fundamental to define some essential indicators such as forest types or protected forest area that are common to all the criteria and indicator processes and international reporting oblig- ations (Newton and Kapos2002).

Determination of species may be difficult and requires specialized field people and lot of time which implies high cost. This means that is not always possible to determine all the species of all the life forms because it is not cost-efficiency feasible. It is also critical to the success of the inventory that species should be identified in a re- peatable way and that presence/absence should be determined certainly (Vanclay1998). That is why most inventories at large scales (especially NFIs) are based on list species and it is not mandatory to recognize the species not included in the lists.

The selected species sometimes take account of key species, umbrella species, threatened species, endemic species, and/or introduced species used as indicators in some international processes. The list must be carefully elaborated, and one must be aware that in some cases, other surveys should complete this inventories (Newton and Kapos

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2002), for instance, Swiss NFI has an independent inventory for ground vegetation.

Species richness

The species richness is the number of species. It is usually related to the total or average number of species in each habitat. Evenness can be taken into account or not. The species richness in NFIs is related to the current richness, but there is also the possibility to determine potential richness and relate them in order to assess the conservation status. Potential richness calculation is always problematic and never precisely defined.

It can be estimated using habitat associations or by statistically calculated functions which determine maximal species richness (Terradas et al.2004).

Frequency

Frequency describes the distribution of species in a community and is a useful index for monitoring changes in vegetation over time and comparing different plant communities (Bonham1989). Fre- quency is the percentage of species present in a sample unit. It is influenced by size and shape of sample units also sensitive to abundance and pattern of plant growth (Bonham1989).

Abundance and density

Abundance is generally referred to an estimation of the present species; it is usually expressed in relative numbers, as rare, seldom, frequent, or abundant species.

Density can be defined as the number of species per unit of area (see Table 3). That attribute requires that individuals be countable (Bonham 1989). Woody species are easily counted while

others life-forms are not. The recommended scale is in Table8.

Structure

Number of layers

Ground vegetation can be stratified by life forms, height, or others classifications (see “Definitions of ground vegetation and its components”).

Sociability

Distribution of community individuals is not ran- domly distributed; they form colonies, and this fact is defined as vegetation species sociability. It is the responsible of the vegetation pattern.

Cover

Cover is determined by projecting aerial parts of vegetation onto the ground (foliage cover) or by vegetation in contact with the ground (basal area). It can be calculated for all of the existing vegetation for determinate layers or groups or for each species. Canopy coverage of species life forms is useful to indicate the amount of sunlight interception (Bonham1989).

Size (height, basal area) and biomass

There are several dimensional factors as height, thickness, or weight that can be measured of each individual or estimated as averages of a group of individuals. Vegetation biomass is the weight of plants per unit of area, express in dry weight, kilocalorie, or in carbon grams (Gounot 1969).

Biomass indicates the quantity of resources as water or nitrogen (Thimonier et al.2003) used by a species in the community and so how much of the

Table 3 Abundance classifications and density values

Scale Tansley and Braun-Blanquet Hanson and Bocher Hanson Number of

Chipp (1926) (1932) Love (1930) (1933) (1934) individuals/m²

1 Rare Highly seldom Highly seldom Rare Seldom 1–4

2 Occasional Seldom Seldom Not common Less frequent 5–14

3 Frequent No numerous Less frequent Frequent Frequent 15–29

4 Abundant Numerous Frequent Common Abundant 30–99

5 Very abundant Very numerous Abundant Very common Very abundant >100

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communities resources are tied up with different species. It also indicates the forage available for animals. Browse is sometimes measured if large herbivores are present. Another very important fact to determine this attribute is to know the quantity of combustible in case of forest fire.

Shrub biomass is usually estimated from dimensional analysis while herbaceous biomass is frequently measured clipping or guessing amounts of biomass (Bonham1989). Biomass of other life forms is not usually monitored.

Methods for measure the different attributes inside the plots

All the composition and structure attributes can be estimated by plots, some of them also by lines (as line intercept method) and others by points (as point frames) too. Points are considered to be the most objective way of estimation while areas are often biased (Bonham1989) but all variables can be measured by plots, and they are considerate the optimal cost-efficiency methods. Line transect is also a very used method and adequate for some variables. Plots and line intercept are the most commonly used methods in the large-scale inventories. The methodologies are described below.

Line: line intercept

This method is mostly used to estimate vegetation cover, but it is also used some times for frequency data collection. It can be obtained from a line placed to contact plants. If basal cover area is needed, then the line is placed at ground level (Bonham 1989). The length of each intercepted plant i part is measured.

Percentcover= d_i length×100

In general, cover in herbaceous communities can be estimated with short lines (less than 50 m), while long lines (more than 50 m) should be used with shrubs.

Bonham (1989) elaborates several conclusions between the monitoring cover methods:

Line intercept is most useful for open-grown, woody vegetation while point intercept is more useful for pastures and grasslands.

These methods are much more efficient that charting methods and as accurate as them.

Areas Plot

Areas are commonly used to estimate species richness, frequency, density, cover, and biomass.

For long-term monitoring of plant species diversity circular, quadrangular, or rectangular plots of given areas (see “Sampling design”) are the most used and recommended areas. A minimal area must be determined depending on the inventory objectives but the smaller the sampling area, the higher the accurate estimation. It is also a useful process to divide the plot into smaller subunits.

Scales or class methods

Abundance and density There are several abundance classifications and they usually correspond with density values. In Table 3, some frequent scales are shown (Shimwell1971).

Frequency The most common methods for frequency estimation are counting individuals into nested plots or complementary plots (Bonham 1989). Frequency data usually follow the Poisson or binomial distribution.

Cover scales Cover range comprises generally values from 0% to 100%, and they are arbitrarily divided into a number of categories, and each cat- egory is assigned a rating or scale value (Bonham 1989). As they are visual estimations it is necessary to have trained technicians to avoid bias. The most used ones are shown on Table4. In all these scales, it is important to decide about excluding or including, major heterogeneities (big rocks, paths, water streams, cliffs, fences, etc.) and as well the global cover of bare soil, litter, and other elements of the surface.

Some methods also take into account the number of individuals of each species, in addition to the area occupied by them, to obtain the “species

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Table 4 Cover scales Scale Scale Cover (%) Number of plants of each species

Margalef (1974) 1 0–5

2 5–25

3 25–50

4 50–75

5 75–100

Daubenmire (1968) 1 0–5

2 5–25

3 25–50

4 50–75

5 75–95

6 95–100

Barkman (1989) R Sporadic/

association

+r Sporadic

(1,2 ind)/plot Few (3/20 ind)

+p <1

+a 1–2

+b 2–5

Numerous (20–100 ind)

1p <1

1a 1–2

1b 2–5

Very numerous (>100 ind)

2m 0–5

2a 5–12.5

2b 12.5–25

3 25–50

4 50–75

5 75–100

Schmidt scale (1986) + <1

1a 1–3

1b 3–5

2a 5–12.5

2b 12.5–25

3 25–50

4 50–75

5 75–100

Braun-Blanquet (1932) R <1

+ 1

1 1–5

2 5–25

3 25–50

4 50–75

5 75–100

Londo scale (1975) 0.5 0.1 <1

1 0.2 1–3

3 0.4 3–5

5 1 5–15

8 1− 5–10

10 1+ 10–15

15 2 15–25

20 3 25–35

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Table 4 (continued) Scale Scale Cover (%) Number of plants of each species

25 4 35–45

30 5 45–55

40 5− 45–50

50 5+ 50–55

60 6 55–65

70 7 65–75

80 8 75–85

90 9 85–95

100 10 95–100

Domin-Krajina scale (1959) + Insignificant Solitary adapted by Mueller-Dombois 1 Insignificant Seldom

and Ellenberg (1974) 2 <1 Very scattered

3 1–5 Scattered

4 5–10 Any number

5 10–25 Any number

10 100 Any number

magnitude” (Braun-Blanquet1932) or species significance (Bonham1989).

There are some studied transformation between scales and percentage for the estimation of species cover. There is a well-known transformation between Braun-Blanquet and Domin- Krajina scales. The Expert panel on Ground vegetation assessment (Dan Aamlid et al. 2002), in the “Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of the effects of air pollution on forest. Part VIII. Assessment of ground vegetation” proposed a transformation between Braun-Blanquet, Braknam, Schmidt, and Londo scales and the percentages for the estimation of species cover (Table5).

Biomass (height, diameter) Biomass is considered by several authors as a primary vegetation measure (Bonham1989; Somogyi et al.2006).

Tree and shrub biomass is usually estimated from dimensional analysis (indirect method).

Through other measures such as total height, DBH, basal area, or crown diameter, it is possible to predict biomass from regression equations. But shrub biomass is sometimes estimated by the “reference unit technique.” It consists in considering

a small unit of the plant (e.g., a small branch) as a reference, clip, dry, and weigh it and estimate the number of the references that form the shrub.

To estimate shrub biomass from dimensional analysis, crown diameter has been used with not bad correlations, but crown volume is considered an adequate predictor of the total leaf biomass (Bonham 1989). To get the volume–weight re- lation is necessary to establish the crown shape (conical, spherical, etc.) and to measure at least one diameter (two in irregular shapes) and height.

The European FORSEE project (2004–2007) is working in this way. Porté et al. (2005) in the project “Détermination de la biomasse aérienne du sous-bois de peuplements adultes de Pin mar- itime: contribution à la quantification des stocks de carbone forestier à l’aide d’indicateurs de couvert” made biomass measurements per species group (woody species, fern, herbaceous species, and mosses). They collected total aerial living biomass for a small number of samples.

In a high number of samples and also in the pre- viously described small number of samples, they calculated a volumetric index per species group (IFN estimation) with the following equation.

VI=

% cover×height

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Table 5 Cover scales transformation

From the “Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forest.

Part VIII. Assessment of ground vegetation”

Braun-Blanquet Brakman et al. Schmidt Londo Domin-Krajina

scale scale scale scale scale

R R r

+ + + 0.5 1

1 +p 1

+a 3

+b 5

1p 1a 8 2

1a 1b 10 3

1b 15

20 25 2m

2 2a 2a 4

2b 2b 5

3 3 3 30

40 6

50 7

4 4 4 60 8

70 75

5 5 5 80

90 9

100 10

And finally, they calculate total biomass by a rela- tion VI-dry weight.

In the same project, Silva et al. (2006) determines the forest shrub biomass with the following formula:

B_j=

iδb_s×Pc_si×A_i

Where Bjis the shrub biomass in plot j (Kg/m²), δbsis the apparent density of the s specie (Kg/m³), Pc_siis the coverage of the specie “s” in the height class “i,” and A_iis the height of class “i.”

Anyway, to measure herbaceous or mosses biomass, the most frequent methods are to clip or to harvest (destructive methods) or guess amount of biomass in quadrats (indirect method; Bonham 1989). In the destructive methods is to weight and dry the vegetation is needed. In large-scale surveys, a few samples of each species can be dried;

the “weight estimates and double sampling” is one of the most common used methodologies. In this method, the visual method is combined with the destructive method. There is a large sample that contains only observations and a small one that contains clipped weights. The small sample is

used to calculate a regression to estimate the large sample biomass.

For National Forest Inventories (due to the costs and the extension), only nondestructive methods are recommended (See “Attributes measured” and “Conclusions and recommendations for NFIS”). Somogyi et al. (2006) point at the factor that forest inventories can provide representative dimensional data such as volume, height, or diameter, but the biomass factors or equations used are in most cases not representative because they are based on local studies.

Areas: mapping and charting methods

Cover Plant cover is determined by drawing to scale the outline of the crown or basal areas of plants on a sheet of graph paper. Charting or mapping may be done with a grid quadrat or a pantograph. This method is used successfully into grasses communities (Bonham1989), and it is less efficient than intercept techniques (Bonham 1989).

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Areas: photographic methods

Cover Photographic techniques based on vertical stereophotography are objective, rapid, and does not need a trained field worker, but they require a later analysis. A software program can be used to obtain the covers. This method is not suitable when vegetation has several layers ex- cept to determine dominant species composition (Bonham1989).

Data process

Ground vegetation biodiversiy indicators

There are three basic indicator functions: sim- plification, quantification, and communication.

Indicators generally simplify in order to make complex phenomena quantifiable so that information can be communicated (Delbaere 2003).

Biodiversity indicators support communication about the state of biodiversity in a selected region.

The target group for biodiversity indicators consists of two groups: those providing the data on the indicators and those making policy decisions on the basis of the message expressed by the indicators (Delbaere2003).

There are several institutions defining biodiversity indicators as European Topic Center on Nature Protection and Biodiversity (ETC/NPB).

But, it is necessary to define the indicators each project can provide and the reliability. The proposed selection indicators have been selected considered large-scale inventories.

Composition

Forest types The identification of forest types depending on species composition is the first indicator, and all the rest will be conditioned by this classification. That is why it is so important to have a standardized classification. Ground vegetation is directly related with forest types.

Floristic composition Ground vegetation studies are based on the analysis of the determination of all the present species. Therefore, to make complete monitoring is necessary to have experts and to go in certain season periods of the year for

some of the species. These two conditions are not always possible in large-scale inventories. Species list with a short number of them are usually used in NFIs.

With floristic composition, there are several indicators or factors that can be deduced as the following ones (8.1.3, 8.1.4, 8.1.5).

Endemic species, introduced species, key species, threatened (extinct, endangered, rare, and vulner- able) species Taxonomical studies are needed to determine presence of certain species that must be recognized for its meaning. Those data could be used for example to protect certain areas. It must be noted that for threatened species, inventories must be design in a different way; plots or transects must be normally located be deliberated.

Species richness There are many indices based on species number and its abundance. All these indexes (e.g., Margaleff, Menhinick, Berger-Parker, Simpson, and Shannon) are commonly used, but they have been highly criticized as they heavily depend on plot area. The ICP FOREST experts (2003) recommend, in addition to species number, evenness index (Pielou1969) because it is almost unbiased by different sampling areas.

Ecological succession To estimate forest biodiversity status is essential to know forest natural- ness. When there is enough information about vegetation, succession phase is very important to know how far the current forest is to the potential vegetation. This is also related with the stability degree.

Ecological sequence or natural succession can be defined as the dynamic process of coloniza- tion of a virgin biotope by living organisms that in progress increase their biomass. At the same time, by autoregulation, mechanism and control recourses get a higher degree of complexity in their structure. The final process culminates in stable equilibrium in the biotope conditions, the climax. That vegetal component is called potential vegetation. The potential vegetation would be forest evolved as a sequence of natural succession in a relative large period without anthropogenic influences (Aguilo et al.1992). It is very important to realize that climax stage of a natural forest

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ecosystem is not constant but a mosaic of different phases consisting of different plant communities, different species of flora and fauna as well as hanging soil development stages (Schuck et al.

1994). Climax stands could have natural or anthropogenic disturbances suffering degradation or regression stages.

Ground vegetation is a key component to determine the succession stage and to be able to define how close is to the climax vegetation.

To consider the development of the succession theory, it is important to differentiate between the holism and the reductionism. Walker (2005) gives a good description of both in the article “Margalef and ecological succession”:

...Holists focus on changes in diversity, productivity, biomass, nutrient cycling efficiency and other ecosystem characteristics as well as the general directionality and predictability of succesional trajectories that end in a single climax. Reductionists emphasize distur- bance, stochasticity, species life histories and species interactions, believing hat succession is a largely unpredictable consequence of each species’ unique interactions, with its abiotic and biotic environments (Gleason 1926; Glenn-Lewin et al. 1992). The two approaches can be integrated, sequentally or simultaneously, as when one begins by focusing on particular species responses but ends by integrating the details into a general energetic framework (McIntosh1985)....

There is, therefore, a story of two competing research traditions, holistic, and reductionistic.

The holists believe that ecological systems exhibit order, structure, and regularity at population, community, and ecosystem levels of organization while reductionists believe that ecological systems are nothing more than assemblages of individual species populations whose behavior is primarily determined by response to local environmental conditions (Odenbaugh and de Laplante2006).

As Maurer (1999) suggests, holistic studies might be used to design specific management plans while reductionistic focus might suggest how to apply the experimental results to a wider group of organism. The two approaches contribute information about the communities; in fact, they pro-

vide complementary information (Maurer 1999;

Walker 2005). What are needed are coordinated programs that simultaneously examine communities from multiple scales (Maurer1999).

Structure

Number of layers There have been many pro- posals to classified vegetation. Raunkiaer (1934) defines five categories (phanerophytes, chamae- phytes, hemicryptophytes, cryptophytes, and tero- phytes) from which many studies used them.

Danserau (1957) divide biotypes into six categories: trees, shrubs, herbaceous, bryophytes, epiphytes, and lianas (of course, trees and epiphytes are not a part of ground vegetation).

Ellenberg and Mueller-Dombois (1967a) developed Raunkiaer classification. Küchler (1967) made a classification based in the main physiog- nomic characteristics and a subdivision depending on height and cover.

There have been also classifications based on height as the one proposed by Godron et al. (1968) in ten different classes (six of them can be considered form by GV) or the one proposed by ICP experts (see “Photointerpretation and geographic information systems”).

Sociability of each group of species or each species Qualitative and descriptive aspect are derived from vegetation pattern. The most popular classification is the one establish by Braun-Blanquet (1932):

1. Species growing solitary

2. Species forming clumps or dense groups 3. Species forming small patches or cushions 4. Species growing in small colonies

5. Species growing in large, almost pure stands Cover of each group of species or each species It is one of the most indicative measurements of vegetation structure. Due to its estimative character, a learning period for the technicians is needed.

This estimation is cheap and very useful accepting that it will never be exact measurement.

Biomass of each group of species or each species This is an expensive and no realistic parameter for large-scale inventories with a direct method

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but due to the needs of studding carbon pools, it would be a very interesting indicator. It should be calculated with dimensional analysis and estab- lishing functions of dry weight. It could be very useful in future to use “Forsee projects” results.

Calculation of the variables, including statistics estimations

The variables used can be directly the cover values, values from any scale, biomass influence potential (Kuuluvainen and Pukkala 1989; Økland et al. 1999; Saetre1999), ground vegetation production (Ford and Newbould 1977), gross pho- tosynthetic production (Kolari et al. 2006), and carbon balance (e.g., Goulden and Crill1997; Law et al.1999).

If the data are in ranges, majority of the times, it is necessary to convert them in unique values (e.g., the average of the range). If measurements are not in the appropriate form for the statistical analysis, they will be adapted.

Usual statistic analysis like ANOVA (Zenner et al. 2006; Zobel 1998) and correlation studies can be applied over the pieces of information from the field works. Each survey must decide which analysis is better to reach its own aims.

Anyway, when multiple measurements are made, there are often correlations among the measurements. It is this dependence that differentiates multivariate from univariate analysis, in which is assumed that the observations are independent one from another. Unless the homogene- ity of treatment differences variance assumption is met, one should turn directly to multivariate procedures (Gurevitch and Chester1986).

Ordination methods are widely used in ecology for gradient analysis or the study of species disyti- butions along gradients. Some of these analyses are: detrended correspondence analysis (DCA;

Hill and Gauch 1980); Spearman’s Rank Corre- lation Coefficient (Scott 1970); principal component analysis (PCA; ter Braak and Smilauer1998;

Zobel1998), and canonical correspondence analysis (CCA; ter Braak1986).

PCA provides solution to the multidimensional linear ordination problem if the errors are inde- pendently and normally distributed with constant variance across species and sites.

DCA is supposed to be an efficient ordination technique when species have bell-shaped response curves or surfaces with respect to environmental gradients and is, therefore, more appropriate for analyzing data on community composition and environmental variables than CCA (ter Braak1986).

It is important to consider that unlike PCP and CA, DCA does not produce the arch or horseshoe effect. Anyway, there are many authors against this method; Palmer (1993) indicates that CCA is immune to most of the problems of DCA and for instance, Wartenberg et al. (1987) recommends reporting the arch unscaled in two dimensions, even though it is a one-dimensional form.

At present, the importance in some surveys of the ground vegetation is developing software programs (BioCalc software) to facilitate the calculation of the variables; also it is possible to use general vegetation computer programs [PC-Ord 4 (McCune and Mefford 1999)]. In other cases, the proper researchers develop their own software (Evans et al.2006).

Model chains for assessing impacts of biodiversity Nitrogen (N) and sulfur are the main pollutants causing acidification, eutrophication, and changes in biodiversity. Sulfur deposition can be quantified with geoquemical models. But the majority of N transformation processes are biologically medi- ated (Rowe et al.2005).

Rowe et al. (2005) indicates that estimations of N availability are needed as an input for biodiversity modeling and that to predicting responses to nitrogen pollution is better divided into two parts:

Predicting changes in N availability as a consequence of N deposition and soil and plant processes

Predicting changes in species composition as a consequence of this level of N availability There are some methods for predicting soils, water, and plant responses to N pollution. Some chains also include a model of vegetation succession. There are four main model chains mentioned by Rowe et al. (2005):

1. FORSAFE-VEG

2. SMART2-SUMO-MOVE-NTM

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3. MAGIC-GBMOVE 4. VSD-BERN

To construct the relationships between species and environment, Ellenberg indices have been used in those models. Abiotic measurements are not available for all the plots and in NTM and GBMOVE models relationships were first derived from floristic data as mean Ellenberg scores (Rowe et al. 2005). Ellenberg produced a set of indicator values referring to the ecological pref- erences of plant species growing in natural com- petition in Central Europe. Separate indicator values, each on an integer scale from 1 to 12, refer to individual ecological parameters such as light availability, continentally, and soil moisture availability. These values were produced by largely subjective means, based on extensive experience of the ecosystems of Central Europe over many years (Wilson et al. 2001). These can be seen as indicating the maximum probability of occurrence of each species or its environmental optimum. In Southern Europe, there are many species with no available scores. There have been some studies in the Mediterranean area as the one made by Madotz (2004) in the Cantabric region of Spain using those indices showing that more intensive studies are needed.

Rowe et al. (2005) describe the main characteristics of the use of Ellenberg indices in the document “Model chains for assessing impacts of nitrogen on soils, waters and biodiversity: a review” of the Joint Expert Group on Dynamic Modelling Working Group on Effects. Conven- tion on Transboundary Air Pollution:

Mean scores for these indicators can be used to describe a plant assemblage (Pitcairn et al. 2004) or for prediction by biogeo- chemistry and vegetation type models. Mean Ellenberg fertility (EbN) scores have been shown to be good indicators of soil N availability (Van Dobben 1993), although the relationship usually shows large variation (Wamelink et al.2002) and appears to corre- late best with annual above-ground biomass production rather than soil nutrient status (Hill and Carey 1997; Schaffers 2002). This

variation may be reduced by forming relationships separately for different phytoso- ciological groups (Wamelink et al. 2005).

According to Van Dobben (1993), the relationship between N availability and mean EbN score is better when mean EbN is sim- ply based on presence/absence data rather than being abundance-weighted. This may be because species presence/absence are less subject to inter-seasonal variation than cover.

Therefore, it is a long way for harmonizing this model at large scale. But these are very good efforts to model plant diversity.

Ground vegetation in National Forest Inventories After the review of the different projects and measurements for the estimation of ground vegetation biodiversity, it is really interesting to understand how NFIs are describing GV and to propose some useful finest indicators for such important net- works. Most of the information shown in the NFIs analysis was provided by the different countries in the frame of the Action Cost E-43 project “Har- monization of National Inventories in Europe:

Techniques for Common Reporting.”

Definition

Ground vegetation is not been measuring in all the NFIs. For example, Greece, Hungary, and Latvia do not make a ground vegetation sampling and in Finland, understory vegetation is not included in the current NFI, but vegetation has been included in the NFI in three previous cycles.

Firstly, it is remarkable that ground vegetation considered layers are very different depending on each country. Small trees, shrubs, seedlings, and saplings are the most problematic groups. Only a few countries are assessing all the life forms (that can be grouped in layers), as Sweden and UK, and there are some countries monitoring only bushes (Italy, Lithuania, and Norway; see Fig.1).

• Small trees layer. Small trees are distinguished from trees in some countries but they will not

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Fig. 1 List of countries assessing the different ground vegetation life forms

be considered as GV group by consensus with the COST E43 experts. e.g.: Germany consider them as “sample trees dbh <7” cm or Romania “small, perennial, woody plant, with 7–15 m height at maturity.” In some others countries, the concept is not clear.

• Shrub layer. Shrubs attributes are measured in every country, but the definitions are also different. The criteria used are based on height mostly, on a species list or if it is ligneous or not or a combination of them. It is also important to distinguish between the ones based on potential height (e.g., France, Romania) and the ones based on real-present height (e.g., Austria, Belgium, Germany, and Italy).

The harmonized definition would imply an agree- ment on the threshold or a complete species list.

There could be some species determined as shrubs in some countries and in other layers in others.

Also, in some cases, the same bush species could have different height depending on the biogeo-

graphical region (Table6). As height appears as the most common criterion used to differentiate the strata, it should be the major parameter, but it could not be enough. Maybe also the main biological characteristics could be added like growing parameters, ligneous or not, etc.

Different definitions on NFIs and its thresholds are shown on Table7.

– Herbaceous layer: this layer is not been assessed for every country. For most of the countries the definition could be the same as given by Germany NFI “herbaceous seed plants (nonwoody or only woody at the base of the shoot)” but there are some countries that include only the “nonwoody species” as France or Austria. There are also some countries that subdivide this layer into:

• Grass and herbs: Austria, Germany, and Ireland

• Forbs, graminoids: Estonia

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Table 6 Shrub criteria definition in NFIs

Country Based on Including

Species list Growth form Size (height) Sprawling shrubs Lianas, climbers Palm shrubs

Austria Yes Yes Yes Yes Yes –

Belgium No Yes Yes Yes No –

Cyprus Yes No No Yes No No

Czech Republic Yes Yes No No No –

Estonia No Yes No Yes No Yes

France No Yes No Yes Yes –

Germany Yes Yes Yes Yes Yes –

Italy Yes No Yes Yes Yes Yes

Ireland No Yes Yes Yes Yes –

Lithuania Yes No No Yes na –

Romania Yes No Yes Yes No –

Slovaquia Yes No No No No –

Spain Yes Yes Yes Yes Yes Yes

Switzerland Yes No No Yes Yes Yes

UK Yes No No Yes Not available –

• Graminoids, leguminous, others: Spain and Portugal

• Broadleaf grasses BLG, Fine Leaf grasses FLG. UK

– Ferns: They are usually defined by NFIs as vegetation life form (Pterydophyta species), (Austria, France, Spain) or/and by species/

taxon list (Czech Republic, France, Slovakia,

or Germany) but sometimes is defined by height, strata, e.g: Belgium includes them in herb strata and Switzerland contains them in a layer formed by herbs, ferns, and low shrubs.

– Terrestrial lichens: terrestrial lichens have not been measured by most of the countries, but there are some exceptions as Czech Republic which is differentiating three morphological groups or Sweden which is determining several lichen species.

Table 7 Shrub NFIs

definition Countries NFI definitions

AT Usually from 0 up to 5 m

BE By strata

CY List of species

CZ List of species or group of species EE FRA 2005 shrub definition

FI Not inventoried

FR Woody species that are not tree species and with a potential height≥2 m DE List of species and also height classes: shrubs<0.5, 0.5–2, and>2 m IT DBH<5 cm; Height>50 cm; included in the list

LT Species list

NO Not inventoried

POR N/A

RO Small, perennial, woody plant, with 0.3–7 m height at maturity

SK Yes

ES Species list. For cover assessment we use the followings: 0–0.5, 0.5–1.5 m

SE No

CH Woody plant species with a minimal height of 40 cm on the sample plot/species list

UK N/A