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Indicators of forest degradation – biodiversity
Ian Thompson, Robert Nasi, Kimiko Okabe, Valerie Kapos, and James Gordon
1.2 Introduction to forest biodiversity indicators:
Forest-associated biodiversity is one of several criteria by which forest degradation can
be assessed. We are not proposing a single ‘biodiversity score’ or cumulative index,
rather we suggest that loss of biodiversity needs to be assessed independently for several
key indicators. These indicators would be scored against an a priori expectation of level
or kind of biodiversity (e.g., number of certain species, populations of functional species,
numbers of ecosystem types, etc.) to determine a level of degradation for each indicator
and for the forest stand or landscape. At the very least, an indicator requires two points in
time, or a measure against a control value. The proposed biodiversity indicators would
form a common set that could be employed to determine the amount of degradation in a
local forest, regardless of the forest type. The actual component, however, (e.g., a
species or a forest type) being measured would obviously differ depending on the local
forests.
Degradation differs from forest loss but some loss of forests across a landscape can
degrade the larger area from a biodiversity perspective. For example, Andren (1994)
suggested a threshold of 30-40% forest loss across a landscape resulted in non-linear
declines in species occurrence. This threshold value has since been tested for various
species and landscapes with the result that generality is difficult and thresholds depend on
the species of interest and forest type (e.g., Betts and Villard 2008). Hence, thresholds
may need to be determined based on expected range of variation for each ecosystem,
community, or species of interest.
Ecologically, biodiversity objectives relate in large part to the functioning of the
ecosystem. This includes important ecosystem services provided by biodiversity such as
pollination (by bats, birds, and insects), decomposition (soil arthropods, fungi, or micro-
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organisms), seed dispersal (insects, birds, mammals, fish), resilience, disease reduction,
etc. Such processes are also affected by the scale at which they are assessed.
Biodiversity indicators for forest degradation should be assessed for two scales:
landscapes (multiple stands) and stands (individual groups of trees distinguishable from
other surrounding groups of tree by their species composition). Both scales are important
and both require a different, but sometimes overlapping, set of indicators. In many cases,
scaling up from stand to landscape will be required for reporting degradation. Indicators
must be relatively uncomplicated to use in terms of data collection and easily repeatable,
especially for countries with limited resources. The indicators must also be unambiguous
and provide quantitative data that can be used to assess trends over time.
2.0 A summary of biodiversity indicators from other forest-related processes:
The following list reflects sustainable forest management (SFM) indicators from
indicator processes (Table 1), certification processes, and indicators as suggested in Sheil
et al. (2004) and Loh et al. (2005). SFM and forest degradation are not the same
consideration and so many of these indicators are not helpful. However, if some of these
indicators are useful for both and are possibly being collected for SFM, then it makes
sense to use those indicators to suggest degradation as well.
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Table 1. Biodiversity indicators for sustainable management from five different indicator sets or processes. Indicators relevant to
indicate degradation are marked with an asterisk.
Process or
attribution
ITTO
Montreal
Process
Landscape
Ecosystem
Species
Genetic
5.1 Forest
protected area
*5.4 Number of listed
species
*1.1.c
Fragmentation of
forests
*5.6 Measures in
place to protect listed
species, species of
interest, keystone
species, and seed
trees.
1.2.a Number of
native forest
associated species
5.5 Measures for
protection of
genetic diversity of
commercial
species or listed
species
*1.1.a Area and percent of
forest by forest ecosystem type,
successional stage, age class,
and forest ownership or tenure
1.1.b Area and percent of forest
in protected areas by forest
ecosystem type, and by age
class or successional stage
*1.2.b Number and
status of native forest
associated species at
risk, as determined
by legislation or
scientific assessment
1.2.c Status of on site
and off site efforts
focused on
conservation of
species diversity
*1.3.a Number and
geographic
distribution of forest
associated species
at risk of losing
genetic variation
and locally adapted
genotypes
*1.3.b Population
levels of selected
representative
forest associated
species to describe
genetic diversity
1.3.c Status of on
site and off site
efforts focused on
conservation of
genetic diversity
Health
Other
*3.a Area and
percent of forest
affected by biotic
processes and
agents (e.g.
disease, insects,
invasive species)
beyond reference
conditions
2.a Area and
percent of forest
land and net area
of forest land
available for
wood production
*3.b Area and
percent of forest
affected by abiotic
agents (e.g. fire,
storm, land
clearance) beyond
reference
conditions
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Convention
on Biological
Diversity
*7.1 Patch size
distribution,
connectivity and
fragmentation
*7.2 Area burned
*5.1 Change in
forest area
1.1 Percentage area of forest
protected by forest type
*1.2 Percentage of threatened
or vulnerable ecosystems
protected
*5.2 Forest areas by class:
primary, modified natural, seminatural, plantation
*2.1 Changes in
abundance of
populations of
selected species
2.2 Changes in
distribution of
selected species
*2.3 Number of listed
species by category
3.1 Area managed
for ex situ
conservation of
forest genetic
resources
3.2 Area
managed for in situ
conservation of
forest genetic
resources
*6.1 Number of
invasive species in
forests
6.2 Number of
invasive species
controlled
*4.1 Percentage
of forest area
under
management that
is certified
*5.3 Area of
degraded forest
*6.3 Area of forest
affected by IAS
*7.2 Area burned
*2.4 Changes in
status of individual
listed species
Biodiversity
Indicators
Partnership
*9.3 Forest
fragmentation
*1.1 Extent of forests and forest
types
2.1 Living planet
index
1.2 Extent of selected habitats
2.2 Global wild bird
indicator
5.1 Ex situ
collections
*4.7 Landscape
- level spatial
pattern of forest
cover
*4.3 Area of forest and other
wooded land, classified by
“undisturbed by man”, “seminatural” or by “plantations, each
by forest type
*4.4 Area of forest and other
wooded land dominated by
introduced tree species
*4.5 Volume of standing
*4.8 Number of
threatened forest
species, classified
according to IUCN
Red List categories in
relation to total
number of forest
species
6.1 Area
managed and
certified
6.2 Area
managed that
has been
degraded and
deforested
*4.1 Change in the
status of listed
species
MCPFE
Forest
Europe
Indicators
*8.2 Number and
trends of AIS
*4.6 Area managed
for conservation
and utilisation of
forest tree genetic
resources (in situ
and ex situ gene
conservation) and
area managed for
seed production
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deadwood and of lying
deadwood on forest and other
wooded land classified by
forest type
*4.9 Area of forest and other
wooded land protected to
conserve biodiversity,
landscapes and specific natural
elements, according to MCPFE
Assessment Guidelines
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3.0 Indicators of degradation based on maintaining biological diversity
The proposed biodiversity indicators would apply for all forest types for managed
(including agro-forests, which are not classified as forests in FRA), used but unmanaged,
and primary forests. For managed forests, the management objectives might be related to
any goods and services, such as game animals (bushmeat), other NWFP production such
as certain tree species used for carving or crafts, other foods, wildlife viewing
possibilities, etc., or they could relate to maintaining all species in time and space. The
criteria for indicator selection included: sufficiently generic to apply globally, techniques
available to allow measurement, possible existing data sources, reflect a change of
biodiversity, potential to be scaled up, and indicates a change in ecosystem goods and
services. Ideally, it would be advantageous if indicators could be sensed remotely, but
for biodiversity this is not possible for most because many indicators relate to species or
structures that must be found on the ground. Hence, not all of the proposed indicators
can be sensed remotely and the ones that are ground-based could be viewed as correction
factors for forests reported as ‘not degraded’ or ‘possibly degraded’ based on satellite or
other imagery. To that end, a stratified sample will be required for each forest type to at
least the level of sub-biomes for ground sampling.
From among the proposed biodiversity indicators, the minimum indicator set that should
be used to assess forest degradation from a biodiversity perspective is ‘ecosystem state’
and ‘forest fragmentation’. Both these indicators can be determined through remote
sensing. Ground-based indicators are more difficult and labour-intensive for data
collection, but are necessary to obtain a full understanding of the possible degradation
condition. The technical rationale for the suite of selected biodiversity indicators of
forest degradation (Table 2) follows below.
Table 2. Proposed biodiversity indicators of forest degradation.
Indicator
Measurement method
Relevant case
studies or data
Scale of
measurement
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source
Remotely-sensed indicators:
Ecosystem state (resilience)
Satellite or aerial
photographs: expected
forest type for soil and
moisture condition
Surrounding area,
PAs etc.
Stand or
landscape
Fragmentation/intactness and
road density
Satellite or aerial photos:
area deforested, roads/km2
UNEP-WCMC,
WRI
Landscape
Ecosystem diversity
Satellite or aerial
photography: extent of each
ecosystem type
NFI
Landscape
(stand)
Species 1: Expected
community composition by
forest tree species for the
ecosystem type
Ground plots: species
composition
Individual
research, gov’t
surveys, expert
opinion, IUCN list
Niger WP 168
Ghana WP 160
India WP 157
Nepal WP 163
Stand and
landscape
Species 2: Key indicator
species including threatened
species, old forest species,
and hunted species*
Surveys for change in
population size (relative or
absolute)
IUCN
Stand,
landscape
Species 3: Invasive alien
species**
Remote sensing or ground
surveys: area of forest
affected
Surveys for change in
population size, surveys for
expected function products
(e.g., fruit production)
Species-based indicators:
Species 4: Functional species
Stand,
landscape
Stand
*hunted species (bush meat) dealt with under ‘Forest Goods’
**invasive species dealt with under ‘Forest Health’
3. 1 Technical rationale for ecosystem state as an indicator of forest degradation
Forest state refers to the ecosystem type expected for a given stand and infers to the longterm resilience of the forest ecosystem. If the resilience is overcome through
disturbances, the ecosystem state will change. The main ecosystem states of interest are
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defined by the dominant floristic (tree) composition and stand structure expected for a
given stand. Capacity for resilience and ecosystem stability is required to maintain
essential ecosystem goods and services over space and time (Thompson et al. 2009).
Loss of resilience may be caused by the loss of functional groups caused by
environmental change such as climate change, or a sufficiently large or continual
alteration of natural disturbance regimes (Folke et al. 2004). Loss of resilience results in
a regime shift, often to a state of the ecosystem that is undesirable and irreversible.
Resilience needs to be viewed as the capacity of natural systems to self-repair based on
their biodiversity, hence the loss of biodiversity will often mean a reduction of that
capacity.
However, some changes in the relative abundance of dominant species may occur
following a disturbance with little apparent consequence to the ecosystem. In some
cases, functional roles may also change among species but the forest maintains its
resilience with respect to its capacity to provide certain (most or all) ecosystem goods and
services, even if the forest composition and structure are permanently altered by
disturbances. This ecological resilience (Gunderson 2000, Walker et al. 2004) is strongly
dependent on biodiversity (Thompson et al. 2009), and is the focus of this indicator for
management purposes. As noted above, change in ecosystem condition may be best
measured using several other indicators: species composition, biomass production, etc.
However, a large change in forest state, regardless of cause, will result in a forest that
produces different goods and services that might be derived from the expected forest
type. A major negative change in state from one forest type to another is a clear
indication of degraded forest (Thompson et al. 2009).
A negative change in state refers to a loss of resilience and a shift in the system to a
completely different ecosystem, with a consequent reduction and change in goods and
services. For example, if a forest is expected to be of mixed species but instead it is
actually mostly uniform, or it should be closed canopy but is actually open or savannah
etc., then the state has changed. These are negative changes in state that would be
reported as degraded forest from a biodiversity perspective. For example, Souza et al.
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(2003) mapped degraded forest classes in the Amazon, defined as heavily burned or
heavily logged and burned using satellite data. A relatively simple index of forest
degradation could be a sum of the area of atypical or unexpected forest types on a given
landscape, such as area of open canopy forest in a closed canopy landscape. These
changes are relative to the forest that would be expected on a given site or landscape.
Hence, the indicator is: area of forest that has changed state in a negative fashion.
3.1.1 Method for stand and landscape-scale monitoring:
1. Develop or use a forest classification system that reflects the available data: such as for
few data, use broad forest type (open, closed, deciduous, mixed species, etc.), or with
better data, an ecosystem or forest type classification (e.g., mixedwood forest dominated
by Acer sp. on mesic soils, etc.), and apply the system over a landscape based on
expectation from local knowledge, soil types, and known moisture regimes.
2. Map forest stands based on their condition using remote sensing or ground surveys and
report area of stands in states other than expected.
3. Report area of forest that occurs in an unexpected or undesired state.
3.2 Technical rationale for forest fragmentation as an indicator of forest
degradation
Land use change and other forms of disturbance often lead not only to a reduction in
overall forest area, but also to division of remaining forest into smaller and smaller
pieces. A certain amount of fragmentation on a landscape is unlikely to result in loss of
biodiversity, but thresholds occur that are system and species specific (e.g., Fahrig 2003).
In some cases fragmentation may have a positive effect on some animals and animal
groups, with fragmentation leading to higher levels of biodiversity in a given area.
Negative effects tend to depend on the level of fragmentation, the forest type and the
animals and plants of interest.
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Forest fragmentation poses a substantial threat to global biodiversity and may cause
cascading impacts on a wide range of ecosystem functions and services depending on
thresholds (Wu et al. 2003, Millennium Ecosystem Assessment 2005). When land-use
change breaks tracts of continuous forest into smaller pieces, it also creates new edges
between forest and other vegetation types and disconnects patches from adjacent,
continuous habitat (Collinge 1996, Fahrig 2003, Saura and Carballal 2004). There is a
wealth of information that has been produced regarding forest fragmentation and its
impacts on biodiversity (e.g., see reviews by Fahrig 2003, Fisher and Lindenmayer
2007). A review (Fazey el. 2005) of publications of conservation biologists found that
habitat fragmentation was the largest single area of study in conservation biology. Large
animals and top carnivores, which are well known to require large areas of habitat, are
especially vulnerable to the reduction in habitat area caused by forest fragmentation, and
they may disappear entirely from forest patches because food or other resources are
inadequate to support them. Smaller species are also affected, and disappearance of some
species from forest fragments can profoundly affect the forest itself, for example through
changes in seed dispersal and regeneration. Even species that persist do so in smaller
populations, which may be vulnerable to other ecological changes such as disease,
predation, or Allee effects (i.e., reduced breeding because of low population density).
Rare species and those that normally occur at low population densities are especially
vulnerable to these kinds of effects. The edges of forest patches are associated with
environmental gradients that affect ecological processes including weather effects,
canopy gap formation, biomass and nutrient cycling changes, regeneration, invasion, and
altered levels of predation. For example, invasive species are often favoured by an
increased incidence of forest edges within the landscape. The separation of forest
fragments from each other and from larger blocks of forest reduces the movement of
species that are reluctant or unable to cross non-forest areas and increases the chance of
local extinction of individual species. Overall, these area, edge and isolation effects can
singly and in combination adversely affect local populations of many organisms and
increase their vulnerability to stochastic events, leading to population decline or
extinction (Driscoll and Weir 2005, Arroyo-Rodríguez et al. 2007)
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Natural ecosystems, especially forests, have become increasingly fragmented on a global
scale because of forest development. Increasing universally high levels of forest
fragmentation is a major cause of well-documented reductions in the distribution and
abundance of individual species and on the species composition of many forest
communities, especially in temperate and tropical forests (e.g., Laurance et al. 2002,
Kupfer et al. 2006, Watling and Donnelly 2006, Ewers et al. 2007, Fischer and
Lindenmayer 2007). Empirical evidence shows that fragmentation has significant and
largely negative implications for biodiversity through impacts on species composition
and stand structure of the altered spatial patterns (e.g. area reduction, reduced interior
space, increased edge exposure, isolation) (Fahrig 2003). Alteration of forest spatial
patterns affects biodiversity in both tropical and non-tropical forests (Wade et al. 2003).
There is also evidence that forest fragmentation may reduce total carbon storage at the
landscape scale (Groenveld et al. 2009) and that hydrological cycles are appreciably
altered by forest fragmentation causing changes both in evapotranspiration and local
climates (REF) and changes in run-off (Ziegler et al 2007). Fragmentation appears,
therefore, to be an excellent indicator for biodiversity degradation for all types of forests,
except possibly boreal forests where, at least in managed landscapes, fragmentation is
ephemeral (Thompson and Welsh 1993).
Fragmentation is usually defined as a process involving both the loss and the breaking
apart of formerly continuous habitat. Fahrig (2003) noted that empirical studies of
habitat fragmentation are often difficult to interpret because of (a) many measures
fragmentation at the patch scale, not the landscape scale, and (b) most measures of
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fragmentation do not distinguish between habitat loss (deforestation) and habitat
fragmentation per se, i.e., the breaking apart of habitat after controlling for habitat loss
(degradation). Fragmentation has come to mean many different things to different people
and has lumped together many interacting processes and spatial patterns that accompany
human landscape modification (Lindenmayer and Fisher 2007). Figure 1 (from Estreguil
and Mouton) shows the processes often lumped under the term fragmentation.
A further complexity is added by perspective when studying or reporting results from
fragmentation studies. A small arboreal mammal will perceive a road or a treeless area as
a barrier, so its habitat has been fragmented, but a large ground-dwelling herbivore may
consider treeless as useful paths or food patches, and so its habitat has not been
fragmented. Connectivity is organism specific and so is habitat fragmentation. It is
therefore important to make a distinction between habitat loss and loss of native
vegetation cover because some species can survive or thrive in modified landscapes.
Some naturally fragmented landscapes (like the savannah-forest mosaic of coastal
Gabon) are extremely species rich and letting the habitat return to a 100% forest cover
would result in a decrease in biodiversity measured as number of species (but in a net
gain in carbon stocks).
Keeping all these elements of complexity in mind, we can however suggest that if our
baseline is a primary forest ecosystem (that can be fragmented naturally) or a sustainably
managed forest, then an increase of fragmentation over expected natural levels is
generally indicative of degradation, and needs to be objectively assessed against
management objectives from a forest degradation perspective. There will always be
some exceptions; one example being that in Costa Rica an increase in forest area has
resulted in an increase in fragmentation index, because there has been the establishment
of more small patches of forest, either naturally or by planting.
3.2.1 Fragmentation metrics
The most common source of mapped data for forest cover is remote sensing of various
types. On the whole, fragmentation data derived from remote sensing at higher spatial
resolution, such as Landsat and Spot, are easier to interpret because they relate more
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directly to forest distribution on the ground. Coarser resolution remote sensing, such as
MODIS, MERIS and Spot Vegetation can also be useful for assessing changes in forest
fragmentation, but may obscure finer-scale fragmentation that can be important for some
components of biodiversity (species) and some ecosystem services. Data derived from
aerial surveys may also be a useful (though expensive) source of forest cover data for
assessing changes in fragmentation. Whichever data are used, it is essential that both the
raw data and the ways in which they are processed (including rectification, correction,
and classification) be comparable for all the time periods being assessed. In some cases
this may require specific tests of comparability.
Several metrics can be used to assess fragmentation, some can be used at the entire forest
management unit (FMU) level, others at the within the FMU at the patch level. For an
indicator to be useful its relationship to the values or services of interest needs to be clear
and easily understood by the decision-makers expected to make use of it. To a large
degree, this is dependent on the presentation of the indicator (and on the kind of
investigation described above), but it is also the case that some fragmentation metrics are
more easily understood than others. The most useful potential indicators are those that
represent the major ecological effects of fragmentation (area, edge and isolation effects)
in relatively transparent ways.
Table 3. Proposed best fragmentation measures.
Metric
Calculation
Unit
Relation to degradation
Caveats and
constraints
Mean Patch
Total forest area
Hectares
Decreasing mean patch
Mean patch size
Size
divided by the total
size over time is likely to can increase as a
number of patches
indicate increasing
result of
degradation due to area
elimination of
effects
small forest
patches
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Mean
The mean ratio of
Dimension
Increasing mean
Ratio can decline
Perimeter-
the patch perimeter
-less
perimeter-area ratio can
through the
Area Ratio
to area across all
indicate increasing
elimination of
patches in the
degradation, especially
smaller and more
landscape
via edge effects
complex patch
shapes
Mean
The mean distance
Euclidean
Metres
Increasing mean nearest
Loss of
between all
neighbour distances is
individual
Nearest-
landscape patches,
likely to indicate
isolated patches
Neighbour
based on shortest
increasing degradation
can cause a
Distance
edge-to-edge
through the effects of
decrease in the
distance
isolation
mean nearest
neighbour
distance
Forest
Combined metrics of
Dimension
Declining integrity index
Relationship to
Integrity
patch size,
-less
is likely to indicate a
specific goods
Index (e.g.,
connectivity, and
reduced ability to
and services not
Kapos et al
edge effects
produce goods and
established –
services, and therefore
complexity may
increasing degradation
obscure more
2000)
understandable
trends
For all of the proposed metrics (Table 3) and for most of the other fragmentation metrics
available their utility and interpretation is dependent on having a good understanding of
their relationship to the values and services of greatest interest and on presenting them in
conjunction with information on change in forest area (to minimise the concerns
identified in the column on caveats). They are also only useful if presented as changes in
values over time because many forests are naturally patchy in distribution or have been
fragmented on historical rather than recent time scales. Therefore, establishing a current
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or recent baseline from which to assess change is essential, as is the use of compatible
data sets and analysis methods in consecutive assessments.
Ideally indicators of forest degradation resulting from fragmentation should also be
presented in the context of indicators of the pressures leading to that fragmentation (e.g.
deforestation rates, agricultural conversion, expansion of infrastructure) and the
responses aimed at controlling them (e.g. protected area establishment, other land use
planning and zoning, area under certified management). This will help users to interpret
the trends describe and act on their interpretation.
All these metrics are available in readily available software such as FRAGSTATs
(McGarigal et al. 2002) and can be easily used with GIS or remote sensing type data at
various scales. These metrics alone, however, do not ‘make’ the indicator and there is
little guidance available on how these summary spatial statistics relate to the biological
(or other) effects of the observed fragmentation patterns (Davidson 1998). The metrics
ultimately need to be complemented by other measurements or assessment to provide a
picture as complete as possible. It is possible to determine whether fragmentation does or
does not affect biodiversity in a given forest landscape, but not to quantify the
biodiversity-relevant degradation of that landscape due to fragmentation or to apply the
results in other landscapes. For example, point data can be used to assess species
responses to habitat edges (Ewers and Didham 2008), but to quantify the landscape-scale
net impact on the populations of those species, it is necessary to combine information on
species responses with spatially explicit data on the distribution of habitat edges (Ewers
and Didham 2007, Ewers et al. 2010).
3.2.2 Methods for calculation of fragmentation indices:
1. Requirements: GIS, digitized aerial photographs, or high-resolution satellite imagery,
program such as FRAGSTATS,
2. Develop a comparison between old data and current data or between present data and
expectations from forests undisturbed by man.
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3.3 Technical rationale for an ‘ecosystem diversity’ indicator
An ecosystem can be defined as a dynamic complex of plant, animal and micro-organism
communities and their non-living environment. Classifications of ecosystems can be at
any scale, from global classifications, such as sub-biomes, to local ecological
communities, such as the classification of forest stands based on vegetation associations
and a characteristic set of tree species (e.g., Allen and Hoekstra 1992). In many
countries, classifications of forest vegetation types are also used as classifications of
ecosystems. Ecosystems can be categorised as areas that share similar features among
the driving factors of climatic conditions, geophysical conditions, dominant use by
humans, surface vegetation or water type, and species composition. For the purposes of
forest degradation, this indicator suggests an expectation that, within bounds, a certain
percentage of the landscape should be in each of several known forest types, and that the
broad species composition (multiple species, conifer, deciduous, etc.) of a forest stand
should be predictable given certain pre-existing conditions.
Each ecosystem has a characteristic biodiversity that is recognizable. Since biodiversity
supports almost all goods and services, loss or degradation of the biodiversity in any
ecosystem type will result in a reduction of goods and services from the forest (e.g., Diaz
et al. 2005, Thompson et al. 2009). Therefore, the indicator is useful to suggest broad
changes in the range of forest values that are produced across a landscape.
3.3.1 Landscape scale:
We suggest that, as a minimum, the UNEP/WCMC (2007) forest types be used to a level
of sub-biome. Further information on the 26 forest types is provided on their website that
is referenced. Measurement of the extent of ecosystems or habitats is usually
accomplished by various remote sensing techniques such as aerial photography or
satellite images, with analysis in a GIS. Time series using identical classifications of data
can permit monitoring and analysis of any changes in a sampled area. Many remote
sensing products only enable coarse-scale resolution and are ineffective for determining
degradation. Better resolution, for example of 10 to 50 m is required to map forests.
Landsat, ASTER, SPOT HRV, and IRS, with spatial resolutions from 15 to 60 m, have
17
been used for forest mapping at the national and sub-national level (Strand et al. 2007).
However, these maps provide on rough estimates for forest types and ages and there is
often difficulty even in distinguishing plantations from natural forests (Strand et al.
2007). For example, UNEP/WCMC (2007) mapped four classes of plantations
(temperate/boreal exotic species plantation, temperate/boreal native species plantation,
tropical exotic plantation, and tropical native plantation) at a coarse scale, using AVHRRbased satellite images. However, the data set was too coarse (20 m) to address most
biodiversity monitoring questions. Data at this latter scale can only identify major
changes in forest cover, such as forest loss (UNEP/WCMC 2007).
Souza et al. (2003) developed a method to map degraded forest classes, which they
defined as heavily burned or heavily logged and burned, using a combination of 1 m
resolution IKONOS data and SPOT 4. Even at this fine resolution, however, tree species
could not be identified with accuracy, meaning that only broad forest typing is possible
(deciduous, conifer, open, closed, etc.). Lambin (1999) notes that images must be
evaluated sufficiently frequently to differentiate natural forest change from degradation.
As a result, it is important to understand the rate of natural disturbances and what
processes may be causing degradation locally for any forest type.
In conclusion, to assess change in ecosystem diversity as an assessment of forest
degradation at the landscape scale can probably be accomplished using satellite imagery
for broad forest types (or ecosystems) but not for very fine forest classes or types.
However, the techniques will require certain expertise that may not be available in all
countries and the availability of a forest classification system against which to measure
change. Further, refined assessments require expensive imagery and highly specialized
study. Mid-resolution remote sensing could be used as a first approximation of change in
relative abundance of ecosystems, for example, for the relative abundance of dry to wet
tropical forests, or of conifer to mixed species temperate forests.
The indicator is ‘change in area/percent of forest ecosystems’. The indicator could be
reported in a manner similar to any one of a number of similarity indices that compares
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between locations or times at the same location. Most simply, Sorensen’s index (SI) of
similarity, in this case for departure from expected landscape structure, could be used:
2z
SI =
-----------------------------
x+y
where x is the number of forest types on the landscape of interest, y is the number of
forest types on the reference landscape or at time t+1, z is the number of ecosystems
common to both. The index takes a value between 0 and 1, where 1 = no difference.
For multiple landscapes, Whittaker’s formula as modified by Lennon et al. (2001) or
Diserud and Odegaard (2007) could also be used:
 = 2| b - c|
2a + b + c
Where a is continuity (number of same forest types on both landscapes that are the same),
and b and c are exclusive forest types and c co-occurs on the landscapes or on the same
landscape at different measurement times. The symbol around b – c indicates absolute
value.
3.3.2 Method for landscape level ecosystem diversity monitoring:
1. Use the WCMC/UNEP approximately 27 forest types as it provides a good global
classification, as a first approximation, or use a national or regional forest type
classification system to measure relative abundance of forest types.
2. Map an area using the best available imagery, with ground-truthing if available, for
classes (ecosystems) selected. The area to be mapped could be a large production
landscape, a management unit, or a sufficiently large area (e.g., 100 km2) across which to
sample the forest ecosystems.
3. Develop an a priori expectation of forest types for a given landscapes, based on the
range of natural variability (NRV) for the forest types from historical information or a
nearby primary forest landscape on similar site types.
19
4. Monitor change in ecosystems (area percent), on the area of interest, at a time interval
that is appropriate relative to natural and anthropogenic disturbances.
5. Use the NRV to bound the occurrence (area, percent) for each forest type against
which to determine when degradation might be occurring as a result of human activity.
6. Calculate an index of similarity.
3.3.3 Stand scale:
This scale could also be referred to as ‘habitats’ or forest types as it was by the
Biodiversity Indicators Partnership. The most widely-used techniques for stand level
remote assessments are via small scale aerial photography (e.g., 1:20,000). High
resolution satellite images are available that can be used at the scale of the individual tree
as well, but high-resolution imagery is generally cost-prohibitive. Recently, LiDAR
(light detection and ranging) and other aircraft-mounted sensors have become more
common for forest mapping, however these tools are also generally expensive. Most
work at the stand level to assess degradation will likely occur via ground surveys to
sample the change in forest condition.
Stand-scale ecosystem type degradation is probably best assessed using other indicators
already proposed such as: biomass production, species occurrence, bushmeat production,
etc. However, we propose a separate second indicator for ecosystem condition at the
stand scale under ‘ecosystem state’ below.
3.4 Technical rationale for certain species as indicators of forest degradation
We propose four types of species indicators: tree community structure, focal species
(listed, flagship, indicator, etc.), functional species, and invasive alien species. The latter
will be dealt with under ‘Indicators of Forest Health’.
Species are commonly used as indicators for forest management both in planning and in
monitoring effects and effectiveness (e.g., Oliver and Beattie 1996, Noss 1999,
Azsevedo-Ramos et al. 2010, Lewandowski et al. 2010). In this section various species
20
and species groups are discussed with respect to their usefulness as indicators of forest
degradation, both as a state and as a process.
There are many possible kinds of species indicators and Noss (1999) suggested the
following as useful groups for species as indicators and their selection:
Area limited species: Species that require very large patch sizes or continuous forest to
maintain viable populations. These species typically have large home ranges (e.g.,
woodland caribou in Canada) and/or low populations densities, such as many mammalian
carnivores.
Dispersal limited species: Species that are limited in their ability to move from patch to
patch, or that face a high mortality risk in trying to do so. These species require patches
in close proximity to one another, movement corridors, or crossings across barriers such
as roads. Flightless insects limited to forest interiors, small forest mammals, and large
mammals subject to illegal hunting are among the forest species in this category.
Resource-limited species: Species requiring specific resources that are often or at least
sometimes in limited supply. These resources may include large snags, nectar sources,
fruits, etc. The number of individuals the region can support is determined by the
carrying capacity at the time the critical resource is most limited. Hummingbirds,
frugivorous birds, and cavity-nesting birds and mammals are among species in this
category.
Process-limited species: Species sensitive to the level, rate, spatial characteristics, or
timing of some ecological process, such as flooding, fire, wind transport of sediments,
grazing, competition with exotics, or predation. Plant species that require fire for
germination or to reduce competition are among species in this category.
Functional species and keystone species: Functional species are species that are
disproportionately responsible for key ecosystem functions. Keystone species are
functional species that are also ecologically pivotal species, whose impact on a
21
community or ecosystem is disproportionately large for their abundance. Examples in
forests include tree species that store most carbon, cavity-excavating birds, and
herbivorous insects subject to outbreaks.
Narrow endemic species: Species restricted to a small geographic range (e.g. < 50,000
km2) and often with very few occurrences within that range. Most of these species (that
we know of) are herbaceous plants and some large mammals.
Special cases: Species important in the forest ecoregion that do not fall within one of the
above categories. This group includes disjunct or peripheral populations that are
genetically distinct, and “flagship species” that promote public support for more general
conservation efforts.
The major criterion for the indicators proposed here were: relatively little investment in
their measurement could tell us a lot about other species or processes. It follows
therefore that:

their measurement should be low cost relative to available resources and the
magnitude of the process(es) of interest (efficiency);

they should respond rapidly and measurably to the changes in conditions of
interest (sensitivity);

changes in the indicator should provide a disproportionately large amount of
information about status or change in status of other forest attributes correlated
with the process being monitored (surrogacy); and

the species is especially important for the local area (for culture, food, tourism,
etc.).
Undoubtedly for monitoring the multiple processes that
together comprise degradation, an approach similar to the
‘focal species’ approach would be required with different
species being used to monitor different processes. Despite
Indicator species:
Organisms whose presence is used to mirror
environmental conditions or biological
phenomena too difficult, inconvenient or
expensive to measure directly. They should
be sensitive to changes in real phenomena
of interest and should be used only when
direct measurement is impossible or
infeasible (Rolstad et al. 2002).
the variation in types of indicator species, there are some
common concerns in their application to monitoring forest management and perhaps
22
similarly for degradation (e.g., Lindenmayer et al. 2000, 2002). In most contexts, the
degree to which sensitivity of a single or few species is correlated with other species, or
processes is generally poorly understood. Correlations are usually assumed (albeit based
on sound reasoning) rather than proven (Lindenmayer et al. 2002). The data
requirements of using species as indicators may be limiting, both in demonstrating the
correlation between species abundance and degradation and in sampling populations of
sufficient size to detect changes with statistical confidence. Hence the use of species as
indicators requires an understanding of the limitations of the technique.
For most species indicators, change in abundance is the parameter measured, but
presence/absence, and measures others may be appropriate, such as chemical composition
if pollution is at issue (e.g. lichens for sulphur based pollutants of forests). We note also
that an indicator indicates, it does not necessarily show that, beyond doubt, there has been
a change and it does not explain why the change has occurred. Often, a change in a
species indicator may indicate where further investigation is required before a
management response to the change can be formulated.
3.4.1 Technical rationale for expected community composition by forest tree species for
the ecosystem type as an indicator
Convergence of forest tree species composition between managed and unmanaged stands
is an under-lying tenet of sustainable forest management. Significant departure from the
expected species composition can suggest degradation in goods and services from the
ecosystem, and may indicate a loss of biodiversity for tree species and species-specific
associated organisms such as lichens, fungi, or insects, for example. The tree species
composition changes as a forest becomes degraded through selection logging of
commercially-valuable species, excessive fire, or other unsustainable practices, (e.g.,
Asner et al. 2005, Foley et al. 2007). Stand species composition provides fundamental
information on stand to regional level changes in tree community diversity, and
ultimately for forest ecosystem stability over time. Using species composition as an
indicator requires an understanding of forest ecosystem types (see ecosystems above) and
23
their ‘normal’ species compositions, including some understanding of the variance found
in the same stands across landscapes (beta diversity). Community level analysis also
requires knowledge of successional processes for the forest types.
It may be difficult to establish a baseline for some forest types, for example especially for
humid tropical forest types, where difference in diversity among plots is high owing to
numerous tree species at low densities. Further, if species composition of a primary
forest is used to define a baseline composition, then there are some applied questions that
might arise, e.g., how much distance between the primary forest and the targeted forest is
acceptable for a fair comparison, or which primary forest if there are several, should be
compared with a target forest, how much difference in composition is problematic, and
how to establish a baseline composition if there is no information available from any
primary forest Since species composition in similar vegetation tends to demonstrate a
cluster (Yoshimura 2007), species composition in degraded forest might be defined
locally or at the landscape level. Highly degraded forests will show large differences, to
the extent that the state of the system may have changed. Hence, species composition
surveys can be used to supplement remote sensing work that indicates that the forest is in
a new state (see resilience indicators).
Generally, information on expected species occurrence is available from local to national
forest inventories, especially where management planning is in place. For species
compositional changes, we can determine meaningful changes through many kinds of
analyses that compare community structure, such as discriminant function, clustering, or
various simple indices as suggested above for ecosystem diversity. There are many
software packages available to analyze comparative occurrence or abundance for
indicator species, including: R package ‘indicspecies’, PC-Ord (indicator species
analysis, twinspan, discriminant function, etc.), and others.
3.4.1.1 Methods for determining tree species composition:
There are numerous plot and plotless techniques that can be used for tree species
composition surveys (e.g., prism plots, single large plots, multiple smaller plots, point-
24
quarter, point-distance, etc.). The technique used to census species is probably less
important than an adequate design for the monitoring program and maintaining
consistency with past surveys that may be available. The following outlines the
necessary steps to prepare for tree species census.
1. Use the WCMC/UNEP forest types (ca. 27 forest types) as a first approximation, or
use a national or regional forest type classification system to determine the relative
abundance of forest types.
2. Select forest types of interest for surveying based on relative abundance (most
common and most heavily used) and regional priorities (e.g., rare forest types).
3. Map an area using the best available imagery, with ground-truthing if available, for
classes (ecosystems) selected. The area to be mapped could be a large production
landscape, a management unit, or a sufficiently large area (e.g., 100 km2) across which to
sample the forest ecosystems.
4. Develop an a priori expectation of species abundances in each forest type for a given
landscape, based on the range of natural variability (NRV) for the forest types from
historical information or a nearby primary forest landscape on similar site types.
5. Determine a number of plots to sample based on the expected variance among plots.
6. Conduct the field study using plot-based or plotless methods. However, once a
method is selected it should not be changed. (equipment: data loggers or field notebooks,
prisms, measuring tapes).
7. Determine means and standard deviations for each forest type and develop simple
indices of similarity (e.g., Sorenson’s), and use appropriate multivariate ordinations to
examine for differences between sampled stand types and controls.
3.4.2 Technical rationale for functional species as indicators
Any changes in forest types (age, vegetation, structure) and abiotic environments in and
around forests (average, highest and lowest temperature, precipitation, snow
accumulation) will result in a change in associated species composition. One subset of
species composition that is of particular interest is functional species (e.g., Diaz and
25
Cabido 2001). Not all species contribute equally to ecosystem functioning and in this
regard, some species are more important than others in providing ecosystem goods or
services (e.g., Walker 1992, Diaz et al. 2003) although there if often some redundancy
among species for a given functional role. Loss of functional species in the absence of
redundancy has negative consequences for the ecosystem to the point of ecosystem
collapse (Chapin et al. 1997). because loss of such species often means a reduction in a
given function and hence lower production of goods or ecosystem services. Expanding
the concept to include redundancy, functional groups (groups of species that perform the
same ecosystem function) could be strong indicators of ecosystem change. For example,
loss of all pollinators would have negative consequences for plant reproduction.
Keystone species are a special group of functional species that carry out roles in
ecosystems that affect many other species. Keystone species, where they do occur are
indicators of ecosystem functioning and, if lost, indicate forest degradation. Functional
species are not inevitably the most numerous species in the system (e.g., Hooper and
Vitousek 1997, Diaz et al. 2003).
Certain birds, butterflies, and ground beetles are often used as indicator groups because
of data richness and because many species are clearly functional taxa (Lawton et al
1998). For example, insectivorous birds can regulate herbivore populations, act as seed
dispersal agents, and some act as pollinators. In conifer forests of the western United
States, bird predation on chronic insect herbivores has been shown to increase
productivity in forest stands by as much as 20 percent over control sites with no predation
(Bridgeland et al. 2010). Most insectivorous birds respond negatively to selection
logging and partial harvesting in tropical forests (e.g., Johns 1996, Mason 1996, Aleixo
1999), suggesting that this guild may be good indicators of degradation. Because
ecological information about these groups is also richer than that of many the other
functional species, species composition of such a group demonstrates the condition of
their habitat, which would be related to degree of forest degradation. For example, when
species or age composition becomes unbalanced, such as when only young-forest birds
are recorded at the landscape level, it could suggest degradation at the latter scale
(Yamaura et al 2008). On the other hand, ground beetles are illustrative of stand
26
condition rather than landscape condition. Monitoring methods of both groups of
organisms are well established (e.g., point counts for birds and pit-fall traps for ground
beetles).
If species information, which was originally collected at the stand level, indicates higher
species diversity in natural forests than in plantations at the landscape to regional level,
where there are patches of each forest type, (i.e., gamma diversity is much higher where
natural forests are dominant than for plantation landscapes), then species composition
might be a good indicator of degradation at that scale. So, difference in species
composition (particularly informative as beta or gamma diversity) at the landscape level
might be useful to represent forest degradation.
3.4.2.1 Suggested functional species indicators:
Pollinators
Pollinators are directly or indirectly related to productivity in ecosystems (e.g. Ricketts et
al. 2004, Klein et al. 2007). The importance of each pollinator species or group is
different among regions. For example, insect pollinators are most important globally but
bat pollinators occur mostly in the tropics, while birds (humming birds, honeyeater, and
sunbirds), mammals and sometimes lizards are variously important in particular regions
or ecosystems.
In South-East to East Asia, bees in the tribe Apini of Apidae, including Micrapis,
Magapis and Apis are particularly important indicators of forest degradation because they
are important pollinators of tree flowers and nest in forests or in associated shrubs. Thus,
a dominant species in each region can be a keystone species. Bumblebees are key
pollinators at high altitudes and latitudes, although most of them do not necessarily nest
in forests and pollinate tree flowers. Certain bats are specialists as pollinators. Many
hover flies are important pollinators, they inhabit forests, and are often strongly
associated with woody debris, but their ecology is generally not well known.
27
Population size of key pollinator species can be an indicator of ecosystem function and
degradation and possibly also for landscapes. The ratio of plant species which require
biotic pollination, to those that do not, might indicate condition of pollinators in a forest
ecosystem. As most key pollinators (honeybees and bats) require relatively large snags
in which to nest, these snags can be an indicator of the presence of bees.
In agro-forestry fields, natural vegetation (the area in total/ distance from an agricultural
field) can be an indicator (Gathmann and Tschatntke 2002, Taki et al. 2010). Also in
these landscapes, seed/fruit set can be an indicator but the amount of seeds/fruits
produced, assuming that the natural variation is understood.
Methods of surveying bee fauna (see Westphal et al. 2008)
1. Trap bees with pan-traps (yellow, white and blue are standard colors to use), which are
one of the best traps to cover landscape level sampling. Trap nests with reed/bamboo
internodes could be used as a complementary sampling method (Westphal et al 2008).
However, it should be remembered that still some particular species could not be trapped.
2. Identify trapped species as at the morpho-species level or at least at the family level.
Individual numbers of each should be counted.
3. Determine baseline for bee species using one of the following forms of information:
total species numbers, indicator bee species, compositions of specialist and/or generalist,
or bee diversity index: e.g. (1) by calculating alpha (α), beta (β), and gamma (γ) diversity
for stand level to landscape level.
Communities can be characterised and compared using diversity indices such as:
(2) Simpson’s index
or (3) Shannon’s index
Where S is the number of species in the community and pi is the proportion of S made up
by the ith species.
28
Seed dispersal agents
Seed dispersal is directly related to natural forest regeneration, hence ecosystem
sustainability. Many species of tree seeds are dispersed by biotic agents (animals) while
others are dispersed through abiotic agents (wind, water, etc). Most zoochoric plants
produce fruits whose seeds are intended to be carried by vertebrates. Therefore, those
plants are important food resources for animals. Zoochoric plant richness and their annual
rate of reproduction (the amount of fruit set) is highly related to reproductive success in
many vertebrates.
Animals play important role for seed dispersal of trees. Generally, in any climate zones,
birds are one of the most important groups of seed dispersal organisms in forest
ecosystems. Primates are important seed dispersers in tropical forests (e.g., Chapman
1989). Rodents can carry nuts but are also nut predators (Howe and Smallwood 1982).
Many key seed dispersers migrate or move relatively long distances (birds, bats and
primates), and so the loss of their habitats at the landscape level might prevent natural
regeneration of some trees (Gorchov et al 1993). Further, some small plants, such as
Viola also rely on insects as seed dispersers and as shown in the Viola-ant system,
germination highly depends on these plant-disperser systems.
Habitat conditions (e.g., the area of natural forests, fragmentation, connectivity of each
forest patch, corridor, snags and relatively large trees as nests) could be indicators of the
animals. Also, the numbers of zoochoric plants and their rate of regeneration in an
ecosystem might show the condition of seed dispersers in the area and sustainability of
the ecosystem.
Decomposers
Decomposers are important agents that help to maintain water and soil quality, and
promote nutrient cycling (e.g., Harris 2009). In forests, decomposition is an essential
function, providing fundamental services such as water purification, soil amelioration,
29
and nutrient cycling. There are numerous functional types in soil communities including:
N cycling, P cycling, C cycling, decomposition of organic matter, etc. (Ritz et al. 2009).
However, most of these functions are considered to not directly depend only on the
animal and fungus diversity (Dobson et al. 2006) but probably are also dependent on the
amount and the condition of the forest cover. Species richness is usually not very
different among forest types in soil organisms but differs with the amount of resources
available for the organisms, and a vast array of fungi, arthropods, and micro-organisms is
involved (Harris 2009). The soil microbial community is dependent on the level of site
disturbance and so the microbial community can indicate the impact of restoration and
management practices, as soil conditions improve (Harris 2003).
At the micro-scale, different dead (or almost dead) trees were preferred by different wood
boring insects: cerambycid and bark beetles, and by different wood decaying fungi. Soil
organisms are generally rich in litter that is composed of richer (i.e., high in nutrient
loads) species (Hättenschwiler et al 2005). Therefore, at least for soil formation at the
early stage of organic decomposition and nutrient cycling, soil organism diversity is
crucial to maximize the service (Harris 2009).
Microorganisms are probably the most important forest decomposers, but little qualitative
and quantitative information is available on these species or how they function (e.g.,
Meyer 1994, Harris 2003). Microorganisms decompose organic materials from macro- to
micro-scales throughout the decomposition process. The process of decomposition in
forests is controlled by a cascading effect, with multiple different organisms at differing
stages that are dependent on products from the preceding stage. However, which stage of
decomposition, from a dead organism to nutrient, is associated with exactly which
organisms is mostly unknown.
Methods for monitoring soil micro-organisms
Microorganisms are the most important groups of decomposers although there is no
“best” technique to monitor them, and all techniques require specific expertise and
equipment (Harris 2003, Ritz et al. 2009). Species composition of soil animals is
sometimes used as an indicator (Yeats 2003). However, there are difficulties with
30
identification of soil animals and therefore they are usually only used for scientific
investigations, not for operational monitoring. Using DNA markers of a region of
particular function is possible, but only for a small scale. Nevertheless, these techniques
result in clear differentiation of levels of soil degradation, which translate directly into
functional relationships with above-ground primary production. The suite of methods
available for developing a monitoring program for decomposers is well-described in Ritz
et al. (2009).
Biological control species
Insect herbivores are important pests in forests. Common biological control agents
include pathogenic microorganisms, insect predators and parasitoids (Debach and Rosen
1991), and insectivorous birds and bats (Kalka et al. 2008). Biological control of tree
diseases is very difficult to assess or not well examined scientifically.
Generally, there are not many predators in natural forests but when pest species increase
in number, their natural enemies respond by increasing as well, but with a time lag. Thus,
in a natural forest, an outbreak of herbivores often does not last long. Sometimes
however, insect populations can ‘escape’ control by predators and damage extremely
large areas of forest, depending on favourable conditions.
Some predacious insects, such as ground beetles and parasitoid wasps, do not migrate
long distances and so forest fragmentation may prevent their migration (Kagawa and
Maeto 2009). Birds and bats show particular habitat preferences as discussed above.
Although some ground beetles are sensitive to habitat conditions including vegetation
(e.g., Nummelin and Fursch 1992), ground ants may not be highly influenced by changes
in vegetation cover (e.g., Oliver et al. 2000, Vasconcelos et al. 2000)
Insect predators, parasitoids, invertebrate pathogens, and insectivorous birds and bats are
biological control agents. The importance of each group might differ locally, as does the
species composition. Birds and bats are generally important because of large positive
effects.
31
The period of pest outbreak is one indicator of the strength of biological control. For
instance, an outbreak of invasive alien species tends to continue until their prey or host
population has collapsed because they have no natural enemy in an invaded region. For
insectivorous birds and bats, the total area of available habitat and habitat connectivity
could be indicators.
Methods of surveying insect fauna
As ground dwellers include important predaceous insect groups, pit-fall traps are useful
methods to sample them. For ants, bait traps are sometimes used but if there are well
trained technicians, line transects are an alternative. Bamboo/reed trap nests work for
some wasps and these nests also provide information on their prey menu. A malaise trap
is generally used for collecting flying insects. Species are tallied as numbers/day.
Identification is relatively easy for ground dwelling predators.
Species important as forest carbon sinks
Growth rates of trees and perhaps increases in biomass are indictors of the above-ground
carbon sink. Not all trees store carbon equally, and some species are short-lived while
others are long-lived. Although analysis of the relationship between carbon sequestration
and tree species and between carbon sequestration and forest management/ disturbance is
still incomplete, some tree species require more carbon than the others during growth and
so these species store more carbon than other species (Russell et al. 2010).
Provisioning species: timber, food, chemicals, resources for NWFPs
Indicator species should be determined locally and nationally by considering the relative
importance of each species for sequestration and storage. Certain plants may be culturally
important species for non-wood forest products. Forest degradation can be measured by
yield as the difference between expected and actual yields.
Table 4. Possible species indicators of ecosystem function, related to forest degradation.
Indicator
Pollination
Measurement method
Relevant Case
Studies
Scale of
measurement
32
Population size of key
pollinator species+
Habitat quality for key
pollinators+
Natural vegetation
(maybe same as habitat
quality in some senses)+
Seed dispersal
Habitat quality for key
seed dispersers+
Zoochoric plants (for
seed dispersers)
Seed dispersal animals+
Decomposition
Soil animals for
decomposition-
Soil physical and
chemical properties+
Biological control
Natural enemies for
biological control
Insectivorous birds and
bats+
Habitat quality for
biological control
agents+
Period of outbreak of
pests (for evaluation,
need more scientific
evidences)
Carbon sequestration
Relevant major tree
species for carbon sink
Tree growth+
Soil nutrition level for
evaluation, need more
scientific evidences)
Habitat provisioning
National inventory
Landscape
Number of snags, old (and
maybe large) trees
The area of natural
vegetation, distance between
natural vegetation
Stand, landscape
Gathmann and
Tschatntke
(2002); Taki et al.
(2010)
The area of natural forests,
fragmentation, connectivity
of each forest/corridor, snags
and relatively large trees as
nests
Numbers, the amount of
young zoochoric plants
Species richness, population
size, National survey
Species composition
Landscape
Stand, landscape
Stand, Landscape
Stand, landscape
Mostly used in
scientific study,
requires expertise
(Ritz et al. 2008)
Stand
National and local survey
Stand
National and local survey,
species richness
Species richness, species
composition, population size
Natural vegetation, forest
connectivity (fragmentation)
Stand, landscape
National survey
Stand, landscape
National survey, inventory
National survey, stand level
survey
National survey
Stand, landscape
Stand, landscape
Russell et al.
(2010)
Stand
Stand
Oren et al. (2001)
Stand
33
Forest structure
Forest connectivity
(fragmentation) for
habitat
Tree growth, tree mortality,
layers in forest
Remote sensing, corridor
Ellison et al.
(2005)
Stand and landscape
Landscape
- = difficult indicator to use; + = easy/good indicator
3.5 Technical rationale for an ‘Invasive alien species’ (IAS) as indicators of
degradation
A forest invasive alien species is a species not native to a given forest type, which has
successfully invaded the system and is causing harm (e.g., Pimental et al. 2005). Harm
may be the elimination of local biodiversity and/or a reduction in goods and services. IAS
often result in a change in forest state, and a consequent reduction in services. This
indicator was not suggested during the first Technical Meeting, but because forests can be
degraded as a result of IAS and many forests are in fact degraded as a result of IAS (e.g.,
Chornesky et al. 2005), most indicator processes use this indicator. Further, because the
indicator can be measured, we have included it here. It is incorporated as a biodiversity
indicator because the usual effect of IAS is to reduce native species either through
competition, herbivory, or predation (e.g., Lucier et al. 2009). Invasive species that cause
degradation of forests can be almost any life form from insects to trees themselves, but
the degraded end result is obvious to an observer who understands the original forest
type.
3.5.1 Landscape scale:
In some instances, mapping the spread and impacts of specific invasive tree species can
be accomplished using remote sensing (Van der Meer et al. 2002). For example, certain
invasive species occur in or dominate forest canopies and so have been mapped remotely,
including tamarisk (Tamarix chinensis) (Everitt and Deloach 1990), leucaena (Leucaena
leucocephala) (Tsai et al. 2005), maritime pine (Pinus pinaster) (Ferreira et al. 2005),
Chinese tallow (Sapium sebiferum) (Ramsey et al. 2002), and Australian wattles (Acacia
spp.) (Theron et al. 2005). Another valuable use of remote sensing in monitoring
invasive species is the effect that some invasives have on forest condition. In Hawaii
montane rain forest, Asner and Vitousek (2005) used aircraft with infrared imaging
34
spectrometer to show that leaf nitrogen concentrations in Metrosideros polymorpha
forests invaded by Myrica faya were reduced. Sometimes differential morphology or
colouration of an invasive tree species can be detected by remote sensing. For example,
Pauchard and Maheu-Giroux (2007) used the yellow colour of Acacia dealbata to
examine the extent of its invasion into forests in Chile using 1:20,000 digital colour aerial
photographs on a 30 x 30 m grid. As was indicated for ecosystems, limitations for these
kinds of data are the available technology, expert capacity to analyse the data, and the
cost of acquiring the imagery. Nevertheless, remote sensing is the only way to monitor
invasive species over large areas to assess damage and extent.
Damage to forests is not only caused by invasive tree species. Invasive insect herbivores
or pests, such as emerald ash borer (Agrilus planipennis), or pathogens, such as Dutch
elm disease (Ophiostoma ulmi and O. novo-ulmi) in North America have caused
extensive degradation to millions of hectares of forests. In many cases, once the cause is
known, damage to forests can be mapped by assessing numbers of dead trees by using
aerial photographs to measure extent. Similarly, damage from defoliating insects can be
also mapped remotely if severe enough to be detected by the sensors.
Other invasive insects, such as ants and earthworms, can cause cascading effects over
large regions as a result of competition or replacement of endemic species in systems
(Kenis et al. 2009 Straube et al. 2009). However, changes caused by these kinds of
species are often subtle and difficult to monitor.
Even if sufficient funding is available for a large-scale remote sensing study of an IAS as
a degradation agent, at least two points in time are still required. That is, images must be
acquired at intervals of several years to assess trends in change and to provide a measure
of change over time. Nevertheless, current extent can be determined from a single set of
images.
The indicator is: area of forest damaged by invasive species. Precise methods will vary
depending on technology available, expertise available, particular invasive species, and
35
type of damage by the invasive species. Modelling may be an appropriate approach to
predict area affected, depending on the particular IAS.
3.5.1.1 Methods for landscape and stand scale monitoring of IAS:
1. Develop a list of invasive species and map their distributions. Damage (degradation)
will be the sum of stand level assessments.
2. Assess the area affected by each invasive species based on known effects from
summary maps based on several techniques: i) remote sensing if possible, ii) ground
surveys and summarized ad hoc observations where needed, iii) expert opinion and
available research where available.
3. Monitor change in ecosystems (area, percent), on the area of interest, at a time interval
that is appropriate.
Forest resilience and tipping points (thresholds)
Desired state
Degraded state
36
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