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Practical Application of Biodiversity Surrogates and Percentage Targets for Conservation in Papua New Guinea

Measuring biodiversity in PNG

Mapping units
Papua New Guinea occupies more or less the eastern half of the large tropical island of New Guinea and its associated off-shore islands. Most of the land surface, with the exception of some coastal areas and valleys in the highlands, is covered by tropical forest. In 1975, forest covered 330,650 km2, approximately 70% of the total land area of 464,100 km2. The other 30% also contains substantial areas of primary and secondary forest, but in a mosaic with village agriculture and grasslands. The Papua New Guinea Resource Information System, known as PNGRIS, contains maps and information on current land use, population density, geology, slope, landform, inundation, vegetation, soils, and limitations on land use for the whole country, (Bellamy and McAlpine 1995; Keig and Quigley 1995). The land units for which this information is recorded, and within which it is stored, are called Resource Map Units (RMUs; Figure 3). These units are widely used and understood by many different Government agencies in PNG, so they were used as the planning allocation units for this study. The RMUs were mapped from aerial photographs during the extensive land resource surveys carried out by the Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO) in the 1970s. They number 4,470 and vary in size from 0.045km2 to 8508km2, with a mean of 96km2 and a standard deviation of 292km2. All assembled environmental and biological data described below were combined into the RMUs and data files were generated listing the attributes (environmental and biological data) present in each RMU. These files were used to implement the conservation planning methods described by Faith et al. (2001a,b).

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Biodiversity surrogates
Since our descriptive knowledge of what species there are and where they are is inadequate, dramatically so in Papua New Guinea, biodiversity surrogates are critical to planning. Potential surrogates include sub-sets of taxa such as birds, mammals, butterflies, etc., or assemblages of taxa such as vegetation types or communities, or environmental variables or classes. A sound practical strategy is to adopt as many of these surrogates as possible to maximise the likelihood of representing more of biodiversity in selected priority areas. The biodiversity surrogates information available for the PNG study included:

• Environmental domains (described in Nix et al. 2000)
• Vegetation types (described below)
• Species distribution models classified as species bioclimatic profile clusters (described in Nix et al. 2000)

Environmental domains
Nix et al. (2000) describe the method of deriving environmental domains in PNG from the 50 attributes representing bioclimates, terrain, landform and lithological types. Preliminary classifications without the lithology were used to estimate the approximate number of classes (the level in the classification pattern) in combination with the vegetation continuum, that could be sampled in 10% of PNG.

Vegetation types
There are 642 different vegetation types in PNG's forest inventory mapping data base (FIM; McAlpine and Quigley 1998). A few are non-vegetation classes such as open water and urban areas. These were deleted. Many other types distinguish degrees of disturbance. For example, type B is mixed forest and type B8 is mixed forest 80% undisturbed. Any type that was 70% or more undisturbed was merged with its primary, wholly undisturbed, type. Thus, in the example above, B and B8 were merged to form the new type B. All types that were 60% or less undisturbed (that is, 40% or more of them was disturbed) were not regarded as suitable biodiversity surrogates and were deleted. Types that were combinations of two or more original types, but were only distinguished on the basis that in the first type one was dominant and in the second type it was the other one that was dominant, were merged. For example, Hm/Wsw, medium crowned forest dominant over swamp woodland, was merged with Wsw/Hm8, swamp woodland dominant over medium crowned forest 80% undisturbed. This new merged type was also merged, on the basis of percentage disturbed as above, with Hm9/Wsw8, medium crowned forest 90% undisturbed dominant over swamp woodland 80% undisturbed. This procedure resulted in 208 new vegetation types (a complete listing is available from the authors).

The vegetation types are based mainly on structural features and it seems certain, for instance, that a swamp woodland in the north-west of the country will contain different species than a swamp woodland in the south-east of the country. Thus, the environmental domain classification can be continued up to a nominated group level to produce some number, N, of broad-scale physio-climatic zones. The intersection of 208 vegetation types with these N zones resulted in new combinations of vegetation types with physio-climatic zones. The group level chosen for this purpose was required to result in a number of vegetation type attributes which, when combined with a number of domains, could be represented in any 10% of the country.

Two kinds of information, described below, were not used as part of the continuum model of biodiversity, but did provide additional attributes for the representation process.

Species bioclimatic profile clusters
Species groups were produced in Nix et al. (2000). To generate a predicted bioclimatic distribution for each species group, a BIOCLIM profile was produced from the combined specimen records for all members of the group. The predicted distribution of the group was then determined by matching the values of the bioclimatic parameters estimated for each grid point on the 0.01 degree DEM to the bioclimatic profile values with the BIOMAP program. A species group was predicted to occur at a grid point if the values of all 16 bioclimatic parameters (Nix et al. 2000) were within the range of the corresponding BIOCLIM profile. Unlike the environmental domains, more than 1 species group could be predicted to occur at a grid point.

The grids of predicted distributions were converted to a polygon coverage and overlaid on the RMUs. The summary Table from this combined coverage gave the area of each of the 10 species groups predicted to occur within each RMU.

Rare and threatened species
Rare and threatened species are also included in our planning analyses, but are not surrogates for biodiversity. Their inclusion in the TARGET planning analyses not only ensures that the biodiversity surrogates "net" does not miss them, but also provides a possible surrogate for other rare or threatened species. The same area containing one or more of these species may contain other rare taxa.

A list of the rarest and most threatened bird and mammal species was taken from Beehler (1993). These are shown in Table 1 along with the number of RMUs in which they are found. Data for Queen Alexandra's Birdwing Butterfly (Ornithoptera alexandrae) were supplied by the PNG Department of Environment and Conservation. Eleven of the species found in 119 or fewer RMUs were included as attributes to be represented in the set of priority areas. Dorcopsis atrata is found in only on Goodenough Island in one, or perhaps two RMUs. Goodenough Island always makes it into the BPA set because of its distinctive environment, so there was no need to include this species as an attribute. Species occurring in more than 119 RMUs were expected to be represented in any set of BPAs because they are widespread and this is indeed the case. They are all represented in the set of areas chosen following implementation of the selection methods described in Faith et al. (2001a).

Other information
The goal of representing this variation was complemented by the incorporation of additional biodiversity information. Areas identified as priority 1 biotic hotspots in the Conservation Needs Assessment (CNA; Alcorn 1993; Beehler 1993) were used as preferences in the selection of priority areas. All else being equal, an area falling within a CNA priority 1 area was selected over an area not falling within a CNA priority 1 area. This was done in a deliberate attempt to take advantage of the knowledge acquired by experts in their fields and summarised in the CNA study (Faith et al. 2001a).

The detail within the biodiversity surrogate types listed above reflects our current knowledge of the environment and the biota of PNG. Current knowledge in PNG is biased towards descriptions of the environment and forest types, rather than the locations of species. Field records of the locations of species are sparse and poorly documented so the amount of species-based information that could be used in selecting biodiversity priority areas was limited. In many other countries and regions species data may be more readily available and might therefore form a more significant component of the biodiversity surrogates used for conservation planning. On the other hand, the PNGRIS (Papua New Guinea Resource Information System) and FIM (Forest Inventory Mapping) data bases (Nix et al. 2000) available in PNG provide a great deal of the biodiversity surrogate information, as well as costs and constraints information used in the biodiversity priority area selection process (Faith et al. 2001a), and data sources such as these may not be available in other countries and regions.

Results
A 10%-based target formed our initial target level for the PNG study, but we also explored a larger 15%-based target. For each target level, we determined a baseline number of domains and vegetation type clusters using the procedure described above.

The intersection of 208 vegetation types with N=10 zones resulted in 564 combinations of vegetation types with physio-climatic zones. The 10 group level was chosen because it resulted in a number of vegetation type attributes (564) which, when combined with 608 domains, could be represented in an unconstrained 10% of the country. The relative of weighting of the continuum for domains and for vegetation was somewhat arbitrary; our use of approximately the same number of clusters for each reflects an approximate equal weighting.

The 10 species clusters (discussed above) were not included in the analysis to determine the biodiversity representation target; however, these 10 classes were so extensive in distribution that they would have no effect on the required area needed.

The resulting set of biodiversity surrogates used to select biodiversity priority areas in PNG is shown in Table 2. There is a total of 1193 attributes consisting of 608 environmental domains, 564 vegetation types, 10 species clusters and 11 rare and threatened species.

Figure 4 shows the set of areas selected to maximise biodiversity representation and to total approximately 10% of the country. For the 10%-based target, 258 RMUs totaled 47958 km2 (for the 15%-based target, 365 RMUs totaled 69283 km2, but represented more attributes). These baseline areas in principle only function to determine how much biodiversity might have been protected in the absence of constraints. But there is also information in this map that has a direct bearing on the selection of areas under costs and constraints (Faith et al. 2001a,b). Some of the areas can be identified as "must-haves". They represent one form of "complementarity hotspot" (Faith and Walker 1996d) and must be selected if the target level of biodiversity representation is to be achieved (see also the "irreplaceability" of Pressey et al. 1993). We identified such areas by selecting all PNG areas using the TARGET software, and then determining, using TARGET diagnostic outputs, which areas still had a non-zero complementarity. Such areas uniquely contributed components to the target level of representation. This analysis was repeated for the 15% target level as well. Thus, the process of determining a biodiversity target for later planning already determines some "must-have" areas (Figure 5).

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