1 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- 2 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. 3 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 4 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 5 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 6 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 7 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 8 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. 9 (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. 10 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) 11 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 12 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 13 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 14 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 15 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. 16 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 18 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. 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