2015 Forest Resources Assessment

Methodology and results in the project sites of Aklan, Antique, Capiz and Iloilo adjacent to the Panay Mountain Range Imprint As a federally owned enterprise, GIZ supports the German Government in achieving its objectives in the field of international cooperation for sustainable development.

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Programme: Forest and Climate Protection in Panay – Phase II

Authors: Ralph Lennertz and Jürgen Schade, DFS Deutsche Forstservice GmbH

Photo credits/sources: Ralph Lennertz

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Quezon City, Philippines | 2017

Table of contents List of tables ...... 1 List of figures ...... 2 Acronyms ...... 3 Executive summary ...... 5 1. Introduction and background ...... 6 1.1 Forest and Climate Protection Project - Phase II ...... 6 1.2 Methodological framework ...... 6 1.3 Definition of terms and concepts ...... 7 1.3.1 Forest ...... 7 1.3.2 Species abundance and diversity ...... 7 1.3.3 Carbon pools ...... 8 1.3.4 IPCC key categories ...... 9 1.3.5 IPCC tiers ...... 9 1.3.6 Forest sampling-related terms ...... 10

2. Objectives ...... 11 2.1 General objectives ...... 11 2.2 Areal sampling frame ...... 11 2.3 Scope and content ...... 13 2.4 Variables of interest ...... 13 2.5 Targeted precision ...... 14

3. Inventory and estimation design ...... 15 3.1 Sources of information ...... 15 3.1.1 Geographical–political subdivisions ...... 15 3.1.2 Forest strata and areas ...... 15 3.1.3 Soil classes ...... 15 3.1.4 Wood specific gravity ...... 17 3.2 Inventory method ...... 18 3.3 Sampling unit design ...... 18 3.3.1 Observations and measurements at the sample points ...... 19 3.3.2 Observations and measurements in the nested plots ...... 20 3.4 Sample size and margin of error ...... 21 3.5 Sampling type and distribution ...... 22 3.6 Estimation design ...... 24 3.6.1 Diversity indices ...... 24 3.6.2 Merchantable volume of trees ...... 24 3.6.3 Above-ground biomass of trees ...... 24 3.6.4 Above-ground biomass of bamboos ...... 25 3.6.5 Above-ground biomass of palms ...... 25 3.6.6 Above-ground biomass of rattan and tree ferns ...... 26 3.6.7 Below-ground biomass of trees, bamboos and palms ...... 26 3.6.8 Above-ground biomass of standing dead wood ...... 26 3.6.9 Biomass of lying (downed) dead wood ...... 26 3.6.10 Biomass of litter ...... 27 3.6.11 Conversion of biomass to carbon ...... 27 3.6.12 Statistical inference ...... 27

4. Field data collection ...... 29 4.1 Human and material resources ...... 29 4.1.1 Human resources ...... 29 4.1.2 Inventory equipment...... 29 4.2 Organization of the field work ...... 30 4.2.1 Field manual ...... 30 4.2.2 Training ...... 30 4.2.3 Inventory camps ...... 30 4.3 Getting to and marking of sampling units ...... 31 4.3.1 Getting to the sample points ...... 31 4.3.2 Location of sample points and plot centers...... 32 4.3.3 Permanent marking of sample points and plot centers ...... 33 4.3.4 Inaccessible sample points and plot centers ...... 33 4.4 Assessment or measurement of variables ...... 34 4.4.1 Administrative location ...... 34 4.4.2 Actual coordinates ...... 34 4.4.3 Elevation...... 35 4.4.4 Slope ...... 35 4.4.5 Slope orientation ...... 35 4.4.6 Terrain ...... 35 4.4.7 Land classification ...... 35 4.4.8 Land cover ...... 36 4.4.9 Forest type ...... 36 4.4.10 Tree crown cover ...... 36 4.4.11 Plant Diversity ...... 37 4.4.12 Ground coverage classes by vegetation layers ...... 37 4.4.13 Ground coverage and average depth of litter ...... 37 4.4.14 Mid-diameter and length of lying dead wood sections ...... 37 4.4.15 Observations and measurements on trees and standing dead wood ...... 38 4.5 Quality assurance and quality control ...... 42 4.6 Time and cost of the field data collection ...... 43

5. Data processing ...... 44 5.1 Software, database and database application ...... 44 5.2 Data entry, quality assurance and quality control ...... 44

6. Results ...... 46 6.1 Species diversity ...... 46 6.1.1 Species diversity of closed forests ...... 48 6.1.2 Species diversity of open forests ...... 50 6.2 Stand composition ...... 52 6.2.1 Stand composition of closed forests ...... 52 6.2.2 Stand composition of open forests ...... 55 6.3 Stand structure ...... 58 6.3.1 Stand structure of closed forests ...... 58 6.3.2 Stand structure of open forests ...... 64 6.4 Timber stocks ...... 70 6.4.1 Timber stocks of closed forests ...... 70 6.4.2 Timber stocks of open forests ...... 72 6.5 Carbon stocks ...... 74 6.5.1 Carbon stocks of closed forests ...... 74 6.5.2 Carbon stocks of open forests ...... 74 6.5.3 Carbon stocks of mangroves ...... 76

7. Uncertainty of the estimates ...... 78 7.1 Statistical sampling error ...... 78 7.2 Representativeness of the sampling network ...... 79 7.3 Measurement errors ...... 79 7.4 Data entry errors ...... 79 7.5 Estimation design uncertainties ...... 79 7.6 Overall error budget ...... 79

References ...... 81

Appendix 1: Field data forms ...... 83

Annex 1: Coordinates of inventoried sampling units ...... 86

Annex 2: List of recorded species by common name ...... 88

Annex 3: List of recorded species by botanical family ...... 94

Annex 4: Detailed results - closed forests ...... 103

Annex 5: Detailed results - open forests ...... 104

Annex 6: Statistical parameters - closed forests ...... 105

Annex 7: Statistical parameters - open forests ...... 106

List of tables Table 1. 2010 Land and forest cover areas of the selected project sites in the Panay Mountain Range...... 13 Table 2. IPCC Tier 1 soil organic matter stocks...... 16 Table 3. Overview of sub-plot sizes and assessments or measurements made on trees and dead wood...... 21 Table 4. Preliminary estimate of provisional statistical precision of the biomass estimates ...... 21 Table 5. Sample size, coefficients of variation and margins of error of the biomass estimates ...... 22 Table 6. Deviation of initial measurements from control measurements ...... 42 Table 7. Time and cost the field data collection ...... 43 Table 8. Deviation of encoded data from field forms ...... 45 Table 9. Threatened species according to IUCN ...... 47 Table 10. Threatened species according to DENR AO No. 2007-01 ...... 47 Table 11. Species diversity indices ...... 48 Table 12. Relative frequency, density and dominance, importance and rank of the 20 most "important" species in closed forests ...... 49 Table 13. Relative frequency, density and dominance, importance and rank of the 20 most "important" species in open forests ...... 51 Table 14. Stand composition of closed forests ...... 53 Table 15. Stand composition of open forests...... 56 Table 16. Stand structure in terms of N/ha of closed forests ...... 58 Table 17. Stand structure in terms of G/ha of closed forests ...... 60 Table 18. Stand structure in terms of AGB/ha of closed forests ...... 62 Table 19. Stand structure in terms of N/ha of open forests ...... 64 Table 20. Stand structure in terms of G/ha of open forests ...... 66 Table 21. Stand structure in terms of AGB/ha of open forests ...... 68 Table 22. Merchantable volume in closed forests...... 70 Table 23. Merchantable volume in open forests ...... 72 Table 24. Carbon stocks of closed forests ...... 74 Table 25. Carbon stocks of open forests ...... 75 Table 26. Carbon stocks of mangroves ...... 77 Table 27. Statistical sampling errors of the main variables of interest in closed and open forests ...... 78 Table 28. Overall error budget for V/ha ...... 80 Table 29. Overall error budget for AGB/ha ...... 80

List of figures Figure 1. Forest carbon pools ...... 9 Figure 2. 2010 NAMRIA land cover of the selected project sites in the Panay Mountain Range ...... 12 Figure 3. 2013 BSWM FAO soil map of the selected project sites in the Panay Mountain Range ...... 17 Figure 4. Sampling unit design ...... 19 Figure 5. Distribution of the sampling units effectively measured ...... 23 Figure 6. Inventory camp ...... 31 Figure 7. Open cycle map with "outdoors" base layer ...... 32 Figure 8. Google Maps versus Apple Map ...... 32 Figure 9. Location and marking of sample points and plot centers ...... 33 Figure 10. Re-location of inaccessible plots ...... 34 Figure 11. Measurements on lying dead wood sections ...... 38 Figure 12. DBH and DAB measurements ...... 40 Figure 13. Diameter estimates for inaccessible measurement points ...... 41 Figure 14. N/ha, G/ha, V/ha and AGB/ha by number of species in closed forests ...... 50 Figure 15. N/ha, G/ha, V/ha and AGB/ha by number of species in open forests ...... 52 Figure 16. Stand composition of closed forests ...... 54 Figure 17. Stand composition of open forests ...... 57 Figure 18. Stand structure in terms of N/ha of closed forests ...... 59 Figure 19. Stand structure in terms of G/ha of closed forests ...... 61 Figure 20. AGB/ha of closed forests by DBH / DAB threshold ...... 62 Figure 21. Stand structure in terms of AGB/ha of closed forests...... 63 Figure 22. Stand structure in terms of N/ha of open forests ...... 65 Figure 23. Stand structure in terms of G/ha of open forests ...... 67 Figure 24. AGB/ha of open forests by DBH / DAB threshold...... 68 Figure 25. Stand structure in terms of AGB/ha of open forests ...... 69 Figure 26. Merchantable volume in closed forests ...... 71 Figure 27. Merchantable volume in open forests ...... 73 Figure 28. Carbon stocks of closed forests ...... 75 Figure 29. Carbon stocks of open forests ...... 76 Figure 30. Carbon stocks of mangroves ...... 77

Acronyms AD Activity data AFOLU Agriculture, Forest and Other Land Use AGB Above-ground biomass ALOS Advanced Land Observing Satellite AVNIR Advanced Visible and Near Infrared Radiometer BCEF Biomass Conversion and Expansion Factor BGB Below-ground biomass BMUB Bundesministerium für Umwelt, Naturschutz, Bau und Reaktorsicherheit (Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety) BSWM Bureau of Soils and Water Management C Carbon DAB Diameter above buttress DBH Diameter at breast height DENR Department of Environment and Natural Resources DFS Deutsche Forstservice GmbH Dg Quadratic mean diameter DOM Dead organic matter Dref Reference diameter EF Emission factor FAO Food and Agriculture Organization FLUP Forest Land Use Planning FMB Forest Management Bureau FRA Forest resources assessment GADM Global Administrative Areas GHG Greenhouse gas GIS Geographic Information System GIZ Deutsche Gesellschaft für Internationale Zusammenarbeit GmbH GPS Global positioning system HWSD Harmonized World Soil Database IPCC Intergovernmental Panel on Climate Change IUCN International Union for Conservation of Nature and Natural Resources JDK Java Development Kit JRE Java Runtime Environment LB Living biomass LDW Lying dead wood LGU Local government unit LI Litter NAMRIA National Mapping and Resource Information Authority NSCB National Statistical Coordination Board PENRO Provincial Environment and Natural Resources Office(r) POI Point of interest PSGC Philippine Standard Geographic Code QA Quality assurance QC Quality control RDBMS Relational Database Management System REDD+ Reducing Emissions from Deforestation and forest Degradation, and conservation, sustainable management of forests and enhancement of carbon stocks SDW Standing dead wood SFM Sustainable Forest Management SLC Scan line corrector

SOM Soil organic matter SOP Standard operating procedure UNCBD United Nations Convention on Biological Diversity UNFCCC United Nations Framework Convention on Climate Change UTM Universal Transverse Mercator WGS World Geodetic System WRB World Reference Base for soil resources

Executive summary The forest resources assessment (FRA) report describes the methodology and the results of the FRA conducted from 12 February 2015 until 08 December 2015 in the sites of the Forest and Climate Protection Project Panay - Phase II in Aklan, Antique, Capiz and Iloilo adjacent to the Panay Mountain Range (the inventory covers 38 municipalities).

The methodology used is a refinement of the forest carbon baseline study carried out from 2011 to 2012 in Leyte in the framework of the Climate Relevant Modernization of Forest Policy and Piloting of Reducing Emissions from Deforestation and Forest Degradation (REDD) Project. It takes into account evolving international standards and good practices with regards to forest carbon stock assessment in compliance with the latest (2006) Intergovernmental Panel on Climate Change (IPCC) guidelines for national greenhouse gas (GHG) inventories.

The FRA aimed to provide information about the forest condition and carbon stocks for key forest strata of the selected project sites. This information comprises: • Species abundance and diversity; • Stand density, basal area and merchantable volume; • Stand composition and structure; • Forest carbon stock estimates for key carbon pools, including living biomass (above- and below-ground), dead organic matter and soil organic matter. The report successively provides details about: • The background, including (i) a brief introduction to the Project, (ii) the methodological framework for which the FRA is to provide biomass and carbon stock estimates, and (iii) the definition of the specific terms and concepts used throughout the report (Chapter 1). • The objectives pursued by the FRA, starting with (i) the general objectives, which are further refined in terms of (ii) the areal sampling frame, (iii) the elements sampled, (iv) the variables of interest to be estimated, and (v) the precision to be achieved (Chapter 2). • The inventory design, considering (i) the sources of information, (ii) the inventory method, (iii) the sampling unit design, (iv) the sample size planned and actually achieved, (v) the sampling type and distribution, and (vi) the estimation design (Chapter 3). • The field implementation, describing (i) the human and material resources deployed, (ii) how the field work was organized, (iii) how the sampling units were located and marked on the ground, (iv) how the variables were assessed or measured, (v) the precautions taken to assure and control quality of the field work, and (vi) the time and cost of the field data collection (Chapter 4). • The data processing, covering (i) the software, database and database system application used, and (ii) the data entry together with the corresponding quality assurance and quality control measures (Chapter 5). • The results of the FRA, examining successively (i) the species diversity, (ii) the stand composition, (iii) the stand structure in terms of density, basal area and above-ground biomass, (iv) the timber stocks and (v) the forest carbon stocks, including a tier 1 carbon stocks estimate for mangroves (Chapter 6); • The uncertainties of the estimates, analyzing successively the five main sources of uncertainty, namely (i) the statistical sampling error, (ii) the representativeness of the sampling network, (iii) the measurement errors, (iv) the data entry errors and (v) the estimation design, which are consolidated into (vi) an overall error budget (Chapter 7).

In the open and closed forests of the Panay Mountain Range, 53 plus 33 sampling units have been located in the field, permanently marked and measured. During the inventory 230 different species (based on their scientific names; 203 other species were only defined through their local names) were sampled and identified with a diameter at breast height (DBH) or diameter above buttress (DAB) ≥ 5.0 cm. The total forest carbon stock is estimated be 10.516 million t C (on average 220 t C/ha) in closed forests, 12.215 million t C/ha (175 t C/ha) in open forests, and 204,800 t C/ha (526 t C/ha) in mangroves.

1. Introduction and background A forest resources assessment (FRA) was carried out in 2015 in the Panay Mountain Range by the Forest and Climate Protection Project Panay - Phase II. This chapter briefly recalls the background of the Project (Chapter 1.1), before conveying the methodological framework governing the inventory design (Chapter 1.2) and defining the specific terms and concepts (Chapter 1.3) used throughout this report. 1.1 Forest and Climate Protection Project - Phase II The German Federal Ministry for the Environment, Nature Conservation, Housing and Nuclear Safety (BMUB) funded Forest and Climate Protection Project Panay - Phase II aims at the protection of the Panay Mountain Range with globally significant biodiversity and at the sustainable and climate friendly management and use of the natural resources in the adjacent areas through the upscaling and mainstreaming of the innovative and successful approaches to (i) forest land use planning (FLUP), (ii) participatory planning and implementation of forest conservation and management, (iii) forest rehabilitation, as well as (iv) agroforestry and income generation developed during the Project's first phase from August 2010 to May 2014. Implemented by the Department of Environment and Natural Resources (DENR), the Project contributes to (i) the implementation of the Strategic Plan 2011 - 2020 of the United Nations Convention on Biological Diversity (UNCBD) with achievement of strategic Aichi targets, (ii) the achievement of global targets for sustainable forest management (SFM) and (iii) the reduction of emissions from deforestation and forest degradation (REDD+).

The following 38 municipalities adjacent to the Panay Mountain Range were selected as territorial, actually jurisdictional units for FLUP elaboration and project implementation: • In the Province of Aklan the Municipalities of Buruanga, Ibajay, Libacao, Madalag, Makato, Malay, Malinao and Nabas; • In the Province of Antique the Municipalities of Anini-Y, Barbaza, Belison, Bugasong, Culasi, Hamtic, Laua-An, Libertad, Pandan, Patnongon, San Jose, San Remigio, Sebaste, Sibalom, Tibiao, Tobias Fornier (formerly known as Dao) and Valderrama; • In the Province of Capiz the Municipalities of Jamindan and Tapaz; and • In the Province of Iloilo the Municipalities of Alimodian, Calinog, Igbaras, Janiuay, Lambunao, Leon, Maasin, Miagao, San Joaquin and Tubungan.

Among others, the Project shall achieve the following indicators: • The forest cover within the Panay Mountain Range does not annually decline by more than 0.6% from 2010 (baseline: 3.8% annually) to 2017;

• At least 50,000 t of greenhouse gaz (GHG) emissions (expressed in CO2 equivalent) have been additionally avoided respectively have been sequestered in the Panay Mountain Range by 2017.

It is in support of the assessment of these indicators that a forest resources assessment (FRA) was carried out in the selected municipalities, in preparation of the use of the "Stock Difference Method" (see Chapter 1.2). 1.2 Methodological framework The 2006 IPCC guidelines for national GHG inventories propose two methods of calculating carbon stock changes in a given carbon pool for a given land-use category in the agriculture, forestry and other land use (AFOLU) sector: • The "Gain - Loss Method" also referred to as "Mass Balance", estimating the difference between increases (transfer from another carbon pool or increase of biomass [removal]) and decreases (transfer to another carbon pool or emissions) of the amount of carbon; • The "Stock Difference Method", estimating the change of carbon stocks through measurements at two (or more) points in time (which reflects the emissions and removals).

The stock difference method is robust, transparent and most common to monitor the carbon stock changes from the five activities eligible under REDD+, namely (i) reducing emissions from deforestation, (ii) reducing emissions from forest degradation, (iii) conservation of forest carbon stocks, (iv) sustainable management of forests, and (v) enhancement of forest carbon stocks.

The stock difference method requires two estimations: • "Activity Data" (AD), defined as "data on the magnitude of human activity resulting in emissions or removals taking place during a given period of time". In the forest sub-sector, the AD corresponds to the area deforested for forest land converted to other land uses, and to the area changes between the different forest strata for forest land remaining forest land. • "Emission Factor" (EF), defined as "a coefficient that relates the activity data to the amount of chemical compound which is the source of later emissions". In the forest sub-sector, the EF corresponds to the CO2 equivalent of the carbon stock per unit area of a forest stratum.

Emissions per stratum are estimated though the multiplication of the activity data by the emission factor.

The forest area by strata has been mapped nationwide by the National Mapping and Resource Information Authority (NAMRIA), through visual classification of medium- to high- resolution multi spectral satellite data acquired mainly in 2010. A new wall-to-wall mapping assessing the 2015 land cover is under way. The results, however, won't be available before 2017.

Carbon stock per unit area of forest for the different strata must be determined using appropriate probabilistic (statistical) field sampling inventory methods. The inventory design is described in detail in Chapter 3. 1.3 Definition of terms and concepts In the FRA, a number of terms and concepts have been used, with quite specific meanings attached to them. The definitions (and as much as applicable sources) of these terms are given hereafter, except for the "Stock Difference Method", which has already been explained in Chapter 1.2 above.

1.3.1 Forest Forest is defined according to DENR Memorandum Circular 2005-005 of 26 May 2005 as "Land with an area of more than 0.5 ha and tree crown (or equivalent stocking level) of more than 10%. The trees should be able to reach a minimum height of 5 m at maturity in situ. It consists either of closed forest formations where trees of various Layers and undergrowth cover a high portion of the ground or open forest formations with a continuous vegetation cover in which tree crown cover exceeds 10%. Young natural stands and all plantations established for forestry purposes, which have yet to reach a crown density of more than 10% or tree height of 5 m are included under forest. These are normally forming part of the forest area which are temporarily unstocked as a result of human intervention or natural causes but which are expected to revert to forest. It includes forest nurseries and seed orchards that constitute an integral part of the forest; forest roads, cleared tracts, firebreaks and other small open areas; forest within protected areas; windbreaks and shelter belts of trees with an area of more than 0.5 ha and width of more than 20 m; plantation primarily used for forestry purposes, including rubber wood plantations. It also includes bamboo, palm and fern formations (except coconut and oil palm)."

Coconuts and oil palms occurring in forests are part of the forest carbon stock.

1.3.2 Species abundance and diversity The following terms and/or indices are commonly used in ecological studies to measure species abundance and diversity: • The relative frequency of a particular species, defined as the proportion in percent (%) of the sampling units where that species has been sampled. • The relative density of a particular species, defined as its proportion in percent (%) of the total density (N), all species combined.

• The relative dominance of a particular species, defined as its proportion in percent (%) of the total basal area (G), all species combined. • The importance of a particular species, defined as the sum of its relative frequency, density and dominance, typically used to determine the rank of species. • Species richness, referring to the number of (different) species. • The Margalef index, measuring species richness. • The Shannon H' index, measuring species abundance, simultaneously taking the evenness of the species distribution into account; higher values indicate more diversity and evenness. • The Shannon E index, measuring evenness. • The Berger-Parker and Simpson indices, measuring species dominance; higher values indicate more dominance, hence less diversity.

The mathematical expressions used to calculate the diversity indices are given in Chapter 3.6.1.

1.3.3 Carbon pools Following the 2006 IPCC guidelines for national GHG inventories in the AFOLU sector (IPCC 2006b, p. 1.9), the forest carbon pools are defined as follows (see also Figure 1 for an illustration): • Living biomass (LB), composed of: o Above-ground biomass (AGB): "All biomass of living vegetation, both woody and herbaceous, above the soil including stems, stumps, branches, bark, seeds, and foliage. In cases where forest understory is a relatively small component of the above-ground biomass carbon pool, it is acceptable for the methodologies and associated data used in some tiers to exclude it, provided the tiers are used in a consistent manner throughout the inventory time series." The Panay Mountain Range FRA set the inventory threshold (minimum diameter) for the living vegetation to 5.0 cm. It excluded the herbaceous vegetation, which normally does not contribute much to the forest carbon stock. o Below-ground tree biomass (BGB): "All biomass of live roots. Fine roots of less than (suggested) 2 mm diameter are often excluded because these often cannot be distinguished empirically from soil organic matter or litter."

• Dead organic matter (DOM), composed of: o Dead wood (DW): "Includes all non-living woody biomass not contained in the litter, either standing, lying on the ground, or in the soil. Dead wood includes wood lying on the surface, dead roots, and stumps, larger than or equal to 10 cm in diameter (or the diameter specified by the country)." The Panay Mountain Range FRA set the inventory threshold for dead wood to 5.0 cm. o Litter (LI): "Includes all non-living biomass with a size greater than the limit for soil organic matter (suggested 2 mm) and less than the minimum diameter chosen for dead wood (e.g. 10 cm), lying dead, in various states of decomposition above or within the mineral or organic soil. This includes the litter layer as usually defined in soil typologies. Live fine roots above the mineral or organic soil (of less than the minimum diameter limit chosen for below-ground biomass) are included in litter where they cannot be distinguished from it empirically." The Panay Mountain Range FRA set the inventory threshold for dead wood to 5 cm. Hence, litter comprises all non-living biomass with a size greater than 2 mm and less than 5 cm.

• Soil organic matter (SOM): "Includes organic carbon in mineral soils to a specified depth chosen by the country and applied consistently through the time series. Live and dead fine roots and DOM within the soil, that are less than the minimum diameter limit (suggested 2 mm) for roots and DOM, are included with soil organic matter where they cannot be distinguished from it empirically. The default for soil depth is 30 cm."

Figure 1. Forest carbon pools (source: DiRocco et al. 2014)

1.3.4 IPCC key categories According to the 2006 IPCC guidelines for national GHG inventories, Volume 1 (IPCC 2006a), a key category is "one that is prioritized within the national inventory system because its estimate has a significant influence on a country's total inventory of greenhouse gases in terms of the absolute level, the trend, or the uncertainty in emissions and removals" (IPCC 2006a, p. 4.5).

Two approaches for performing the key category analysis should be used concomitantly: • According to Approach 1, key categories are those that, when summed together in descending order of magnitude, add up to 95% of the total level (IPCC 2006a, p. 4.12). • According to Approach 2, key categories are those that, when summed together in descending order of magnitude, add up to 90% of the total uncertainty (IPCC 2006a, p. 4.19).

In other words, forest strata or carbon pools that hold less than 5% of the total forest carbon stock are not key. It is good practice to focus the available resources for the inventory onto categories identified as key. Non-key categories may be estimated using lower tier methods.

1.3.5 IPCC tiers A tier represents a level of methodological complexity. Usually three tiers are provided: Tier 1 is the basic method, tier 2 intermediate and tier 3 most demanding in terms of complexity and data requirements.

Tier 1 methods are designed to be the simplest to use, for which equations and default parameter values (e.g. emission factors) are provided by IPCC. Activity data, however, must always be country-

9 specific. For tier 1 there are often globally available sources of activity data estimates (e.g. deforestation rates), though these data are usually spatially coarse.

Tier 2 can use the same methodological approach as tier 1, but applies emission factors that are based on country- or region-specific data. Higher temporal and spatial resolution and more disaggregated activity data are typically used in tier 2.

At tier 3, higher order methods are used, including models and inventory measurement systems tailored to address national circumstances, repeated over time, and driven by high-resolution activity data and disaggregated at sub-national level. These higher order methods provide estimates of greater certainty than lower tiers. Such systems may include comprehensive field sampling repeated at regular time intervals and/or GIS-based systems of land-use and management activity data, integrating several types of monitoring. Pieces of land where a land-use change occurs can usually be tracked over time, at least statistically. In most cases these systems have a climate dependency, and thus provide source estimates with interannual variability. Models should undergo quality checks, audits, and validations and be thoroughly documented.

1.3.6 Forest sampling-related terms The forest sampling related terms are defined hereafter, in line with the terminology found in FAO (2012), Johnson (2000), Kleinn et al. (2013), Mandallaz (2008), Schreuder et al. (2004), etc. • Areal sampling frame: The sampling frame or continuum (area) from which dimensionless sample points are selected based on a statistical sampling design. • Sampling design: The statistical framework or design that describes how sample points are selected (e.g. simple random sampling or systematic sampling). • Sample point: A point selected from the areal sampling frame to which a sampling unit is associated, where data are to be collected through observations and/or measurements. • Sampling unit: Basic observation unit in forest sampling associated to a sample point, designed according to a decision rule defining which population units are to be included in the sampling unit at each sample point. Common decision rules are fixed area plots, angle-count (plot-less) samples and transects. When more than one decision rule is involved, the sampling unit is also known as cluster. • Plot: A common decision rule used to configure a sampling unit, defining an area of some geometric shape where population units are to be observed and/or measured. • Variable: A characteristic of the objects of interest that can take on different values and follows a distribution, e.g. the elevation, slope and slope orientation of a sample point, the species, diameter and height of a tree, etc. • Parameter: A characteristic of the population, e.g. the total, mean (average), range of values or distribution of the density, basal area, volume, biomass, etc.

2. Objectives This chapter starts recalling the general objectives of the FRA (Chapter 2.1). These are then successively refined in the subsequent chapters in terms of the areal sampling frame (Chapter 2.2), the elements sampled (Chapter 2.3), the variables of interest to be estimated (Chapter 2.4) and the precision to be achieved (Chapter 2.5). 2.1 General objectives The FRA aimed to provide for the selected project sites in the Panay Mountain Range information about the forest condition and carbon stocks of the key forest strata. This information comprises: • Stand and stock data estimates, notably: o Species abundance and diversity, o Stand density (N), o Basal area (G), o Merchantable volume (V), o Stand composition (proportions of species or species groups in terms of N, G and V), o Stand structure (distribution of N, G and V by diameter classes),

• Forest carbon stock estimates for key carbon pools: o Living biomass (LB), composed of: - Above-ground biomass (AGB), - Below-ground tree biomass (BGB),

o Dead organic matter (DOM), composed of: - Dead wood (DW), - Litter (LI),

o Soil organic matter (SOM).

The data should satisfy the requirements of the 2006 IPCC guidelines for national GHG inventories in the AFOLU sector (see IPCC 2006b) to: • Determine emission factors (EF), • Estimate the change of carbon stocks using the "Stock Difference Method" (see Chapter 1.2) by providing initial forest carbon stocks (at T0, prior to the implementation of REDD+ eligible activities).

The attribute "key" attached to forest strata and carbon pools is used in the sense of the key category analysis of the IPCC guidelines (see Chapter 1.3.4).

Living biomass and dead organic matter of key forest strata and key carbon pools are to be estimated using tier 3 methods. Soil organic matter in general and forest carbon stocks of non-key forest strata may be estimated using tier 1 methods (see Chapter 1.3.5 for the definition of the tiers). 2.2 Areal sampling frame The areal sampling frame consisted of the key forest strata (closed forests and open forests) of the selected project sites (38 municipalities of Aklan, Antique, Capiz and Iloilo Provinces adjacent to the Panay Mountain Range, see Chapter 1.1).

There were no time nor financial resources allocated for a mapping of the land cover and a stratification of the forest cover. Hence, the forest cover and strata boundaries were taken from the 2010 NAMRIA national forest cover map, see Figure 2. Table 1 summarizes the area statistics.

Figure 2. 2010 NAMRIA land cover of the selected project sites in the Panay Mountain Range

Table 1. 2010 Land and forest cover areas of the selected project sites in the Panay Mountain Range

Province Closed Open forests Mangroves Others Total land area forests [ha] [ha] [ha] [ha] [ha] Aklan 11,698 20,114 244 79,044 111,100 (8 Municipalities) Antique 20,663 28,799 134 208,366 257,962 (17 Municipalities) Capiz 13,466 7,560 0 80,404 101,430 (2 Municipalities) Iloilo 2,055 13,269 11 158,205 173,540 (10 Municipalities) Total 47,882 69,742 389 526,019 644,032

2.3 Scope and content The elements sampled in the field consisted of the following: • Trees (all species, including bamboos, palms, rattan and tree ferns; in the remainder of this document, the term "tree(s)" refers to all these woody vascular plants, unless specific taxa are mentioned) with a diameter at breast height (DBH) or diameter above buttress (DAB) ≥ 5 cm. • Dead wood, both standing and lying, down to a small end diameter of 5 cm (the smaller fractions are part of the litter). • Litter.

Trees with a DBH or DAB < 5 cm have been excluded from the FRA and the forest carbon stock estimates because more than 96% of the AGB of tropical forests is found in trees with DBH or DAB ≥ 10 cm (Gillespie et al. 1992). This is supported by Lasco et al. (2006), who reported that 98% of the AGB in Surigao del Sur is found in trees with DBH or DAB ≥ 19.5 cm.

The inventory threshold for trees and dead wood thus consistently amounted to 5 cm (in diameter). 2.4 Variables of interest The FRA aimed to provide estimates of the following variables (also called attributes) of interest, disaggregated as much as possible and applicable by forest strata, jurisdictions, species and diameter classes: • For trees: o Species richness, o Berger-Parker, Margalef, Shannon (H' and E) and Simpson diversity indices, o Relative frequency, o Relative density, o Relative dominance, o Importance, o Quadratic mean diameter (Dg), o Density (N), o Basal area (G), o Merchantable volume (V), o Above-ground biomass (AGB), o Below-ground biomass (BGB),

• For dead wood: o Density (N) of standing dead wood,

o Basal area (G) of standing dead wood, o Volume (V) of standing and of lying dead wood, o Above-ground biomass (AGB) of standing and of lying dead wood,

• Biomass of litter (LI), • Soil organic matter (SOM), • Forest carbon stock (C).

The estimates of N, G, V, AGB, BGB, LI, SOM and C should be calculated including all following statistical parameters: • Sample size (n), • Mean (y̅), • Variance (s²), • Standard variation (s), • Coefficient of variation (s%), • Standard error of the mean (S), • Margin of error (E) at 90%, 95% and 99% confidence level.

2.5 Targeted precision The FRA strived to estimate the total forest carbon stock with a margin of error at 90% confidence level hopefully not exceeding 10%, time and budget permitting. The sample size actually achieved and the resulting margins of error are reported in Chapter 7.1.

3. Inventory and estimation design The inventory design used is a refinement of the methodology of the forest carbon baseline study (described by Schade et al. 2013) carried out from mid-2011 to end-2012 in Leyte in the framework of the BMUB-supported Climate Relevant Modernization of Forest Policy and Piloting of Reducing Emissions from Deforestation and Forest Degradation (REDD) Project. It takes into account evolving international standards and good practices with regards to forest carbon stock assessment in compliance with the latest (2006) IPCC guidelines for national GHG inventories.

This chapter starts with an account of the sources of information that have been used (Chapter 3.1). The following sections detail the essential inventory design elements, such as the inventory method (see Chapter 3.2), the sampling unit design (see Chapter 3.3), the sample sizes planned and actually achieved (see Chapter 3.4) and the sampling type and distribution (see Chapter 3.5) The last section (Chapter 3.6) details the estimation design used to calculate the variables of interest and the statistical parameters. 3.1 Sources of information Apart from the tree, stand or site characteristics counted, estimated and/or measured in the field, the FRA made use of the following available information, whose sources are described hereafter: • Geographical-political subdivisions, see Chapter 3.1.1, • Forest strata and areas, see Chapter 3.1.2, • Soil classes for the estimation of the soil organic matter, see Chapter 3.1.3, • Wood specific gravity for the estimation of the above-ground biomass of trees, see Chapter 3.1.4.

3.1.1 Geographical–political subdivisions The names of the geographical-political subdivisions were taken from the Philippine Standard Geographic Code (PSGC) developed and regularly updated by the National Statistical Coordination Board (NSCB), which can be downloaded from http://nap.psa.gov.ph/activestats/psgc/. As of December 2016, the PSGC comprises 18 regions, 81 provinces, 145 cities, 1,489 municipalities and 42,036 barangays.

In the absence of publicly available authoritative boundaries of the geographical-political subdivisions, the boundaries downloadable from the GADM database of Global Administrative Areas (http://gadm.org/) have been used.

3.1.2 Forest strata and areas The forest areas and their stratification were taken from the 2010 NAMRIA land cover map (see Table 1 and Figure 2), released in 2013, that was elaborated through visual interpretation of medium- to high- resolution multi-spectral satellite data (116 ALOS AVNIR 2, 40 SPOT 5 and 29 LANDSAT 7 gap-filled scan line detector (SLC) off scenes covering the national territory, acquired mainly 2010), adopting a minimum mapping area of 0.5 ha in accordance with the 2005 DENR forest definition (see Chapter 1.3.1), distinguishing the following three forest strata: • Closed forests: tree crown cover > 40%; • Open forests: 10% < tree crown cover ≤ 40%; • Mangroves.

Tree plantations have not been mapped as a separate class, since the satellite data did not warrant their comprehensive and systematic identification. The documentation of the classification and its accuracy (confusion matrix) has not been published yet. A new wall-to-wall mapping of the 2015 land cover is under way.

3.1.3 Soil classes The World Reference Base (WRB) soil classes have been looked up from the 2013 FAO soil map of the Philippines prepared by the Bureau of Soils and Water Management (BSWM 2013), see Figure 3.

Combined with the IPCC climate region (IPCC 2006b, p. 3.39), depending on the mean annual temperature (MAT), the elevation and the mean annual precipitation (MAP), the soil organic matter carbon stocks can be estimated using the IPCC tier 1 data (IPCC 2006b, p. 2.31) summarized in Table 2.

Table 2. IPCC Tier 1 soil organic matter stocks

Climate region Soil FAO soil class Soil organic matter [t C/ha] Tropical, moist High activity clay Cambisols 65 Kastanozems Luvisols Phaeozems Regosols

Low activity clay Acrisols 47 MAT > 18°C Nitosols elevation ≤ 1,000 m Sandy soils Arenosols 39 MAP ≤ 2,000 mm Volcanic soils Andosols 70 Wetland soils Gleysols 86 Tropical, wet High activity clay Cambisols 44 Kastanozems Luvisols Phaeozems Regosols

Low activity clay Acrisols 60 MAT > 18°C Nitosols elevation ≤ 1,000 m Sandy soils Arenosols 66 MAP > 2,000 mm Volcanic soils Andosols 130 Wetland soils Gleysols 86 Tropical, montane High activity clay Cambisols 88 Kastanozems Luvisols Phaeozems Regosols

Low activity clay Acrisols 63 MAT > 18°C Nitosols elevation > 1,000 m Sandy soils Arenosols 34 Volcanic soils Andosols 80 Wetland soils Gleysols 86

Source: IPCC (2006b).

Fluvisols are not mentioned. Depending on their texture, they may be treated like sandy soils or wetland soils.

Figure 3. 2013 BSWM FAO soil map of the selected project sites in the Panay Mountain Range

3.1.4 Wood specific gravity Wood specific gravity (p, expressed in grams per cubic centimeter [gr/cm³] or in tonnes per cubic meter [t/m³]) is one of the variables needed when using certain allometric equations for the estimation of biomass, such as the equation developed by Chave et al. (see Chapter 3.6.3.2). The values have been looked up (and averaged whenever several gravities were available) by species or species group growing in South-East Asia from the following sources: • Global wood density database prepared by Zanne et al. (2009); • Publication on wood densities of tropical tree species by Reyes et al. (1992).

For species not found in any of the above cited sources, the average wood specific gravity for tropical tree species in Asia of 0.57 g/cm³ published by Brown (1997) has been used. 3.2 Inventory method The FRA adopted a probabilistic (statistical) sampling. The sample consisted of a predetermined number of sampling units, where tree, stand or site characteristics were counted, assessed or measured in circular plots spatially arranged around the sample points. If the sample point associated to the sampling unit fell into the areal sampling frame (see Chapter 2.2), the sampling unit was to be measured. 3.3 Sampling unit design Each sampling unit consisted of a cluster centered on the sample point, composed of the following circular plots (see Figure 4): • Four nested plots with their centers at 40 m horizontal distance from the sample point in the four cardinal directions (north, east, south and west), each consisting of two concentric circular sub-plots: o 5 m radius sub-plot (corresponding to an area of 0.0079 ha) for: - the sampling of small-sized trees with 5 cm ≤ DBH or DAB < 20 cm for the estimation of their contribution to the AGB and BGB; - the sampling of standing dead wood with DBH or DAB ≥ 5 cm for the estimation of their contribution to the DOM; - the sampling of lying dead wood down to a diameter of 5 cm for the estimation of their contribution to the DOM; - the sampling of litter for the estimation of its contribution to the DOM.

o 10 m radius sub-plot (corresponding to an area of 0.0314 ha) for: - the sampling of big-sized trees with DBH or DAB ≥ 20 cm for the estimation of their contribution to the AGB and BGB;

• One plot with 25 m radius centered on the sample point, for the ocular assessment of the land cover, to serve as remote sensing training and validation data.

The entire cluster is inscribed in an area of 100 m x 100 m (1 ha).

On average, 14.5 trees were sampled in each nested plot, which falls within the commonly recommended range of 12 to 20 trees in uneven-aged forests reputed to offer the best compromise in terms of sampling efficiency, considering the ratio between the "unproductive" time invested in retrieving sampling units and the "productive" time measuring them.

Clusters were used to increase the representativity of the sampling units. Indeed, compared to a solid (non-clustered) sampling unit of the same area, a cluster covers a wider area. Statistically, one cluster constitutes one observational unit. For the computation of the results per ha, the following blow-up factors are applicable:

• Parameters measured in the 10 m radius plots: 10,000 / (4 × 휋 × 102) = 7.9577; • Parameters measured in the 5 m radius plots: 10,000 / (4 × 휋 × 52) = 31.8310.

The sample points and nested plot centers were marked permanently to be prepared for their re- measurement.

N 40 m

sample point W E 40 m

nested plot composed of 2 sub-plots

Figure 4. Sampling unit design

3.3.1 Observations and measurements at the sample points The following variables were assessed or measured at the sample points: • Administrative location: province, city or municipality and barangay. • Actual coordinates. • Elevation. • Slope. • Slope orientation. • Terrain: 11 classes (plateau; summit or crest; upper slope; middle slope; lower slope; bench or terrace; valley; plain; narrow depression; water course; dunes). • Land classification: legal status (forest land or alienable and disposable).

The following variables were assessed within a radius of 25 m horizontal distance around the sample points: • Land cover: 12 classes (forest; marshland or swamp; fallow; shrubs; wooded grassland; grassland; annual crop; perennial crop; open or barren land; built-up area; fishpond; inland water).

• Forest type: 10 types (dipterocarp old growth forest; dipterocarp residual forest; mossy forest; submarginal forest; closed pine forest; open pine forest; mangrove old growth forest; mangrove reproduction forest; native tree plantation forest; other plantation forest). • Tree crown cover: 3 classes (tree crown cover ≤ 10%; 10% < tree crown cover ≤ 40%); tree crown cover > 40%).

3.3.2 Observations and measurements in the nested plots The following variables were assessed or measured at the four nested plot centers (similar to the observations or measurements at the sample points): • Administrative location: province, city or municipality and barangay. • Actual coordinates. • Elevation. • Slope. • Slope orientation. • Terrain: 11 classes (plateau; summit or crest; upper slope; middle slope; lower slope; bench or terrace; valley; plain; narrow depression; water course; dunes). • Land classification: Legal status (forest land or alienable and disposable).

The following variables were assessed or measured in the 5 m radius sub-plots of the four nested plots: • Plant diversity. • Ground coverage classes for six (6) vegetation layers according to height (< 50 cm; 50 cm ≤ height < 130 cm; 130 cm ≤ height < 200 cm; 2 m ≤ height < 4 m; 4 m ≤ height < 10 m; height > 10 m): 4 classes (none; coverage ≤ 10%; 10% < coverage ≤ 50%; coverage > 50%). • For each of the sampled small-sized trees with 5 cm ≤ DBH or DAB < 20 cm: species, azimuth and horizontal distance (from the plot center), and DBH or DAB. • For each of the sampled standing dead wood (including stumps) with DBH or DAB ≥ 5 cm: species, azimuth and horizontal distance (from the plot center), DBH or DAB and merchantable height. • For each of the sampled lying dead wood sections (those portions that are within the 5 m horizontal distance radius circle) down to a diameter of 5 cm: mid-diameter and length. • Litter: ground coverage percentage plus average depth.

The following variables were assessed or measured in the 10 m radius sub-plots of the four nested plots: • Land cover: 12 classes (forest; marshland or swamp; fallow; shrubs; wooded grassland; grassland; annual crop; perennial crop; open or barren land; built-up area; fishpond; inland water). • Forest type: 10 types (dipterocarp old growth forest; dipterocarp residual forest; mossy forest; submarginal forest; closed pine forest; open pine forest; mangrove old growth forest; mangrove reproduction forest; native tree plantation forest; other plantation forest). • Tree crown cover: three classes (tree crown cover ≤ 10%; 10% < tree crown cover ≤ 40%); tree crown cover > 40%). • For each of the sampled big-sized trees with DBH or DAB ≥ 20 cm: species, azimuth and horizontal distance (from the plot center), DBH or DAB and merchantable height.

Table 3 summarizes the sub-plot sizes and the assessments or measurements made on trees and dead wood.

Table 3. Overview of sub-plot sizes and assessments or measurements made on trees and dead wood

Item Trees Dead wood Small-sized Big-sized Standing Lying 5 cm ≤ Dref* < 20 cm Dref* ≥ 20 cm Dref* ≥ 5 cm Dref* ≥ 5 cm Sub-plot radius 5 m 10 m 5 m 5 m Species Species Species Species – Azimuth Azimuth Azimuth Azimuth – Horizontal distance Horizontal distance Horizontal distance Horizontal distance – Diameter DBH or DAB DBH or DAB DBH or DAB Mid-diameter Height or length – Merch. Height Merch. Height Section length

* Dref of trees and standing dead wood refers to DBH or DAB, Dref for lying dead wood refers to the small end diameter.

3.4 Sample size and margin of error It was initially planned to measure 200 sampling units with the available budget and time frame. These should suffice to estimate the above-ground biomass with a margin of error of around ± 10% at 90% confidence level. Table 4 shows a preliminary estimate of the provisional statistical precision of the biomass stock estimates together with the computational assumptions in terms of average AGB stock and coefficient of variation per stratum.

Table 4. Preliminary estimate of provisional statistical precision of the biomass estimates

Stratum Area Sample size Above-ground biomass [ha] y̅ s% E%* [t d. m./ha] [%] [%] Closed forests 47,882 81 360 ± 75 ± 14 Open forests 69,742 119 200 ± 100 ± 15 Total 117,624 200 ± 87 ± 10

* 90% confidence level

Due to the very difficult terrain (mountains up to 2,000 m, steep and rocky slopes, deeply dissected valleys), accidents and occasional security problems, it was only possible to effectively measure 86 complete sampling units.

The factors that have contributed to the lower than expected output are the following: • Remoteness and very difficult accessibility of the area to be inventoried. The team could seldom reach the sample points within one day from the next road or trail accessible by four- wheel-drive car and had to establish camps in the forest. The travel, hiking, establishing and moving camps took 2 to 3 days per week, so the net working time was just 2 to 3 days per week. The terrain was very steep and rocky, often without trails, and the teams could only move very slowly. • Information of and coordination with local officials, police and army (security reasons) and community members prior to the hiring of local helpers and/or guides and the conduct of the inventory activities, preventing the teams to swiftly proceed to the sample points or inventory camps. • Unfavorable weather conditions in the forest area (frequent rains), hampering or stalling the measurement and data recording operations.

• Some sample points pre-determined on the map could not be reached in the mountains (too steep, rocky, dissected river valleys / canyons, critical security situation), and the teams had to go back.

Fortunately, the coefficients of variation turned out to be lower than expected (see Table 5). Hence, the targeted precision could almost be achieved despite of the considerably reduced sample size.

Table 5. Sample size, coefficients of variation and margins of error of the biomass estimates

Stratum Area Sample size Above-ground biomass [ha] y̅ s% E%* [t d. m./ha] [%] [%] Closed forests 47,882 33 239.40 ± 57.31 ± 16.90 Open forests 69,742 53 176.22 ± 89.85 ± 20.67 Total 117,624 86 201.94 ± 74.56 ± 13.34

* 90% confidence level

3.5 Sampling type and distribution The FRA used a probabilistic (statistical) sampling. The initially planned 200 sample points were drawn at random without replacement from the 1,178 nodes of a quadratic grid with a side length of 1 km located within the areal sampling frame. The sampling unit ID is composed of a three-letter code identifying the Province ("AKL" for Aklan, "ANT" for Antique, "CAP" for Capiz, and "ILI" for Iloilo), followed by a one-letter code ("M" signifying "measurement", "C" signifying "control"), followed by a four-digit number corresponding to the consecutively numbered 1,178 nodes. For instance, "AKLM0010" identifies the measurement data of sampling unit N° 10 located in Aklan.

Figure 5 shows the distribution of the 86 effectively measured sampling units in the Panay Mountain Range. Annex 1 provides the list of these sampling units with their Universal Transverse Mercator (UTM) and World Geodetic System (WGS) 84 geographic coordinates.

Figure 5. Distribution of the sampling units effectively measured

3.6 Estimation design The following sections provide information on the estimation design, in other words how (i) variables of interest that cannot be observed or measured directly (such as merchantable volume, biomass, diversity indices, etc.), and (ii) statistical parameters (such as mean, variance, standard error, margin of error) were calculated.

In the absence of allometric equations specifically developed for the trees, bamboos, palms, rattan and tree ferns found in the tropical rainforests of the Philippines, the biomass was calculated using the equations found in the literature and databases (notably on the web platform GlobAllomeTree, see http://www.globallometree.org/). Whenever several equations were available, preference was given to expressions that do not use height, since the latter is difficult to measure in tropical rainforests, hence constituting an important source of uncertainty.

3.6.1 Diversity indices The species diversity indices are computed as follows: • Berger-Parker index: 푁 푑 = 푚푎푥 푁 (1) • Margalef index: (푆−1) 퐷 = 푀푔 ln (푁) (2) • Shannon H' index: 푛 푛 퐻′ = − ∑푆 (( 푖) × 푙푛 ( 푖)) 푖=1 푁 푁 (3) • Shannon E index: 퐻′ 퐸 = ln (푆) (4) • Simpson index: 푛 ×(푛 −1) 퐷 = ∑푆 푖 푖 푖=1 푁×(푁−1) (5)

with o 푁 total number of individuals

o 푛푖 number of individuals of species i o 푁푚푎푥 number of individuals of the most abundant species o 푆 number of species (species richness) o 푙푛 natural logarithm

3.6.2 Merchantable volume of trees The merchantable volume (V, expressed in cubic meter [m³] inside bark) of trees and standing dead wood is calculated based on the diameter at breast height or above buttress (Dref) and the merchantable height (H) using the Philippine regional volume equations for Dipterocarps and Non- Dipterocarps (DENR 2014): 푉 = 0.00004649 × 퐷푟푒푓2 × 퐻 Dipterocarps, Regions 6 & 7 & Palawan except Bohol (6) 푉 = 0.00004874 × 퐷푟푒푓2 × 퐻 Non-Dipterocarps, Regions 6 & 7 & Palawan except Bohol (7)

3.6.3 Above-ground biomass of trees Two different allometric equations, developed by Brown (1997) and more recently by Chave et al. (2014), respectively, may be used alternatively to estimate the above-ground biomass (AGB, expressed in kilogram of dry matter [kg d.m.]) of trees.

3.6.3.1 Allometric equation by Brown (1997) Brown's allometric equation for the estimation of the AGB of trees in moist climatic zones was developed based on the destructive measurement of 170 trees, with 5 cm ≤ Dref ≤ 148 cm: 퐴퐺퐵 = exp (−2.134 + 2.530 × ln(퐷푟푒푓)) (R² = 0.97) (8) with • AGB oven-dry above-ground biomass of trees, in kg d.m. • Dref diameter at breast height (1.30 m) or above buttress (30 cm), in cm

3.6.3.2 Allometric equation by Chave et al. (2014) Chave's et al. allometric equation for the estimation of the AGB of tropical trees was developed based on the destructive measurement of 4,004 trees, with 5.0 cm ≤ Dref ≤ 180.0 cm: 퐴퐺퐵 = exp (−1.803 − 0.976 × 퐸 + 0.976 × ln(푝) + 2.673 × ln(퐷푟푒푓) − 0.0299 × (ln(퐷푟푒푓))2) (9) with • AGB oven-dry above-ground biomass of trees, in kg d.m. • p wood specific gravity, in g / cm³ (by species or species groups, see Chapter 3.1.4) • Dref diameter at breast height (1.30 m) or above buttress (30 cm), in cm • E environmental variable measuring stress, defined as: 퐸 = (0.178 × 푇푆 − 0.938 × 퐶푊퐷 − 6.61 × 푃푆) × 10−3 (10) with • TS temperature seasonality, the standard deviation of the monthly mean temperature over a year, expressed in degrees Celsius multiplied by 100 • CWD climatological water deficit in mm per year, computed by summing the difference between monthly rainfall and monthly evapotranspiration, only when this difference is negative • PS precipitation seasonality, the coefficient of variation in monthly rainfall values, expressed in percent of the mean value

A global grid layer of E at 2.5 arc-minute resolution is available at http://chave.ups-tlse.fr/pantropical_ allometry.htm#E and has been integrated into the FRA database system application used to store, manage and analyze the inventory data (see Chapter 5.1). The values of E are extrapolated from the gridded layer based on the geographic coordinates of the sample points.

3.6.4 Above-ground biomass of bamboos The AGB of bamboos is calculated using the allometric equation developed by Priyadarsini (1998, cited in Zemek 2009, p. 94) based on the destructive measurement of Dendrocalamus asper in Indonesia, with 3 cm ≤ Dref ≤ 7 cm: 퐴퐺퐵 = 0.1312 × 퐷푟푒푓2.2784 (R² = 0.95) (11) with • AGB oven-dry above-ground biomass of bamboos, in kg d.m. • Dref diameter at breast height (1.30 m), in cm

3.6.5 Above-ground biomass of palms The AGB of palms is calculated using the allometric equation developed by Goodman et al. (2013), based on the destructive measurement of 97 palms in Western Amazonia, with 6 cm ≤ Dref < 40 cm: 퐴퐺퐵 = exp(−3.3488 + 2.7483 × ln(퐷푟푒푓)) (R² = 0.80) (12) with • AGB oven-dry above-ground biomass of palms, in kg d.m. • Dref diameter at breast height (1.30 m), in cm 25

3.6.6 Above-ground biomass of rattan and tree ferns No allometric equation for the estimation of the AGB of rattan and tree ferns could be found.

3.6.7 Below-ground biomass of trees, bamboos and palms The below-ground biomass (BGB, expressed in kilogram of dry matter [kg d.m.]) of trees, bamboos, palms and tree ferns is calculated based on the AGB using the AGB to BGB ratio (R) of the 2006 IPCC guidelines (IPCC 2006, p. 4.49): 퐵퐺퐵 = 푅 × 퐴퐺퐵 (13) with • BGB Oven-dry below-ground biomass of trees, bamboos and palms, in kg d.m. • R BGB to AGB ratio: 0.37 • AGB Oven-dry above-ground biomass, in kg d.m.

3.6.8 Above-ground biomass of standing dead wood The AGB of standing dead wood (SDW, expressed in tonnes of dry matter [t d.m.]) is calculated based on (i) the merchantable volume (V) estimated using the Philippine regional volume equations for Dipterocarps and Non-Dipterocarps (see Chapter 3.6.2) and (ii) the biomass conversion and expansion factors (BCEFs) of the 2006 IPCC guidelines (IPCC 2006, p. 4.52), (iii) divided by 2 to account for decay (Thiele et al. 2010, p. 74): 푆퐷푊 = 푉 × 퐵퐶퐸퐹푠 / 2 (14) with • SDW biomass of standing dead wood, in t d.m. • V merchantable volume inside bark of standing dead wood, in m³ • BCEFs Biomass conversion and expansion factor of merchantable growing stock volume to AGB for humid tropical natural forests, in t / m³, depending on the growing stock level: o 9.00 t d.m. / m³ for V < 10 m³ / ha o 4.00 t d.m. / m³ for 10 m³ / ha < V ≤ 20 m³ / ha o 2.80 t d.m. / m³ for 20 m³ / ha < V ≤ 40 m³ / ha o 2.05 t d.m. / m³ for 40 m³ / ha < V ≤ 60 m³ / ha o 1.70 t d.m. / m³ for 60 m³ / ha < V ≤ 80 m³ / ha o 1.50 t d.m. / m³ for 80 m³ / ha < V ≤ 120 m³ / ha o 1.30 t d.m. / m³ for 120 m³ / ha < V ≤ 200 m³ / ha o 0.95 t d.m. / m³ for V > 200 m³ / ha

3.6.9 Biomass of lying (downed) dead wood The biomass of lying (downed) dead wood (LDW, expressed in tonnes of dry matter [t d.m.]) is calculated by section based on (i) the section volume (V) estimated using the mid-diameter (Dref) and length (L) of the section assimilated to a cylinder and (ii) the average wood density for Asia (Reyes et al. 1992, p. 2), (iii) divided by 2 to account for decay (Thiele et al. 2010, p. 74):

퐷푟푒푓2 퐿퐷푊 = π × × 퐿 × 푝/ 2 (15) 40,000 with • LDW biomass of lying dead wood, in t d.m. • Dref mid-diameter of lying dead wood section, in cm • L length of lying dead wood section within the sub-plot, in m • p average wood density for Asia: 0.57 t d.m. / m³

3.6.10 Biomass of litter The biomass of litter (LI, expressed in tonnes of dry matter [t d.m.]) is calculated based on (i) the volume of litter estimated using the ground coverage percentage (C) and the average depth (DPT) of litter and (ii) the average density of litter (Chojnacky et al. 2009 in South. J. appl. For. 33(1) 2009, p. 32): 푉 = C × 퐷푃푇 × 10,000 × 퐷 (16) with • LI biomass of litter, in kg d.m. / ha • C ground coverage percentage of litter, in% • DPT average depth of litter, in m • D average density of litter: 40 kg d.m. / m³

3.6.11 Conversion of biomass to carbon The carbon (C) equivalent of the biomass is calculated based on the AGB and BGB using the following carbon fractions (CF, expressed in tonnes of carbon per tonne of dry matter [t C / t d.m.]) of the 2006 IPCC guidelines (IPCC 2006, p 4.48): 0.47 t C / t d.m. Carbon fraction of dry matter for living biomass (17)

0.37 t C / t d.m. Carbon fraction of dry matter for dead organic matter (18)

3.6.12 Statistical inference Assuming for simplicity's sake a random distribution of the sample points, the estimated stratum and total means, variances, standard errors and margins of error are computed using the following formulas (see Zöhrer 1980): • Stratum means: 푛 ∑ 푗 푖=1 푦푖푗 푦̅푗 = (19) 푛푗 • Stratum variances: 2 푛푗 푛푗 ∑ 푦2 − (∑ 푦 ) / 푛 2 푖=1 푖푗 푖=1 푖푗 푗 푠푗 = (20) 푛푗−1 • Stratum standard errors: 푠푗 푆푗 = (21) √푛푗 • Stratum margins of error: 푠푗 퐸푗 = × 푡푗 (22) √푛푗 • Total mean: 푛 푦̅ = ∑푀 푗 × 푦̅ 푗=1 푛 푗 (23) • Total variance: 푛 푠2 = ∑푀 푗 × 푠2 푗=1 푛 푗 (24) • Total standard error:

1 2 푆 = √ × (∑푀 푃 × 푠 ) (25) 푛 푗=1 푗 푗 • Total margin of error:

1 2 퐸 = √ × (∑푀 푃 × 퐸 ) (26) 푛 푗=1 푗 푗

with o 푦푖푗 variable (such as number of trees per ha, basal area per ha, volume per ha, biomass per ha, etc.) of sampling unit i in stratum j o 푦̅푗 arithmetic mean of variable 푦 in stratum j o 푦̅ total arithmetic mean of variable 푦 2 o 푠푗 variance of variable 푦 in stratum j o 푠2 total variance of variable 푦 o 푆푗 standard error of the mean of variable 푦 in stratum j o 푆 total standard error of the mean of variable 푦 o 퐸푗 margin of error of the mean of variable 푦 in stratum j o 퐸 total margin of error of variable 푦 o 푀 number of strata o 푛푗 number of sampling units in stratum j o 푡푗 two-tailed Student t-value with 푛푗 degrees of freedom in stratum j at a given confidence level C, commonly 90%, 95% or 99% o 푛 total number of sampling units o 푃푗 weight of stratum j

4. Field data collection This chapter provides a detailed account of the field data collection. The human and material resources deployed are related in Chapter 4.1. The organizational aspects are described in Chapter 4.2. The main body expands on how the sample points and nested plot centers were located in the field and marked on the ground (Chapter 4.3), and on how the variables were assessed or measured (Chapter 4.4). The precautions taken to assure and control quality of the field work are described in Chapter 4.5. Chapter 4.6 concludes with a summary of the time and cost of the field data collection. 4.1 Human and material resources 4.1.1 Human resources Two teams carried out the field work between April and December 2015 during a net assignment (including training) of 8 months (a limit imposed by the available budget). Each team was composed of the following: • Team leader: o Mr. Keneth A. Bornias, B.Sc. Forestry; o Mr. Guilberto P. Sarceno, B.Sc. Forestry;

• Assistant: o Mrs. Crystal Jade M. Lapeciros, B.Sc. Forestry; o Mrs. Diosyleta D. Prado, B.Sc. Forestry;

• Three helpers, recruited locally, familiar with the area and knowledgeable about tree species and forest products.

The team leaders were responsible for the security of the team, for the equipment entrusted to them, and for the work of their members. They directed the members, validated the data observed or measured by their assistants, and completed the field data forms.

The assistants handled the equipment and carried out the observations and measurements.

The helpers advised on getting to the sample points, carried the equipment, opened and/or brushed trails, access and sighting lines, marked the sample points and centers of the nested plots, helped the assistants in carrying out the measurements, and marked the trees.

A control team re-measured four sampling units (5% of the measured sampling units) for quality control purposes (see Chapter 4.5) between 04 February 2016 and 02 June 2016. This team was composed of: • Junior Advisor Mrs. Jenylyn J. Daisog, B.Sc. Forestry; • Chief Advisor Dr. Jürgen Schade; • Three helpers.

4.1.2 Inventory equipment To carry out the field work, each team was equipped with the following: • One handheld IPX7 waterproof GPS receiver (GARMIN GPSMap 78 series) with proven ability to operate under difficult signal reception conditions (under tree cover), to get to the sample points and measure coordinates. • One handheld IPX7 waterproof precision compass (SUUNTO KB 14/360) graduated in degrees for the measurement of bearings or azimuth. • One handheld IP54 laser hypsometer (LASER TECHNOLOGY Inc. [LTI] TruPulse Laser 200 rangefinder) for the measurement of tree heights using the trigonometric principle, hence capable of measuring distances and inclination angles. Regrettably, the LTI TruPulse Laser 200

hypsometer is not waterproof. A better choice would have been the IP55 waterproof LTI TruPulse Laser 200X. • One handheld IPX7 waterproof precision clinometer (SUUNTO PM 5/360) as alternative to and backup for the laser hypsometer (a strategy that paid off when the LTI TruPulse laser hypsometers failed to work after heavy rain). • One fiberglass distance tape, 30 m, to measure distances. • One steel diameter tape, 5 m, to measure tree diameters. Upon request of the teams, the steel tapes were replaced with fiberglass tapes to lessen the risk of injuries from the sharp cutting edges of the steel tapes. • Per sampling unit five iron rods (of at least 1 cm diameter and 50 cm length) to permanently mark the sample points and the four plot centers, forced at least 4/5 of its length into the ground, topped each with a 50 cm bright-colored 1/2 " PVC pipe to facilitate the retrieval for quality control purposes (see Chapter 4.5). • One hatchet to force the iron rods used to permanently mark the sample points and the four plot centers into the ground. • One first aid kit. • One backpack to carry the equipment. • Personal field work gear for the team leaders and assistants (boots, rain coats, head lamps, sleeping bags, etc.). • Camping equipment (tents, mobile stoves, etc.).

4.2 Organization of the field work 4.2.1 Field manual A field manual (Lennertz et al. 2014) explaining the use and care of the equipment, the configuration of the sampling units and the orderly sequencing of the field operations to get to, establish, permanently mark and assess and/or measure the sampling units, prepared by the BMUB funded National REDD+ System Philippines Project, was used to ensure that the field work followed standard operating procedures (SOP), minimizing operating errors and maximizing the homogeneity of the data acquisition.

The data were recorded with pencils on the respective paper field data forms (see Appendix 1). They were regularly collected by the Junior Advisor, Mrs. Jenylyn J. Daisog, coordinating and supervising the field works, and taken to the office for electronic databanking and processing.

4.2.2 Training At the onset of the field work, the team leaders and assistants participated in a five days training in April 2015, followed by one week of closely supervised inventory work. During that time, the teams became thoroughly familiar with the sampling design, the instruments (especially the laser dendrometer) and the data recording procedure. All team leaders and assistants had already worked for forest inventories and were knowledgeable about tree species. In addition, two dendrology refresher courses of six days each were held in June and July 2015. The training has been carried out at two forest sites in Panay by Prof. Dr. Manuel L. Castillo (Department of Forest Biological Sciences, UP Los Baños). He was also available throughout the FRA, assisting in the identification of species not known to the teams, based on local names, digital pictures and/or specimen of samples forwarded to him.

4.2.3 Inventory camps The sample points were grouped into batches assigned to inventory camps (see Figure 6) on the basis of their location and accessibility. A reasonable compromise had to be found between (i) the number of sample points assigned to a specific camp (ideally not less than the number of sampling units that can be observed and measured in one field mission of one to two weeks) and (ii) the distance from the camp to the furthest sample point (in principle so that a team could get to the sample point and perform all observations and measurements in one day). For safety concerns the two teams generally operated from the same camp. Due to the remoteness and poor accessibility of

most forest sites, the equipment and supplies for the entire duration of a field mission had to be hauled on foot to the camps.

Figure 6. Inventory camp

4.3 Getting to and marking of sampling units 4.3.1 Getting to the sample points The sample points were located in the field on the basis of their geographic coordinates using handheld GPS stand-alone receivers. The coordinates were uploaded from a computer as points of interest (POIs) rather than as waypoints, using the GARMIN POI loader software (freeware downloadable from http://www8.garmin.com/support/ mappingsw.jsp). POIs offer the advantage that unlike waypoints, they cannot be edited nor erased from the GPS receivers (unless connected to a computer and with the use of the aforementioned software).

Good sources of information to study the approach of sample points are the following: • Topographic maps in Open Cycle Map (http://www.opencyclemap.org) showing the "Outdoors" base layer, which is particularly useful for the appreciation of the relief (see Figure 7); • Satellite images in Google Maps (http://www.google.com/maps), Bing Maps (http://www.bing.com/maps) and Apple Maps (only available on Apple Mac OS and iPhone or iPad iOS operating systems), particularly where high resolution satellite data are available (see Figure 8). It is worthwhile to compare the different sources for best results, since the images are regularly updated.

As much as possible, the approach of a targeted sample point was studied together with local helpers or guides, who are well versed with the terrain, existing trails, unsurmountable barriers and/or obstacles such as steep hills or waterlogged areas to be avoided.

Figure 7. Open Cycle Map with "Outdoors" Base Layer Cangaranan River, Valderrama, Antique

Figure 8. Google Maps versus Apple Map Vicinities of SU No. AKLM0010 and AKLM0400, Barangay Dalagsa-an, Libacao; Google Map image (to the left) and Apple Map image (to the right) retrieved on 08 November 2016

4.3.2 Location of sample points and plot centers Considering the limited positional precision of stand-alone GPS measurements (in practice ± 10 m, as evidenced by the virtual movement of an immobilized GPS receiver, which is a remarkable precision to come close to any point on the globe from whatever origin over considerable distances, but insufficient

to measure distances of less than 100 m to 200 m, since the relative precision deteriorates to 10% - 5%), the location of sample points was determined covering the last 10 m to 15 m by compass and horizontal distance measurement (referring to the azimuth and distance to the sample point displayed by the GPS receiver once the distance to the destination was less than 15 m) using a distance tape or a ranging laser, in order to prevent bias (preference for easily accessible areas) when closing in on the sample point. The same applied to the location of the four nested plot centers, situated at 40 m in the four cardinal directions (north = 0°; east = 90°, south = 180°; west = 270°) from the sample point.

The azimuth was measured with the help of a handheld precision compass.

4.3.3 Permanent marking of sample points and plot centers The sample points and the four nested plot centers of each sampling unit were permanently marked with an iron rod (of at least 1 cm diameter and 50 cm length), forced at least 4/5 of its length into the ground, topped with a 50 cm bright-colored 1/2 " PVC pipe to facilitate the retrieval for quality control purposes (see Chapter 4.5 and Figure 9).

Figure 9. Location and marking of sample points and plot centers On the way to a sample point in Malinao, Aklan (left); Permanent marking of a sample point using an iron rod topped with a PVC pipe (right).

4.3.4 Inaccessible sample points and plot centers In the rare event that one of the four nested plot centers of a sampling unit turned out to be inaccessible because of unsurmountable obstacles, it was re-located at 80 m horizontal distance from the sample point in the next cardinal direction, turning clockwise (see Figure 10: if the western plot center was inaccessible, it could be re-located at 80 m horizontal distance to the west + 90° = north from the sample point).

In the equally rare event that a sample point turned out to be inaccessible, the sampling unit was abandoned. A replacement sample point was drawn at random from those nodes of the quadratic grid with a side length of 1 km (see Chapter 3.5) located (i) in the same forest stratum and (ii) at a similar elevation as the inaccessible sample point.

If the sample point or one of the nested plot centers of the sampling unit fell on an area whose land cover assessed in the field was other than forest, it was not re-located, but observed and measured as is.

re-located W 80 m

N 40 m

inaccessible

sample point W E 40 m

nested plot composed of 2 sub-plots

Figure 10. Re-location of inaccessible plots

4.4 Assessment or measurement of variables The variables to be assessed or measured were recorded on specifically designed paper field forms (see Appendix 1), following the standards and specifications described in the following sections.

4.4.1 Administrative location The administrative location, comprising at least the region, province and city or municipality, and as much as possible the barangay, was assessed at and recorded for the sample points and the four plot centers. Hence, five such observations were recorded per sampling unit (in some cases, a sampling unit may be crossed by an administrative boundary).

4.4.2 Actual coordinates The actual Universal Transverse Mercator (UTM) coordinates, comprising the zone (in the Philippines 50 in Palawan, 52 in the eastern-most portions of Mindanao, 51 elsewhere), the northing in m and the

easting in m, were measured at and recorded for the sample points and the four nested plot centers. Hence, five coordinate measurements were performed per sampling unit. The coordinates were read from the GPS stand-alone receiver, immobilized at the sample point or plot center, using averaging.

4.4.3 Elevation The elevation in m above sea level was measured at and recorded for the sample points and the four nested plot centers. Hence, five elevation measurements were performed per sampling unit. The elevation was read from the GPS stand-alone receiver.

4.4.4 Slope The slope was measured at and recorded for the sample points and the four nested plot centers. Hence, five slope measurements were performed per sampling unit. The slope corresponds to the average inclination in % measured with a handheld precision clinometer in two opposite directions along 10 m segments (oblique distance) of an imaginary straight line passing through the sample point or plot center, respectively, and following the steepest slope gradient (where water would run off).

4.4.5 Slope orientation The slope orientation was measured at and recorded for the sample points and the four nested plot centers. Hence, five slope orientation measurements were performed per sampling unit. The slope orientation corresponds to the azimuth in ° of the downhill direction of the imaginary straight line used for the measurement of the slope gradient, read from a handheld precision compass.

4.4.6 Terrain The terrain (topography) class was assessed at and recorded for the sample points and the four nested plot centers. Hence, five terrain classes assessments were performed per sampling unit. The assessment through ocular inspection distinguished the 11 classes defined by FAO (2012): • Plateau: Relatively flat (slope ≤ 5%); terrain of great extent and high elevation, above adjacent lowlands limited by an abrupt descent scarp on at least one side; may be dissected by deep valleys and deeply incised rivers. • Summit, crest: Crest of any kind or hilltop; can be sharp or rounded. • Upper slope: Upper slope of hillside (located on the upper 1/3 of the slope) (shoulder). • Middle slope: Middle slope of hillside (slope > 5%) (back slope). • Lower slope: Lower slope of hillside (foot slope). • Bench, terrace: Horizontal zone of average width over 30 m interposed in the valley side (slope < 15%) or a terrace over 6 m width. • Valley: Very wide, gently sloping depression with predominant extent in one direction commonly situated between two mountains or ranges of hills; the profile may be U- or V-shaped; includes river valley (formed by flowing water) or glacier valleys. • Plain: A large flat to very gently undulating area at a low elevation with reference to surroundings • Narrow depression: Enclosed depression or small, narrow valley or distinct crater (including ravine, gorges, gullies, canyons, etc.). • Water course: Permanent or temporary water course (river, etc.). • Dunes: Sandy hills developed through sand deposits from wind erosion or storms, often unstable and moving.

4.4.7 Land classification The land classification (legal status) was assessed at and recorded for the sample points and the four nested plot centers. Hence, five land classification assessments were performed per sampling unit. The assessment through consultation of the latest available land classification map from DENR distinguished 2 classes: • Forest land. • Alienable and disposable.

4.4.8 Land cover The land cover was assessed in and recorded for the 25 m radius plot and the four 10 m radius sub- plots. Hence, five land cover assessments were performed per sampling unit. The assessment through ocular inspection distinguished forests (further classified according to their type, see Chapter 4.4.9 below) and the 11 non-forest land cover classes used in the 2010 NAMRIA national forest cover map: • Forest: Land with an area of more than 0.5 ha and trees able to reach a minimum height of 5 m in situ with a crown cover of more than 10% (see Chapter 1.3.1 for the detailed definition). • Marshland or swamp. • Fallow. • Shrubs. • Wooded grassland. • Grassland. • Annual crop. • Perennial crop. • Open or barren land. • Built-up area. • Fishpond. • Inland water.

4.4.9 Forest type The forest type was assessed in and recorded for the 25 m radius plot and the four 10 m radius sub- plots. Hence, five forest type assessments were performed per sampling unit. The assessment through ocular inspection distinguished the 8 natural forest types used in the conduct of the second National Forest Resources Inventory (1979 - 1988), plus 2 additional types for planted (man-made) forests: • Dipterocarp old growth forest: Tropical rain forest dominated by Dipterocarpaceae with traces of commercial logging. • Dipterocarp residual forest: Tropical rainforest dominated by Dipterocarpaceae after commercial logging. • Mossy forest: Tropical rainforests of the high elevations dominated by Podocarpaceae, Myrtaceae and Fagaceae with trees of medium height and short boled, covered with epiphytes. • Submarginal forest: Tropical rainforest dominated by Leguminosae and lesser utilized species, mainly restricted to shallow and excessively drained lime stone soils. • Closed pine forest: Pure stands of Benguet (Pinus kesiya var. langbianensis) or Mindoro pine (P. merkusii) with crown cover > 30%. • Open pine forest: Pure stands of Benguet or Minodoro pine with 10% < crown cover ≤ 30%. • Mangrove old growth forest: Tidal forests dominated by Rhizophoraceae, Avicenniaceae or Sonneratiaceae, without traces of exploitation. • Mangrove reproduction forest: Tidal forests dominated by Rhizophoraceae Avicenniaceae or Sonneratiaceae, where utilization had been intensive and big trees had been removed. • Native tree plantation forest: Planted forest dominated by native rainforest species. • Other plantation forest: Planted forest dominated by non-native, often fast-growing tree species.

4.4.10 Tree crown cover The tree crown cover was assessed in and recorded for the 25 m radius plot and the four 10 m radius sub-plots. Hence, five tree crown cover assessments were performed per sampling unit. The assessment through ocular inspection distinguished the 3 classes currently used by NAMRIA for forest cover mapping: • Non-forest: tree crown cover ≤ 10%). • Open forest: 10% < tree crown cover ≤ 40%.

• Closed forest: tree crown cover > 40%.

4.4.11 Plant Diversity The plant diversity was counted in and recorded for the four 5 m radius sub-plots. Hence, four plant diversity counts were performed per sampling unit. The inventory consisted of the counting of distinct higher plant species observed, even if not known by their local, official common or scientific names. To avoid repeated counting of the same species, the count was done by only one person, systematically collecting specimen of leaves from plants that can be reached from the ground.

4.4.12 Ground coverage classes by vegetation layers Ground coverage classes for six vegetation layers were assessed in and recorded for the four 5 m radius sub-plots. Hence, four times six ground coverage classes assessments were performed per sampling unit. For each of the following six vegetation layers: • Grass, herbs and mosses; • Tree regeneration, shrubs and plants with 50 cm ≤ height < 130 cm; • Tree regeneration, bushes and plants with 130 cm ≤ height < 200 cm; • Undergrowth of any kind with 2 m ≤ height < 4 m; • Lower trees and other plants with 4 m ≤ height < 10 m; • High trees with height > 10 m;

The following four ground coverage classes were assessed through ocular inspection: • None; • Coverage ≤ 10%; • 10% < coverage ≤ 50%; • Coverage > 50%.

4.4.13 Ground coverage and average depth of litter Litter, defined as all non-living biomass with a size > 2 mm and < 5 cm (i.e. the minimum diameter for dead wood), lying dead, in various states of decomposition above or within the mineral or organic soil (see also Chapter 4.4.14), was inventoried in and recorded for the four 5 m radius sub-plots through ocular estimates of: • Ground coverage in%, and • Average depth in cm.

4.4.14 Mid-diameter and length of lying dead wood sections Lying dead wood, defined as all non-living woody biomass lying on the ground with a diameter ≥ 5 cm (i.e. the inventory threshold for dead wood and trees) not contained in the litter, was inventoried in and recorded for the four 5 m radius sub-plots. For each lying dead wood section within a sub-plot (without considering those portions extending beyond the sub-plot, see Figure 11), the following measurements were performed: • Mid-diameter: mid-diameter outside bark in cm, rounded to 0.1 cm, of the dead wood section within the 5 m radius sub-plot, without considering those portions (i) extending beyond the sub- plot, or (ii) with a diameter < 5 cm. The mid-diameter was measured using a caliper or a diameter tape. • Length: length in m, rounded to 0.1 m, of the dead wood section within the 5 m radius sub-plot, without considering those portions (i) extending beyond the sub-plot, or (ii) with a diameter < 5 cm. The length was measured using a distance tape.

If a lying dead wood section featured branches, these were measured separately.

m 5

# 03 # 02 Mid-Diameter Mid-Diameter Length Length sub-plot center

# 01 Mid-Diameter Length

Figure 11. Measurements on lying dead wood sections

4.4.15 Observations and measurements on trees and standing dead wood Trees and standing dead wood with DBH or DAB ≥ 5 cm were inventoried in and recorded for the four nested plots • in the 5 m radius sub-plot sampling o small-sized trees (all species) with 5 cm ≤ DBH or DAB < 20 cm; o standing dead wood with DBH or DAB ≥ 5 cm;

• in the 10 m radius sub-plot sampling big-sized trees (all species) with DBH or DAB ≥ 20 cm.

For each of the sampled trees and standing dead wood, (i) the species, (ii) azimuth and (iii) horizontal distance from the plot center, (iv) DBH or DAB and (v) for standing dead wood with DBH or DAB ≥ 5 cm as well trees with DBH or DAB ≥ 20 cm the merchantable height was assessed or measured and recorded as described hereafter.

In total, 4,794 trees and 195 standing dead wood have been sampled in the Panay Mountain Range.

4.4.15.1 Species The species of each sampled tree and, as much as possible, of each standing dead wood was recorded as identified by the team mates or the local helpers, referring to the official common name or the scientific name. Local names are not suited to unequivocally identify a species, because they vary from dialect to dialect, and even from place to place. In cases where a tree could only be identified through its local name, the latter was recorded, as much as possible together with other information

(such as digital pictures or specimens) that could facilitate the later identification of the species by its scientific name with the help of a dendrology expert from the local academe.

4.4.15.2 Azimuth The azimuth in ° of each sampled tree and standing dead wood was recorded as measured from the plot center using a handheld precision compass.

4.4.15.3 Horizontal distance The horizontal distance in m, rounded to 0.1 m, of each sampled tree and standing dead wood was recorded as measured from the plot center using a distance tape or a laser rangefinder.

Together with the azimuth, the horizontal distance provides the polar coordinates of the sampled trees and standing dead wood, which will be needed at the time of re-measurements to identify them one by one.

4.4.15.4 Diameter at breast height or above buttress The diameter at breast height or the diameter above buttress outside bark in cm, rounded to 0.1 cm, of each sampled tree and standing dead wood was recorded as measured using a diameter tape at the following measurement points (see also Figure 12): • in general, at breast height, i.e. 1.3 m above ground (DBH) as measured from the uphill side of the stem; • for trees with prominent buttresses or basal flanges at breast height, the diameter is measured 30 cm above the end of the buttresses or flanges (DAB); • for trees with bulges, swellings, depressions, branches or other abnormalities at breast height, the diameter is measured just below and above the abnormality at a point where it ceases to affect normal stem form, and computed as the average of the two measurements; • for stumps with a total height < 1.3 m at the section.

If a tree or standing dead wood forks immediately above breast height, the diameter was measured below the swell resulting from the fork. If a tree or standing dead wood forks below breast height, the stems were considered as separate trees or standing dead woods, respectively. On leaning trees or standing dead woods, the breast height was determined along the axis of the stem.

Whenever it proved impossible to measure the DBH or DAB with a diameter tape as described above (e.g. when the measurement point is inaccessible), it was approximated by comparison with a metric tape held horizontally at the base of the tree (see Figure 13).

Figure 12. DBH and DAB measurements (source: Zöhrer 1980)

Figure 13. Diameter estimates for inaccessible measurement points

4.4.15.5 Merchantable height The merchantable height in m, rounded to 0.1 m, of each sampled tree with DBH or DAB ≥ 20 cm and of each sampled standing dead wood with DBH or DAB ≥ 5 cm including stumps was recorded as measured using either a laser hypsometer.

Merchantable height of trees with DBH or DAB ≥ 35 cm is defined as the linear distance along the axis of the stem from the stump height to the top merchantability limit which is restricted by forks, large limbs, sweep, crook or decay, which make segments of the stem un-merchantable for saw logs. For trees with 15 cm ≤ DBH or DAB < 35 cm, the volume section is limited by a minimum top diameter inside bark which is fixed at 60% of DBH or DAB. By this definition, the measurement to the base of the tree has to be a measurement to the place where the felling cut would be applied, usually about 50 cm above ground, or above the buttresses. Limits for merchantability are the following: • Size of limbs and knots: The sum of diameters in any ¼ m segment ½ the diameter of the log at that point. Where limb and knot diameters exceed this limit, the merchantable height cannot extend through that point, unless there is a merchantable section of 3 m or more in length above that point. • Sweep: Sweep is a curvature in a tree section. Sweep is measured in centimeters of departure of the center line of the section from a straight line joining the centers of each end of the section. The departure is measured at the midpoint of the section containing the sweep. A simple rule for maximum sweep is that departure minus allowance for long taper cannot exceed ½ the small end diameter of the section. Merchantable length is terminated below a section with excessive sweep unless there is a merchantable section of 3 m or more in length above that section. • Crook: Crook is a more or less abrupt bending or angle in a tree section. Crook is measured in cm of maximum departure of the section center line from an extension of the center line of the straight portion of the log. The maximum departure cannot exceed ½ the small end diameter of the log. Excessive crook should terminate the merchantable length unless there is a merchantable section of 3 m or more in length above that section.

4.5 Quality assurance and quality control Apart from selecting qualified and experienced inventory team members and from their training (see Chapter 4.1.1), the key elements of the quality assurance were the following: • Use of a specific and detailed FRA manual (Lennertz et al. 2014), with instructions to be complied with to ensure that the field work followed SOPs, maximizing the homogeneity of the data acquisition and minimizing operating errors. • Regular supervision of the inventory teams, to check whether the inventory procedures, observations and measurements are carried out correctly.

To control the quality of the field work and to appreciate the measurement errors, 10% of the sampling units chosen at random and without prior knowledge of the inventory teams were planned to be re-measured independently. Due to budgetary constraints, however, only 5 % have ultimately been re-measured under the lead of the Junior Advisor, accompanied by the Chief Advisor. The re-measurement concerned sampling units No. AKLM0183, AKLM0732, AKLM0763 and AKLM0774.

The differences between the initial measurements and the (presumably correct) re-measurements (serving as reference) were assessed through the mean absolute deviation (MAD) and the root mean square deviation (RMSD). Table 6 provides an estimate of the impact of the deviations of the initial measurements from the control measurements on the main variables of interest. Due to the small number of sampling units compared, the deviations must be interpreted cautiously.

The following differences between the initial and re-measurements have been observed: • Diverging merchantable height measurements, because of the reduced visibility in the stands. Under such conditions, height measurements tend to be made from positions too close to the trees, leading to steep sighting angles, resulting in inaccurate estimates; this source of error was anticipated, hence the preference for allometric equations relying on DBH or DAB measurements only. • Diverging DBH or DAB measurements, at times observed during the re-measurements, due to non-standard measurement points above ground, diameter tapes either not tightened or not held horizontally; or the non-removal of vines during the initial measurements. • Diverging assessments of borderline trees (at the fringe of the 5 m and 10 m radii sub-plots), falsely considered either to be part or not to be part of the sample; hence the importance of a very through checking of such trees. • Missed or overlooked trees, erroneously not recorded in the sampling unit, especially in the small diameter range. • Missed or overlooked deadwood, erroneously not recorded in the sampling unit.

Table 6. Deviation of initial measurements from control measurements

Variable of Interest Mean absolute deviation Root mean square deviation [%]* [%]* Density 13.1 14.5 Basal area 7.9 9.7 Merchantable volume 30.2 42.9 Above-ground biomass 12.2 13.2 Standing dead wood 89.3 148.9 Lying dead wood 75.4 105.1 Litter 24.4 25.8 Number of plant species 18.9 22.8

* with reference to the re-measurements

4.6 Time and cost of the field data collection Based on the experience gained in the implementation of the forest carbon baseline study from mid- 2011 until end 2012 in Leyte in the framework of the Climate Relevant Modernization of Forest Policy and Piloting of Reducing Emissions from Deforestation and Forest Degradation (REDD) Project, it was initially expected that one inventory team could establish and measure an average of 16 sampling units per month or four per week. In the Panay Mountain Range FRA, however, this target could not be achieved. The output was only around two sampling units per week.

The factors that contributed to the lower than expected output are the following: • Remoteness and very difficult accessibility of the area to be inventoried. The teams could seldom reach the sample points within one day from the next road or trail accessible by four- wheel-drive car and had to establish camps in the forest. The travel, hiking, establishing and moving camps took two to three days per week, so the net working time was just another two to three days per week. The terrain was very steep and rocky, often without trails, and the teams could only move very slowly. • Information of and coordination with local officials, police and army (for security reasons) and community members prior to the hiring of local helpers or guides and the conduct of the inventory activities, preventing the teams to swiftly proceed to the sample points or inventory camps. • Unfavorable weather conditions in the forest area (frequent rains), hampering or stalling the measurement and data recording operations. • Some sample points pre-determined on the map could not be reached in the mountains (too steep, rocky, dissected river valleys or canyons, critical security situation), and the teams had to go back.

The costs of the field work are summarized in Table 7.

Table 7. Time and cost the field data collection

Item Unit Quantity Cost / Unit Total Cost [PHP/Unit] [PHP]* Personnel Cost 1,612,000 Team leaders incl. transport, etc. Person-month 16 46,500 744,000 (2 #) Assistants incl. transport, etc. Person-month 16 42,000 672,000 (2 #) Helpers (3 per team) Person-day 700 280 196,000 Equipment Cost (2 sets) 92,000 Laser dendrometer, compass, clinometer, measuring tapes Operational Cost 68,000 Consumables (backpacks, tents, batteries, paint, steel rods, etc.) Total 1,772,000

* 1 EUR = 52 PHP

5. Data processing The FRA data have been processed using free open-source database and software development tools. Chapter 5.1 shortly describes the software, database and database system application used. Chapter 5.2 provides a brief account of the data entry and the corresponding quality assurance and quality control measures. 5.1 Software, database and database application The popular relational database management system (RDBMS) ORACLE MySQL (see https://www.mysql.com) was chosen to serve as database engine. The database architecture and the user-friendly FRA database system application, both originally developed by the National REDD+ System Philippines Project to process FRA data collected in Davao Oriental and Eastern Samar, described by Barrois (2017) and Barrois et al. (2017), respectively, have been used for the entry, editing and analysis of the Panay Mountain Range FRA data. The software required for the installation and use of the FRA database system application Version 4.1, namely ORACLE MySQL Community Server 5.7.17.0 and ORACLE Java Standard Edition (SE) Runtime Environment [JRE] 8 Update 121, are all of charge.

The FRA database system application offers the following features: • All essential data management operations: add, delete, edit, print to PDF, backup, restore data. • A series of data integrity checks, attracting the data typist's attention with the help of "traffic lights" (green = integrity check passed; orange = warning; red = integrity check failed) to missing, out-of-range, incompatible and unusual values. • Printing to PDF of a schematic representation of the location of the trees and standing dead wood in the four nested plots of a sampling unit, facilitation their retrieval for re-measurement.

• A comprehensive and versatile data analysis framework, performed on any selection of sampling units: o Tabulation of the frequency distribution (in %) of the sample points and plot centers according to site variable classes (elevation-, slope-, slope orientation-, terrain-, land classification-, land cover-, forest type-, tree crown cover- and vegetation layer ground coverage- classes). o Cross-tabulation of the frequency distribution (in %) of the sample points and plot centers for any combination of two site variables. o Display of the species occurrence by geographic coordinates and/or in Google Earth. o Computation of species richness, the Berger-Parker (d), Margalef (DMg), Shannon (H' and E) and Simpson (D) diversity indices. o Computation of relative frequency, relative density, relative dominance, importance, density (N/ha), basal area (G/ha), merchantable volume (V/ha), above-ground biomass (AGB/ha), below-ground biomass (BGB/ha), living biomass (LB/ha) by species and by diameter class. o Computation of standing dead wood (SDW/ha), lying dead wood (LDW/ha), litter (LI/ha), dead organic matter (DOM/ha), soil organic matter (SOM/ha) and forest carbon stock (C/ha). o Provision of tabular results including statistical precision estimates that can be exported to MS Excel, printed to PDF or depicted as pie or bar charts.

5.2 Data entry, quality assurance and quality control The FRA data were entered progressively into the database as they came from the field. The editing was done by the Junior Advisor, Mrs. Jenylyn J. Daisog, B.Sc. Forestry. The use of a person with technical background and well versed with the FRA methodology, together with the data entry closely following the field work, were designed to contribute to quality assurance. Indeed, eventual gaps and errors observed could be ironed out with minimal effort, and the inventory teams could be cautioned on typical flaws and critical issues.

The encoded data have been thoroughly verified by the short-term Expert Mr. Ralph Lennertz, assisted by completeness, value range and plausibility checks implemented in the FRA database system application.

For quality control purposes, the stored data of 10% of the sampling units chosen at random were printed and subject to an independent comparison with the original field data forms. The comparison was done for sampling units No. AKLM1045, ANTM0184, ANTM0329, ANTM0392, ANTM0417, ANTM0439, ILIM0068 and ILIM0069. As for the quality control of the field data collection (see Chapter 4.5), the differences between the stored data and the original field data (serving as reference) were assessed through the MAD and the RMSD, shown in Table 8.

For 50% of the controlled sampling units, no discrepancies were found between the original field data and the encoded data. For the remaining sampling units, the following differences between the original field data and the encoded data were observed: • Typing errors. • Omission of data.

Table 8. Deviation of encoded data from field forms

Variable of Interest Mean absolute deviation Root mean square deviation [%]* [%]* Density 0.2 0.7 Basal area 1.2 3.3 Merchantable volume 3.0 8.3 Above-ground biomass 1.5 4.2 Standing dead wood 1.1 3.2 Lying dead wood 2.6 7.0 Litter 10.2 20.7 Number of plant species 2.7 7.7

* with reference to the field data forms

6. Detailed results The detailed results of the FRA are provided in Annex 4 (closed forests, based on 33 SUs) and Annex 5 (open forests, based on 53 SUs), as computed and printed to PDF by the FRA database system application (see Chapter 5.1). A summary analysis is presented hereafter, focusing successively on species diversity (Chapter 6.1), stand composition (Chapter 6.2), stand structure (Chapter 6.3), timber stocks (Chapter 6.4), and forest carbon stocks, including a Tier 1 carbon stocks estimate for mangroves (Chapter 6.5).

The results pertain to trees and dead wood with DBH or DAB ≥ 5.0 cm. The merchantable volume in cubic meter (m³) inside bark has been estimated using the Philippine regional volume equations for Dipterocarps and Non-Dipterocarps (see Chapter 3.6.2). If not otherwise stated, the AGB of trees has been estimated using the allometric equation developed by CHAVE et al. (2014, see Chapter 3.6.3.2). 6.1 Species diversity Species diversity consists of two components: species richness and species abundance.

Species richness (i.e. the number of species observed or sampled) is the simplest measure of diversity. It is dependent on the sample size and the sampling unit size. The Margalef index also measures species richness. Despite the attempt to correct for the sample size, it remains strongly influenced by the sampling effort. Moreover, the results are very different if densities are used instead of absolute numbers (as required by definition, see Chapter 3.6.1).

Species abundance may be appreciated through the relative representation of a species, in terms of relative frequency, relative density and/or relative dominance (proportion of the basal area). The sum of the three figures corresponds per definition to the importance, which is meaningful for ranking purposes only, not for the comparison of absolute values. Shannon's H' index also measures species abundance, simultaneously taking the evenness of the species distribution into account. It increases as both the richness and the evenness increases. Shannon's E (equitability) index measures the evenness, ranging from 0 and 1. Lower values indicate more diversity, while higher values indicate less diversity (more evenness). The opposite of evenness, dominance, is measured by the Berger- Parker and the Simpson indices, ranging from 0 to 1. Again, lower values indicate more diversity, while higher values indicate less diversity (more dominance).

Table 11 shows the values of the species diversity indices calculated based on the sampling units observed and measured in the FRA. The figures must be interpreted cautiously, taking into consideration both the sample size (number of sampling units) and the size of the sampling units.

Annex 2 lists the official common names of the species recorded in the FRA, including their scientific names and the wood specific gravity used for the calculation of their biomass.

Annex 3 lists by botanical families the scientific names of the species recorded, including the number of observations in the closed and open forests, respectively, and the maximum DBH or DAB measured.

A total of 236 species have been identified through their scientific names, belonging to 159 genera and 63 families. The family with the largest number of species observed is the family of the Moraceae (21 species), followed by the Meliaceae (15 species), the Clusiaceae, Dipterocarpaceae, Lauraceae and Phyllanthaceae (9 species each), the Euphorbiaceae and Rubiaceae (8 species each), and the Sapotaceae (7 species).

Table 9 lists the threatened species according to the International Union for Conservation of Nature and Natural Resources (IUCN) red list of threatened species (see http://www.iucnredlist.org/) recorded in the FRA. Many Dipterocarps are considered "critically endangered" by IUCN. All but two species in Table 9 listed as endangered and critically endangered belong to the family Dipterocarpaceae.

Table 10 lists the threatened species according to DENR AO 2007-01 recorded in the FRA.

Table 9. Threatened species according to IUCN

Critically Endangered (CR) species Almon (Shorea almon) Guijo (Shorea guiso) Antipolo (Artocarpus blancoi) Red Lauan (Shorea negrosensis) Apitong (Dipterocarpus grandiflorus) White Lauan (Shorea contorta) Gisok-Gisok (Hopea philippinensis) Endangered (EN) species Mahogany (Swietenia mahagoni) Tiaong (Shorea ovata) Vulnerable (VU) species Almaciga (Agathis philippinensis) Malakape (Psydrax dicoccos) Balobo (Diplodiscus paniculatus) Malakatmon (Dillenia luzoniensis) Butlo (Aquilaria cumingiana) Malasantol (Sandoricum vidalii) Dalingdingan (Hopea foxworthyi) Molave (Vitex parviflora) Dalinsi (Terminalia pellucida) Narra (Pterocarpus indicus) Hamindang (Macaranga bicolor) Nato (Palaquium luzoniense) Is is (Ficus ulmifolia) Pahutan (Mangifera altissima) Kalingag / Cinamomon (Cinnamomum mercadoi) Pili (Canarium ovatum) Katmon (Dillenia philippinensis) Puso-puso (Neolitsea vidalii) Lanutan (Mitrephora lanotan) Sakat (Terminalia nitens) Magabuyo (Celtis luzonica) Tarangisi (Aglaia cumingiana) Malak malak (Palaquium philippense)

Table 10. Threatened species according to DENR AO No. 2007-01

Critically Endangered species (category A) Dalingdingan (Hopea foxworthyi) Kamagong (Diospyros discolor) Gisok-Gisok (Hopea philippinensis) Narra (Pterocarpus indicus) Kalantas (Toona calantas) Endangered species (category B) Alupag (Dimocarpus longan subsp. malesianus) Molave (Vitex parviflora) Alupag amo (Litchi chinensis) Tiaong (Shorea ovata) Vulnerable species (category C) Almaciga (Agathis philippinensis) Lamio (Dracontomelon dao) Almon (Shorea almon) Lanutan (Mitrephora lanotan) Amugis (Koordersiodendron pinnatum) Malak-malak (Palaquium philippense) Anislag (Flueggea flexuosa) Nato (Palaquium luzoniense) Dao (Dracontomelon dao) Pahutan (Mangifera altissima) Kalingag (Cinnamomum mercadoi) Pakong buwaya (Cyathea contaminans) Kalulot (Artocarpus rubrovenius) Red Lauan (Shorea negrosensis) Labayanan (Radermachera coriacea) White Lauan (Shorea contorta) Other threatened species (category D) Duguan (Myristica philippinensis) Malasantol (Sandoricum vidalii) Malakatmon (Dillenia luzoniensis) Pili (Canarium ovatum)

Table 11. Species diversity indices

Variable Closed Open Closed & open forests forests forests (33 SUs) (53 SUs) (86 SUs) Species richness* 178 193 238 Margalef index* 23.4157 24.4401 28.1753 Shannon H' index* 3.7033 4.3915 4.3057 Shannon E index* 0.7147 0.8345 0.7868 Berger-Parker index 0.0286 0.0249 0.0211 Simpson index* 0.1278 0.0735 0.0935

* referring to species identified through their scientific names; species only identified by local names are accounted as undetermined trees, bamboos, palms, rattan or tree ferns (as appropriate)

6.1.1 Species diversity of closed forests In the 33 sampling units measured in the closed forests, a total of 225 different species have been found and 172 thereof identified through their scientific names. About 1/3 of the sampled trees (36% in terms of density, 32% in terms of basal area, 31% in terms of merchantable volume and AGB) remained undetermined.

From 7 to 60, on average 32 different higher plant species have been observed per sampling unit.

Table 12 lists the 20 most "important" species (in the sense of the definition given in Chapter 1.3.2), led by Tuai and Pagsahingin-bulog. Three Urticaceae (Alagasi, Dalunot and Alilaua), two Moraceae (Tibig and Hawili), two Lauraceae (Kalingag / Cinamomon and Puso-puso) and two Sapotaceae (Nato and Bansalangin) are among the most important species, as well as a tree fern (Pakong buwaya). Remarkably, Dipterocarps occupy lower ranks in terms of their importance: Guijo appears on rank 25, White Lauan on rank 36, Red Lauan on rank 37, and Bagtikan on rank 51.

Figure 14 shows that it takes a considerable number of species, ranked in decreasing order of their contribution to N/ha, G/ha, V/ha and AGB/ha, respectively, to constitute 1/3, 1/2 or 2/3 of the totals: • Eight species, namely Banai-banai, Tuai, Kalulot (Artocarpus rubrovenius), Agoho, White Lauan, Bagtikan, Guijo and Bakan (Litsea philippinensis) together represent 33.3 % of the merchantable volume; 24 species together represent 50.6% of the merchantable volume; 70 species together represent 66.6% of the merchantable volume; • Fourteen species, namely Tuai, Banai-banai, Agoho, Kalulot, Bayanti, Balete, Ulayan, Guijo, Gulob, Bagtikan, White Lauan, Bakan, Bansalangin and Pagsahingin-bulog represent together 33.3% of the AGB; 39 species together represent 50.0% of the AGB; 136 species together represent 66.6% of the AGB; • Twenty one species together represent 33.2% of the basal area; 53 species together represent 50.1% of the basal area; 164 species together represent 66.6% of the basal area; • Twenty seven species together represent 33.5% of the density; 66 species together represent 49.9% of the density.

These figures show that the closed forests are both species rich and diverse.

Table 12. Relative frequency, density and dominance, importance and rank of the 20 most "important" species in closed forests

Species Relative Relative Relative Importance frequency density dominance [%] Rank [%] Rank [%] Rank [*] Rank Tuai 48.48 1 1.18 10 5.29 1 54.96 1 Bischofia javanica Pagsahingin-bulog 39.39 2 1.92 6 1.50 7 42.81 2 Canarium asperum Tibig 36.36 3 1.97 5 1.23 9 39.56 3 Ficus nota Alagasi 36.36 3 2.23 3 0.83 21 39.42 4 Leucosyke capitellata Banai-banai 33.33 4 0.81 19 3.15 2 37.30 5 Radermachera pinnata Agoho 30.30 5 1.80 7 2.47 4 34.57 6 Casuarina equisetifolia Pakong buwaya 27.27 6 2.49 2 2.74 3 32.51 7 Cyathea contaminans Kalomala 30.30 5 0.83 18 0.85 19 31.98 8 Elaeocarpus calomala Kalingag / Cinamomon 27.27 6 0.93 15 0.99 15 29.19 9 Cinnamomum mercadoi Southern Bangkal / Hambabalud 27.27 6 0.87 17 0.63 31 28.77 10 Neonauclea formicaria Hawili 27.27 6 0.90 16 0.29 59 28.46 11 Ficus septica Bayanti 21.21 8 2.86 1 2.02 5 26.09 12 Aglaia rimosa Nato 24.24 7 0.67 22 0.75 22 25.67 13 Palaquium luzoniense Malaruhat / Panglomboyen 24.24 7 0.64 23 0.70 27 25.59 14 Syzygium claviflorum Bansalangin 21.21 8 1.00 14 0.88 18 23.09 15 Mimusops elengi Puso-puso 21.21 8 0.57 26 0.21 81 21.99 16 Neolitsea vidalii Dalunot 18.18 9 2.21 4 1.07 12 21.46 17 Pipturus arborescens Batino 18.18 9 0.29 40 0.55 35 19.03 18 Alstonia macrophylla Alilaua 15.15 10 1.56 8 0.84 20 17.55 19 Oreocnide trinervus Tabau 15.15 10 1.28 9 0.63 30 17.07 20 Lumnitzera littorea

* sum of relative frequency, density and dominance

Figure 14. N/ha, G/ha, V/ha and AGB/ha by number of species in closed forests

6.1.2 Species diversity of open forests In the 53 sampling units measured in the open forests, a total of 362 different species have been found and 185 thereof identified through their scientific names. Almost 1/3 of the sampled trees (33% in terms of merchantable volume, 30% in terms of AGB, 28% in terms of density and 27% in terms of basal area) remained undetermined.

From 11 to 12, on average 32 different plant species have been observed per sampling unit.

Table 13 lists the 20 most important species, led by Pagsahingin-bulog, followed by Southern Bangkal / Hambabalud, Banai-banai, Alagasi and Tuai. Three Moraceae (Hawili, Tibig and Balete), two Clusiaceae (Binucao and Bitanghol) and two Burseraceae (Pagsahinging-bulog and Bogo) are among the most important species, as well as coconut. Remarkably, Dipterocarps occupy lower ranks in terms of their importance: White Lauan appears on rank 48, Guijo on rank 53, and Gisok-Gisok on rank 85.

Figure 15 shows that it takes a very important number of species, ranked in decreasing order of their contribution to N/ha, G/ha, V/ha and AGB/ha, respectively, to constitute 1/3, 1/2 or 2/3 of the totals: • Eleven species, namely Tuai, Taluto, Narra, White Lauan, Pahutan, Benguet Pine, Banai-banai, Nato, Tabau, Bagtikan and Dao (Dracontomelon dao) together represent 32.8% of the merchantable volume; 29 species together represent 50.1% of the merchantable volume; 108 species together represent 66.6% of the merchantable volume; • Twenty four species together represent 33.4% of the AGB; 61 species together represent 50.0% of the AGB; 196 species together represent 66.6% of the AGB; • Twenty seven species represent together 33.1% of the basal area; 66 species together represent 50.1% of the basal area; 165 species together represent 66.6% of the basal area; • Thirty five species represent together 33.1% of the density; 82 species together represent 50.1% of the density; 213 species together represent 66.6% of the density.

These figures show that like the closed forests, the open forests are both species rich and diverse.

Table 13. Relative frequency, density and dominance, importance and rank of the 20 most "important" species in open forests

Species Relative Relative Relative Importance frequency density dominance [%] Rank [%] Rank [%] Rank [*] Rank Pagsahingin-bulog 39.62 1 2.24 2 1.57 6 43.43 1 Canarium asperum Southern Bangkal / Hambabalud 30.19 2 2.49 1 1.70 4 34.38 2 Neonauclea formicaria Banai-banai 28.30 3 1.08 10 1.59 5 30.96 3 Radermachera pinnata Alagasi 26.42 4 1.76 3 1.03 13 29.20 4 Leucosyke capitellata Tuai 22.64 5 0.51 28 3.44 1 26.59 5 Bischofia javanica Coconut 22.64 5 0.68 20 2.53 2 25.85 6 Cocos nucifera Hawili 22.64 5 0.77 17 0.25 84 23.66 7 Ficus septica Batino 20.75 6 1.13 8 0.82 19 22.71 8 Alstonia macrophylla Bayok 18.87 8 0.37 37 0.70 26 19.94 9 Pterospermum diversifolium Binucao 18.87 8 0.71 18 0.33 59 19.91 10 Garcinia binucao Paguringon 16.98 8 0.97 13 0.91 15 18.86 11 Cratoxylum sumatranum Taluto 16.98 8 0.53 27 1.34 10 18.85 12 Pterocymbium tinctorium Tibig 16.98 8 1.08 10 0.70 27 18.76 13 Ficus nota Balinghasai 15.09 9 0.90 14 0.85 18 16.84 14 Buchanania arborescens Balete 15.09 9 0.27 43 0.89 17 16.25 15 Ficus balete Bitanghol 15.09 9 0.69 19 0.40 47 16.19 16 Calophyllum blancoi Nato 15.09 9 0.40 38 0.66 30 16.15 17 Palaquium luzoniense Alagau 15.09 9 0.63 22 0.24 85 15.96 18 Premna odorata Bogo 15.09 9 0.40 36 0.23 88 15.72 19 Garuga floribunda Agoho 13.21 10 1.23 6 0.76 20 15.20 20 Casuarina equisetifolia

* sum of relative frequency, density and dominance

Figure 15. N/ha, G/ha, V/ha and AGB/ha by number of species in open forests

The relatively limited number of sampling units (33 in the closed forests, 53 in open forests) precludes a thorough comparison of the species diversity between closed and open forests. Besides, the remaining closed forests occur mainly at high elevation and have a different species composition than the forests at lower elevation. 6.2 Stand composition 6.2.1 Stand composition of closed forests Table 14 summarizes and Figure 16 illustrates the stand composition of the closed forests in terms of N/ha, G/ha, V/ha and AGB/ha, estimated on the basis of 33 sampling units.

The average G/ha, V/ha and AGB/ha are very low. One would have expected average stocks of more than 33 m²/ha, 200 m³/ha and 360 t. d.m./ha, respectively.

Dipterocarps account for only 10.4% of the merchantable volume. In terms of AGB and basal area, their share of the total stock is even less (5.7% and 4.8%, respectively). In terms of density, all Dipterocarps together represent only 2.2% of the total stock, which is less than each the four most abundant Non-Dipterocarps (Bayanti, Pakong buwaya, Alagasi and Dalunot) taken individually.

On average, Dipterocarps are bigger in size than Non-Dipterocarps, as revealed through the larger quadratic mean diameter (Dg), 23.4 cm compared to 15.8 cm.

The five most dominant Dipterocarps in terms of basal area are White Lauan, Bagtikan, Guijo, Red Lauan and Tiaong. Together, they represent 94% of the total Dipterocarp merchantable volume, but only around 10% of the total merchantable volume, all species combined.

The ten most dominant Non-Dipterocarps in terms of basal area, led by Tuai, followed by Banai-banai, Agoho, Bayanti, Kalulot, Pagsahingin-bulog, Gulob, Tibig and Balete (being stranglers, their size is

arguable) and Dalunot represent together around one fifth of G/ha (21.0%), and nearly one third of V/ha (31.8%) and AGB/ha (29.2%) of their group.

The palms encountered are mainly Alas-as (Pandanus luzonensis) and Sarawag (Pinanga insignis), to a lesser extent Bunga (Areca catechu) and Anahaw (Saribus rotundifolius).

Tree ferns (essentially Pakong buwaya [Cyathea contaminans]) are quite abundant.

Table 14. Stand composition of closed forests

Species group N/ha G/ha V/ha AGB/ha Species [/ha] [%] [m²/ha] [%] [m³/ha] [%] [t. d.m./ha] [%] Dipterocarps White Lauan 7.2 0.5 0.33 1.2 2.32 3.2 2.87 1.2 Bagtikan 4.3 0.3 0.29 1.0 2.15 2.9 2.92 1.2 Guijo 4.6 0.3 0.27 1.0 1.89 2.6 3.48 1.5 Red Lauan 7.7 0.6 0.19 0.7 0.51 0.7 1.42 0.6 Tiaong 4.1 0.3 0.08 0.3 0.29 0.4 0.94 0.4 Other Dipt. 2.6 0.2 0.16 0.6 0.46 0.6 1.91 0.8 Sub-Total Dipt. 30.6 2.2 1.32 4.8 7.63 10.4 13.54 5.7 Non-Dipterocarps Tuai 16.4 1.2 1.46 5.3 4.64 6.3 19.33 8.1 Banai-banai 11.3 0.8 0.87 3.1 5.06 6.9 9.76 4.1 Agoho 25.1 1.8 0.68 2.5 2.88 3.9 9.07 3.8 Bayanti 39.8 2.9 0.56 2.0 1.09 1.5 5.31 2.2 Kalulot 6.3 0.5 0.49 1.8 3.72 5.1 7.07 3.0 Pagsahingin-bulog 26.8 1.9 0.41 1.5 0.89 1.2 2.67 1.1 Gulob 6.8 0.5 0.34 1.2 1.14 1.6 3.19 1.3 Tibig 27.5 2.0 0.34 1.2 0.25 0.3 2.24 0.9 Balete 1.7 0.1 0.34 1.2 0.85 1.2 4.85 2.0 Dalunot 30.9 2.2 0.30 1.1 0.37 0.5 1.93 0.8 Other Non-Dipt. 1,084.3 77.8 19.16 69.3 44.81 61.1 160.13 66.9 Sub-Total Non-Dipt. 1,276.9 91.6 24.95 90.3 65.70 89.6 224.43 93.7 Palms 40.8 2.9 0.50 1.8 1.05 0.4 Rattan 1.0 0.1 0.00 0.0 Tree ferns 43.4 3.1 0.87 3.1 Bamboos 1.0 0.1 0.01 0.0 0.02 0.0 Total 1,393.6 100.0 27.64 100.0 73.33 100.0 239.40 100.0

N/ha [/ha]

G/ha [m²/ha]

V/ha [m³/ha]

AGB/ha [t d.m./ha]

Non- Palms Bamboos White Other Dipterocarps Bagtikan Guijo Red Lauan Tiaong Dipterocarps Rattan Tree ferns Lauan Dipterocarps

Figure 16. Stand composition of closed forests

6.2.2 Stand composition of open forests Table 15 summarizes and Figure 17 illustrates the stand composition of the open forests in terms of N/ha, G/ha, V/ha and AGB/ha, estimated on the basis of 53 sampling units.

Like in the closed forests, the average G/ha, V/ha and AGB/ha are quite low, particularly in terms of V/ha and AGB/ha.

Compared to the closed forests, Dipterocarps account to even a lesser share of the total stock, namely only 5.5% in terms of merchantable volume, 5.1% in terms of AGB, 4.3% in terms of basal area, and 1.7% in terms of density.

On average, the quadratic mean diameter of Dipterocarps (24.3 cm) is bigger than the Dg of Non-Dipterocarps (14.9 cm).

The five most dominant Dipterocarps in terms of basal area are White Lauan, Bagtikan, Guijo (the same species as in the closed forest), Gisok-Gisok and Almon. Together, they represent 100% of the total Dipterocarp merchantable volume, but only 5.5% of the total merchantable volume, all species combined.

The ten most dominant Non-Dipterocarps in terms of basal area, led like in the closed forests by Tuai, followed by Narra, Southern Bangkal / Hambabalud, Banai-banai, Pagsahingin-bulog, Dao, Igem (Dacrycarpus imbricatus), Taluto, Benguet pine and Alagasi, represent together somewhat more than one fourth of V/ha (27.5%), and nearly one fifth of G/ha (18.8%) and AGB/ha (18.9%) of their group.

The palms encountered are mainly coconuts, to a lesser extent Anahaw and Alas-as.

Like in the closed forests, there are tree ferns (essentially Pakong buwaya), though much less in terms of density and basal area.

Table 15. Stand composition of open forests

Species group N/ha G/ha V/ha AGB/ha Species [/ha] [%] [m²/ha] [%] [m³/ha] [%] [t. d.m./ha] [%] Dipterocarps White Lauan 2.1 0.2 0.22 1.0 1.53 2.8 2.19 1.2 Bagtikan 3.9 0.3 0.15 0.7 0.92 1.7 1.19 0.7 Guijo 3.0 0.3 0.08 0.4 0.43 0.8 0.91 0.5 Gisok-Gisok 4.8 0.4 0.02 0.1 0.00 0.0 0.11 0.1 Almon 0.2 0.0 0.01 0.0 0.08 0.1 0.06 0.0 Other Dipt. 5.6 0.5 0.42 2.0 0.00 0.0 4.51 2.6 Sub-Total Dipt. 19.6 1.7 0.91 4.3 2.95 5.5 8.98 5.1 Non-Dipterocarps Tuai 6.0 0.5 0.73 3.4 4.38 8.1 9.88 5.6 Narra 9.6 0.8 0.51 2.4 1.94 3.6 4.65 2.6 S. Bangkal / Hambabalud 29.1 2.5 0.36 1.7 0.73 1.4 2.61 1.5 Banai-Banai 12.6 1.1 0.34 1.6 1.02 1.9 2.37 1.3 Pagsahingin bulog 26.3 2.2 0.33 1.6 0.38 0.7 1.82 1.0 Dao 6.2 0.5 0.31 1.5 0.91 1.7 2.21 1.3 Igem 1.4 0.1 0.29 1.4 0.88 1.6 2.75 1.6 Taluto 6.2 0.5 0.29 1.4 2.56 4.8 1.41 0.8 Benguet Pine 1.8 0.2 0.24 1.1 1.11 2.1 2.18 1.2 Alagasi 20.6 1.8 0.22 1.0 0.12 0.2 1.39 0.8 Other Non-Dipt. 980.0 83.7 15.68 73.7 36.96 68.5 134.12 76.1 Sub-Total Non-Dipt. 1,099.8 93.9 19.30 90.7 50.99 94.5 165.39 93.9 Palms 37.5 3.2 0.91 4.3 1.73 1.0 Rattan 0.0 0.0 - 0.0 Tree ferns 6.0 0.5 0.12 0.6 Bamboos 8.4 0.7 0.04 0.2 0.12 0.1 Total 1,171.3 100.0 21.28 100.0 53.87 100.0 176.15 100.0

N/ha [/ha]

G/ha [m²/ha]

V/ha [m³/ha]

AGB/ha [t d.m./ha]

Non- Palms Bamboos White Other Dipterocarps Bagtikan Guijo Gisok-Gisok Almon Dipterocarps Rattan Tree ferns Lauan Dipterocarps

Figure 17. Stand composition of open forests

6.3 Stand structure 6.3.1 Stand structure of closed forests The stand structure of the closed forests is summarized hereafter in terms of the following: • Density (N/ha) by diameter class, summarized in Table 16 and illustrated in Figure 18; • Basal area (G/ha) by diameter class, summarized in Table 17 and illustrated in Figure 19; and • Above-ground biomass (AGB/ha) by diameter class, summarized in Table 18 and illustrated in Figure 21.

On average, the closed forests count per hectare 31 Dipterocarp trees, 1,277 Non-Dipterocarp trees, 41 palms, 1 rattan, 43 tree ferns, 1 bamboo and 80 standing dead wood. Both the density and proportion of Dipterocarps are very low.

As expected, N/ha by diameter class shows a typical inverse "J"-shaped distribution.

The rise of N/ha for trees with DBH or DAB ≥ 80 cm is due to a few quite large Tuai, Banai-banai, Kalulot, Ulayan (Oak), White Lauan and Balete (being stranglers, their size is arguable).

Table 16. Stand structure in terms of N/ha of closed forests

Species group Density by diameter class Species [5 - 20 cm] [20 - 40 cm] [40 - 60 cm] [60 - 80 cm] [80 cm - ] Total [/ha] [/ha] [/ha] [/ha] [/ha] [/ha] Dipterocarps Bagtikan 2.9 0.7 0.2 0.4 - 4.3 Guijo 2.9 1.0 0.5 0.2 - 4.6 Red Lauan 6.7 0.4 0.4 - - 7.7 Tiaong 3.8 - 0.2 - - 4.1 White Lauan 5.8 0.7 0.2 0.2 0.2 7.2 Other Dipterocarps 2.0 0.3 0.4 0.4 - 2.7 Total Dipterocarps 24.1 3.1 1.9 1.2 0.2 30.6 Non-Dipterocarps Alagasi 30.8 0.2 - - - 31.1 Banai-banai 8.7 1.7 0.2 0.2 0.4 11.3 S. Bangkal / Hambabalud 10.6 1.5 - - - 12.1 Pagsahingin-bulog 24.1 1.9 0.7 - - 26.8 Tibig 26.1 1.4 - - 0.0- 27.5 Tuai 9.7 3.6 1.7 0.5 1.0 16.4 Other Non-Dipterocarps 1,024.3 101.3 21.0 3.1 2.0 1,151.6 Total Non-Dipterocarps 1,134.3 111.6 23.6 3.8 3.4 1,276.8 Palms 37.6 3.1 - - - 40.8 Rattan 1.0 - - - - 1.0 Tree ferns 38.5 3.9 - 1.0 - 43.4 Bamboos 1.0 - - - - 1.2 Total 1,236.6 121.8 25.5 6.0 3.6 1,393.6 Standing dead wood 64.6 12.5 1.9 1.0 - 80.1

Figure 18. Stand structure in terms of N/ha of closed forests

On average, the basal area of the closed forests amounts to 27.6 m²/ha, not much (one would have expected more than 33 m²/ha), and even quite less than the 30.6 m²/ha (for trees with DBH or DAB ≥ 15 cm) observed 1987 to 1988 by the second National Forest Resources Inventory in 29 sampling units in "Old Growth Forests" of Regions VI and VII (DENR 1988).

The small proportion of Dipterocarps (4.8% against 39.1% observed 1987 to 1988) is both striking and alarming. This is a serious sign of forest degradation, when the most valuable and threatened species have decreased from around 39% to 5% in the Panay Mountain Range.

Table 17. Stand structure in terms of G/ha of closed forests

Species group Basal area by diameter class Species [5 - 20 cm] [20 - 40 cm] [40 - 60 cm] [60 - 80 cm] [80 cm - ] Total [m²/ha] [m²/ha] [m²/ha] [m²/ha] [m²/ha] [m²/ha] Dipterocarps Bagtikan 0.04 0.04 0.04 0.17 - 0.29 Guijo 0.04 0.06 0.07 0.10 - 0.27 Red Lauan 0.08 0.03 0.08 - - 0.19 Tiaong 0.03 0.00 0.06 - - 0.08 White Lauan 0.04 0.04 0.03 0.09 0.13 0.33 Other Dipterocarps 0.01 0.03 0.04 0.09 - 0.16 Total Dipterocarps 0.23 0.20 0.32 0.45 0.13 1.32 Non-Dipterocarps Alagasi 0.22 0.01 - - - 0.23 Banai-banai 0.10 0.12 0.03 0.09 0.53 0.87 S. Bangkal / Hambabalud 0.08 0.09 - - - 0.17 Pagsahingin-bulog 0.21 0.09 0.12 - - 0.41 Tibig 0.25 0.08 - - - 0.34 Tuai 0.09 0.22 0.30 0.16 0.69 1.46 Other Non-Dipterocarps 8.69 6.23 3.79 1.12 1.65 21.46 Total Non-Dipterocarps 9.64 6.84 4.24 1.37 2.87 24.94 Palms 0.30 0.20 - - - 0.50 Rattan ------Tree ferns 0.39 0.15 - 0.32 - 0.87 Bamboos 0.01 - - - - 0.01 Total 10.57 7.38 4.56 2.14 2.99 27.64 Standing dead wood 0.54 0.74 0.30 0.27 - 1.87

Figure 19. Stand structure in terms of G/ha of closed forests

On average, the above-ground biomass of the closed forests amounts to 239 t d.m./ha, which is not much (one would have expected more than 360 t d.m./ha), and actually even below the range from 280 t d.m./ha to 520 t d.m./ha referred to by IPCC as Tier 1 estimate for tropical rainforest of insular Asia.

Figure 20 shows that 94% of AGB/ha is composed of trees with DBH or DAB ≥ 10 cm.

Table 18. Stand structure in terms of AGB/ha of closed forests

Species group Above-ground biomass by diameter class Species [5 - 20 cm] [20 - 40 cm] [40 - 60 cm] [60 - 80 cm] [80 cm - ] Total [t d.m./ha] [t d.m./ha] [t d.m./ha] [t d.m./ha] [t d.m./ha] [t d.m./ha] Dipterocarps Bagtikan 0.17 0.32 0.42 2.00 - 2.92 Guijo 0.27 0.67 0.85 1.69 - 3.48 Red Lauan 0.42 0.23 0.77 - - 1.42 Tiaong 0.13 - 0.81 - - 0.94 White Lauan 0.16 0.29 0.25 0.86 1.31 2.87 Other Dipterocarps 0.07 0.26 0.52 1.08 - 1.91 Total Dipterocarps 1.22 1.77 3.62 5.63 1.31 13.54 Non-Dipterocarps Alagasi 1.19 0.08 - - - 1.26 Banai-banai 0.48 0.86 0.29 0.90 7.22 9.76 S. Bangkal / Hambabalud 0.43 0.84 - - - 1.28 Pagsahingin-bulog 0.99 0.60 1.08 - - 2.67 Tibig 1.52 0.72 - - - 2.24 Tuai 0.58 2.14 3.56 2.13 10.92 19.33 Other Non-Dipterocarps 50.47 55.19 41.94 13.81 26.85 186.25 Total Non-Dipterocarps 55.66 60.43 46.87 16.84 44.99 224.79 Palms 0.87 0.18 - - - 1.05 Bamboos 0.02 - - - - 0.02 Total 57.77 62.38 50.49 22.48 46.29 239.40

Figure 20. AGB/ha of closed forests by DBH / DAB threshold

Figure 21. Stand structure in terms of AGB/ha of closed forests

6.3.2 Stand structure of open forests The stand structure of the open forests is summarized hereafter in terms of the following: • Density (N/ha) by diameter class, summarized in Table 19 and illustrated in Figure 22; • Basal area (G/ha) by diameter class, summarized in Table 20 and illustrated in Figure 19; and • Above-ground biomass (AGB/ha) by diameter class, summarized in Table 21 and illustrated in Figure 21.

On average, the open forests count per hectare 17 Dipterocarp trees, 1,102 Non-Dipterocarp trees, 38 palms, 6 tree ferns, 8 bamboos and 57 standing dead wood. A t-test reveals that the density of trees (1,171 /ha) is not significantly lower than the density in the closed forests (1,394 /ha), even at a confidence level of only 90%.

The distribution of N/ha by diameter class follows a similar pattern as in the closed forests, though at a somewhat lower level. The rise of N/ha for trees with DBH or DAB ≥ 100 cm is due to a few quite large Tuai, Pahutan, White Lauan, Balukanag (Chisocheton cumingianus) and Kanapai (Ficus magnoliifolia; being stranglers, their size is arguable).

Table 19. Stand structure in terms of N/ha of open forests

Species group Density by diameter class Species [5 - 20 cm] [20 - 40 cm] [40 - 60 cm] [60 - 80 cm] [80 cm - ] Total [/ha] [/ha] [/ha] [/ha] [/ha] [/ha] Dipterocarps Almon - 0.2 - - - 0.2 Bagtikan 2.4 1.4 0.2 - - 3.9 Gisok-Gisok 4.8 - - - - 4.8 Guijo 2.4 0.3 0.3 - - 3.0 White Lauan 1.2 0.3 0.5 - - 2.1 Other Dipterocarps 1.8 0.8 0.3 0.4 0.2 3.4 Total Dipterocarps 12.6 3.0 1.3 0.4 0.2 17.4 Non-Dipterocarps Alagasi 19.2 1.4 - - - 20.6 Banai-banai 9.6 2.6 0.5 - - 12.6 S. Bangkal / Hambabalud 27.0 2.0 0.2 - - 29.1 Pagsahingin-bulog 24.6 1.7 - - - 26.3 Tibig 12.0 0.7 - - - 12.6 Tuai 3.0 1.4 1.0 0.5 0.3 6.0 Other Non-Dipterocarps 894.4 81.5 13.7 3.1 1.5 994.8 Total Non-Dipterocarps 989.8 91.3 15.4 3.6 1.8 1,102.0 Palms 28.2 9.4 - - - 37.5 Rattan ------Tree ferns 4.8 1.2 - - - 6.0 Bamboos 8.4 - - - - 8.4 Total 1,043.8 104.9 16.5 4.0 2.2 1,171.3 Standing dead wood 46.8 7.8 1.2 - 0.6 56.5

Figure 22. Stand structure in terms of N/ha of open forests

On average, the basal area of the open forests amounts to 21.3 m²/ha. This is significantly less than G/ha of the closed forests (27.6 m²/ha), as confirmed by a t-test at a confidence level of 90%. The observed basal area is also lower than the 22.6 m²/ha (for trees with DBH or DAB ≥ 15 cm) observed 1987 to 1988 by the second National Forest Resources Inventory in 148 sampling units in "Residual Forests" of Regions VI and VII (DENR 1988).

Table 20. Stand structure in terms of G/ha of open forests

Species group Basal area by diameter class Species [5 - 20 cm] [20 - 40 cm] [40 - 60 cm] [60 - 80 cm] [80 cm - ] Total [m²/ha] [m²/ha] [m²/ha] [m²/ha] [m²/ha] [m²/ha] Dipterocarps Almon - 0.01 - - - 0.01 Bagtikan 0.03 0.10 0.02 - - 0.15 Gisok-Gisok 0.02 - - - - 0.02 Guijo 0.02 0.02 0.04 - - 0.08 White Lauan 0.01 0.02 0.07 - 0.13 0.22 Other Dipterocarps 0.04 0.03 0.08 0.11 0.08 0.36 Total Dipterocarps 0.12 0.18 0.21 0.11 0.21 0.84 Non-Dipterocarps Alagasi 0.15 0.07 - - - 0.22 Banai-banai 0.09 0.16 0.09 - - 0.34 S. Bangkal / Hambabalud 0.22 0.11 0.04 - - 0.36 Pagsahingin-bulog 0.24 0.09 - - - 0.33 Tibig 0.12 0.03 - - - 0.15 Tuai 0.03 0.10 0.16 0.19 0.27 0.73 Other Non-Dipterocarps 7.08 5.02 2.43 1.08 1.62 17.24 Total Non-Dipterocarps 7.93 5.58 2.72 1.27 1.89 19.37 Palms 0.29 0.63 - - - 0.91 Rattan ------Tree ferns 0.05 0.06 - - - 0.12 Bamboos 0.04 - - - - 0.04 Total 8.43 6.45 2.94 1.38 2.09 21.28 Standing dead wood 0.47 0.51 0.19 0.00- 0.60 1.77

Figure 23. Stand structure in terms of G/ha of open forests

On average, the above-ground biomass of the open forests amounts to 176 t d.m./ha, which is significantly less than AGB/ha of the closed forests (239 t d.m./ha), as confirmed by a t-test at a confidence level of 90%.

Figure 24 shows that quite similarly to the closed forests, 93% of AGB/ha in the open forests is composed of trees with DBH or DAB ≥ 10 cm.

Table 21. Stand structure in terms of AGB/ha of open forests

Species group Above-ground biomass by diameter class Species [5 - 20 cm] [20 - 40 cm] [40 - 60 cm] [60 - 80 cm] [80 cm - ] Total [t d.m./ha] [t d.m./ha] [t d.m./ha] [t d.m./ha] [t d.m./ha] [t d.m./ha] Dipterocarps Almon - 0.06 - - - 0.06 Bagtikan 0.16 0.83 0.19 - - 1.19 Gisok-Gisok 0.11 - - - - 0.11 Guijo 0.18 0.16 0.58 - - 0.91 White Lauan 0.02 0.12 0.61 - 1.44 2.19 Other Dipterocarps 0.28 0.38 0.89 1.38 1.15 4.06 Total Dipterocarps 0.75 1.55 2.27 1.38 2.59 8.52 Non-Dipterocarps Alagasi 0.82 0.57 - - - 1.39 Banai-banai 0.43 1.14 0.80 - - 2.37 S. Bangkal / Hambabalud 1.24 0.89 0.48 - - 2.61 Pagsahingin-bulog 1.19 0.64 - - - 1.82 Tibig 0.70 0.22 - - - 0.93 Tuai 0.14 1.01 1.84 2.52 4.37 9.88 Other Non-Dipterocarps 39.45 42.56 25.86 13.46 24.31 146.85 Total Non-Dipterocarps 43.97 47.03 28.98 15.98 28.68 165.85 Palms 0.86 0.87 - - - 1.73 Bamboos 0.12 - - - - 0.21 Total 45.70 49.45 31.23 17.36 32.48 176.22

Figure 24. AGB/ha of open forests by DBH / DAB threshold

Figure 25. Stand structure in terms of AGB/ha of open forests

6.4 Timber stocks 6.4.1 Timber stocks of closed forests Table 22 summarizes and Figure 26 illustrates the distribution of the merchantable volume in the closed forests by diameter class and main species.

On average, the merchantable volume in the closed forests amounts to as little as 73.3 m³/ha. This is way below what one would expect (at least 200 m³/ha), and also dramatically less than the 183.2 m³/ha (for trees with DBH or DAB ≥ 15 cm) observed 1987 to 1988 by the second National Forest Resources Inventory in 29 sampling units in "Old Growth Forests" of Regions VI and VII (DENR 1988). Also, this is a serious sign of forest degradation within the past 28 years.

Both in absolute and relative terms, the proportion of Dipterocarps shows a tremendous decline over time, from then 89.4 m³/ha (equivalent to 48.8% of the total stock) to now 7.6 m³/ha (equivalent to 10.4% of the total stock).

Table 22. Merchantable volume in closed forests

Species group Merchantable volume by diameter class Species [5 - 40 cm] [40 - 60 cm] [60 - 80 cm] [80 cm - ] Total [m³/ha] [m³/ha] [m³/ha] [m³/ha] [m³/ha] Dipterocarps Bagtikan 0.12 0.38 1.65 - 2.15 Guijo 0.27 0.49 1.13 - 1.89 Red Lauan 0.15 0.36 - - 0.51 Tiaong 0.00 0.29 - - 0.29 White Lauan 0.18 0.20 0.99 0.95 2.32 Other Dipterocarps 0.20 0.27 - - 0.47 Total Dipterocarps 0.92 1.99 3.77 0.95 7.63 Non-Dipterocarps Alagasi 0.01 - - - 0.01 Banai-banai 0.40 0.11 0.42 4.13 5.06 S. Bangkal / Hambabalud 0.37 - - - 0.37 Pagsahingin-bulog 0.32 0.58 - - 0.89 Tibig 0.25 - - - 0.25 Tuai 0.72 1.02 0.75 2.14 4.64 Other Non-Dipterocarps 23.99 16.82 5.09 8.58 54.48 Total Non-Dipterocarps 26.06 18.53 6.26 14.85 65.70 Total 26.98 20.52 10.03 15.80 73.33

Figure 26. Merchantable volume in closed forests

6.4.2 Timber stocks of open forests Table 23 summarizes and Figure 27 illustrates the distribution of the merchantable volume in the open forests by diameter class and main species.

On average, the merchantable volume in the open forests amounts to 53.9 m³/ha, which, according to a t-test, is not significantly less than the merchantable volume in the closed forests (73.2 m³/ha), even at a confidence level of only 90%.

Like for the closed forests, this is way below expectations, and dramatically less than the 125.7 m³/ha (for trees with DBH or DAB ≥ 15 cm) observed 1987 to 1988 by the second National Forest Resources Inventory in 148 sampling units in "Residual Forests" of Regions VI and VII (DENR 1988).

Both in absolute and relative terms, the proportion of Dipterocarps shows a tremendous decline over time, from then 59.7 m³/ha (equivalent to 47.5% of the total stock) to now almost immaterial 3.0 m³/ha (equivalent to 5.5 % of the total stock).

Table 23. Merchantable volume in open forests

Species group Merchantable volume by diameter class Species [5 - 40 cm] [40 - 60 cm] [60 - 80 cm] [80 cm - ] Total [m³/ha] [m³/ha] [m³/ha] [m³/ha] [m³/ha] Dipterocarps Almon 0.08 - - - 0.08 Bagtikan 0.80 0.12 - - 0.92 Gisok-Gisok - - - - - Guijo 0.10 0.33 - - 0.43 White Lauan 0.11 0.53 - 0.88 1.53 Other Dipterocarps - 0.01 - - - Total Dipterocarps 1.08 0.99 - 0.88 2.95 Non-Dipterocarps Alagasi 0.12 - - - 0.12 Banai-banai 0.72 0.30 - - 1.02 S. Bangkal / Hambabalud 0.44 0.29 - - 0.73 Pagsahingin-bulog 0.38 - - - 0.38 Tibig 0.06 - - - 0.07 Tuai 0.36 0.56 0.92 2.55 4.38 Other Non-Dipterocarps 17.85 9.65 5.97 10.84 44.29 Total Non-Dipterocarps 19.93 10.80 6.89 13.39 50.99 Total 21.00 11.79 6.89 14.27 53.94

Figure 27. Merchantable volume in open forests

6.5 Carbon stocks 6.5.1 Carbon stocks of closed forests Table 24 summarizes and Figure 28 illustrates the carbon stocks of closed forests.

On average, the closed forests feature a living biomass of 328 t d.m./ha, dead organic matter of 24.8 t d.m./ha, composed of (i) 18.2 t d.m./ha of dead wood and (ii) 6.6 t d.m./ha of litter, plus 56.3 t C/ha of soil organic matter. The bulk of the carbon stock is in the above-ground biomass (51.2%), which is essentially composed of Non-Dipterocarps (94.3%).

Extrapolated to the 47,882 ha of closed forests in the selected project sites, the forest carbon stock amounts to 10.516 million t C.

Table 24. Carbon stocks of closed forests

Carbon pool Biomass/ha by diameter class Carbon/ha [5 cm - 40 cm] [40 cm - ] Total Total [t d.m./ha] [t d.m./ha] [t d.m./ha] [t C/ha] [%] Living biomass Above-ground biomass 120.15 119.25 239.40 112.52 51.2 Below-ground biomass 44.46 44.12 88.58 41.63 19.0 Total living biomass 164.61 163.37 327.98 154.15 70.2 Dead organic matter Standing dead wood 12.50 4.62 2.1 Lying dead wood 5.74 2.12 1.0 Litter 6.62 2.45 1.1 Total dead organic matter 24.85 9.20 4.2 Soil organic matter 56.27 25.6 Total 219.62 100.0

6.5.2 Carbon stocks of open forests Table 25 summarizes and Figure 29 illustrates the carbon stocks of open forests.

On average, the open forests feature a living biomass of 241 t d.m./ha, dead organic matter of 25.5 t d.m./ha, composed of (i) 19.0 t d.m./ha of dead wood and (ii) 6.5 t d.m./ha of litter, plus 52.3 t C/ha of soil organic matter. Almost half of the carbon stock is in the above-ground biomass (47.3%), which is predominantly composed of Non-Dipterocarps (96.4%).

Extrapolated to the 69,742 ha of open forests in the selected project sites, the forest carbon stock amounts to 12.215 million t C.

C/ha [t C/ha] C/ha [t C/ha]

Figure 28. Carbon stocks of closed forests

Table 25. Carbon stocks of open forests

Carbon pool Biomass/ha by diameter class Carbon/ha [5 cm - 40 cm] [40 cm - ] Total Total [t d.m./ha] [t d.m./ha] [t d.m./ha] [t C/ha] [%] Living biomass Above-ground biomass 95.15 81.07 176.22 82.83 47.3 Below-ground biomass 35.21 30.00 65.20 30.65 17.5 Total living biomass 130.36 111.07 241.43 113.47 64.8 Dead organic matter Standing dead wood 15.81 5.85 3.3 Lying dead wood 3.20 1.18 0.7 Litter 6.45 2.39 1.4 Total dead organic matter 25.46 9.42 5.4 Soil organic matter 52.25 29.8 Total 175.14 100.0

C/ha [t C/ha] C/ha [t C/ha]

Figure 29. Carbon stocks of open forests

6.5.3 Carbon stocks of mangroves Mangroves have not been inventoried on the ground. Since they represent only 0.33% of the total forest area in the project sites on Panay Island, they are not a key stratum. Table 26 provides nevertheless a tier 1 estimate of their carbon stocks, illustrated in Figure 30, estimated using IPCC default data.

According to these tier 1 estimates, the mangroves feature a living biomass of 286 t d.m./ha, dead organic matter of 11.4 t C/ha, composed of (i) 10.7 t C/ha of dead wood and (ii) 0.7 t C/ha of litter, plus considerable 386.0 t C of soil organic matter. The bulk of the carbon stock is in the soil organic matter (73.3%).

Extrapolated to the 389 ha of mangroves in the selected project sites near the Panay Mountain Range, the forest carbon stock amounts to 204,800 t C.

Table 26. Carbon stocks of mangroves

Carbon pool Biomass/ha Carbon/ha Total Total [t d.m./ha] [t C/ha] [%] Living biomass Above-ground biomass 192.00 86.59 16.5 Below-ground biomass 94.08 42.43 8.0 Total living biomass 286.08 129.02 24.5 Dead organic matter Dead wood 10.70 2.0 Litter 0.70 0.1 Total dead organic matter 11.40 2.2 Soil organic matter 386.00 73.3 Total 526.42 100.0

C/ha [t C/ha]

Figure 30. Carbon stocks of mangroves

7. Uncertainty of the estimates The estimates of all variables of interest, such as N/ha, G/ha, V/ha, AGB/ha, DOM/ha, SOM/ha and C/ha, to cite the most important ones that are summarily presented in Chapter 6, are affected with uncertainties. An approach for the reporting of these uncertainties is presented hereafter, analyzing successively the following five main sources: • Statistical sampling error (see Chapter 7.1). • Representativeness of the sampling network (see Chapter 7.2). • Measurements errors (see Chapter 7.3). • Data entry errors (see Chapter 7.4). • Estimation design uncertainties (see Chapter 7.5).

Chapter 7.6 combines the different sources of uncertainty for the estimates of V/ha and AGB/ha to summarize the overall error budget. 7.1 Statistical sampling error The detailed statistical parameters of the FRA estimates (in terms of number of sampling units, arithmetic mean, variance, standard deviation, coefficient of variation, standard error of the mean and margin of error at confidence levels of 90%, 95% and 99%, respectively) are provided in Annex 6 (closed forests) and Annex 7 (open forests), as computed and printed to PDF by the FRA database system application (see Chapter 5.1).

Table 27 summarizes the statistical sampling error in terms of the margin of error (E%) at a confidence level of 90% for the main variables of interest.

Table 27. Statistical sampling errors of the main variables of interest in closed and open forests

Variable Closed forests Open forests Based on 33 sampling units Based on 53 sampling units Mean Margin of error* Mean Margin of error* N/ha [/ha] 1,393.60 ± 17.11 % 1,171.29 ± 13.45 % G/ha [m²/ha] 27.64 ± 14.89 % 21.28 ± 13.62 % V/ha [m³/ha] 73.33 ± 26.00 % 53.94 ± 30.41 % AGB/ha [t. d.m./ha] 239.40 ± 16.90 % 176.22 ± 20.67 % BGB/ha [t. d.m./ha] 88.58 ± 16.90 % 65.20 ± 20.67 % LB/ha [t C/ha] 154.06 ± 16.93 % 113.47 ± 20.67 % SDW/ha [t C/ha] 4.62 ± 42.92 % 5.85 ± 56.16 % LDW/ha [t C/ha] 2.12 ± 40.51 % 1.18 ± 60.21 % LI/ha [t C/ha] 2.45 ± 16.56 % 2.39 ± 11.11 % DOM/ha [t C/ha] 9.20 ± 23.94 % 9.42 ± 35.80 % SOM/ha [t C/ha] 56.27 ± 4.05 % 52.25 ± 3.76 % Total C/ha [t C/ha] 219.62 ± 11.95 % 175.14 ± 13.58 %

* 95% confidence level

As expected, the coefficients of variation (s%) are higher in open forests than in closed forests (for AGB/ha for instance 89.9% compared to 57.3%).

The margin of error of the SOM/ha estimates appears to be very low. This is due to the fact that following the tier 1 estimate, only two soil types and two climate regions were found in the closed and open forests of the selected project sites in the Panay Mountain Range: high activity clays (HAC) and low activity clays (LAC) in tropical wet and LAC in tropical montane climate, respectively, see Chapter 3.1.3 with three corresponding SOM/ha stocks: 44 t C/ha (tropical wet HAC), 60 t C/ha (tropical wet LAC) and 63 t C/ha (tropical montane LAC). Hence, there is limited variation. 7.2 Representativeness of the sampling network The design of the sampling network (see Chapter 3.5) has been made in accordance with the statistical theory that the sampling units shall well present the overall population. The 200 sampling units initially selected were distributed over the whole Panay Mountain Range, equally over closed and open forests. Because of operational difficulties (remoteness and limited accessibility of the sampling units, steep and high mountains, unfavorable weather conditions, peace and order situation, etc.) it was only possible to record 86 of these sampling units within the available time and budget frame. Most of the measured sampling units were located in open forests, since the closed forest sampling units were often on steep slopes and elevations above 1,500 m. So the closed forests were slightly under-represented in the sample. It cannot be excluded that the failure to measure all allocated sampling units affects the representativeness of the sampling network. It is not possible to calculate however, if this sample selection had an effect in addition to the statistical sampling error in Table 27. An uncertainty of an order of magnitude of 10 % may conservatively be assumed. 7.3 Measurement errors The impact of the measurement errors has been evaluated through the re-measurement of 5% of the sampling units (see Chapter 4.5). Due to the small number of sampling units compared, the deviations must however be interpreted cautiously. While the estimates of N/ha, G/ha and AGB/ha are affected by quite limited uncertainties related to measurement errors (in terms of the RSMD), those of V/ha and SDW/ha are affected by more considerable uncertainties. If more sampling units would have been re- measured, the measurement errors would likely have turned out to be of the same order of magnitude as the statistical sampling errors. 7.4 Data entry errors The effect of the data entry errors has been evaluated through the comparison of the original field data and the stored data of 10% of the sampling units (see Chapter 5.2). The estimates of the variables of interest are affected by limited uncertainties often not exceeding 8% (in terms of the RMSD) related to data entry errors. 7.5 Estimation design uncertainties Except for N/ha and G/ha, where no allometric models, volume equations, wood specific gravities nor conversion and/or extrapolation factors are used, the estimates of all other variables of interest are affected by uncertainties due to the lack of fit of the estimation design (see Chapter 3.6) used.

The uncertainty arising from the use of the regional volume equations for Dipterocarps and Non- Dipterocarps (see Chapter 3.6.2) for the estimation of V/ha is not documented. It may conservatively be estimated to be of an order of magnitude of 15%.

According to the authors, the uncertainty arising from the use of the allometric equation developed by Chave et al. (2014) (see Chapter 3.6.3.2) for the estimation of AGB/ha is of the order of magnitude of 10%.

The uncertainties of the other metrics used to estimate BGB/ha (the root to shoot ratio, see Chapter 3.6.7), SDW/ha (the biomass conversion and expansion factor [BCEFs], see Chapter 3.6.8), LDW/ha (see Chapter 3.6.9), LI/ha (see Chapter 3.6.10), and to convert the biomass to carbon equivalent (carbon fraction [CF] of dry matter, see Chapter 3.6.11) are difficult to evaluate. 7.6 Overall error budget Table 28 and Table 29 show the overall error budget of the estimates of V/ha and AGB/ha, respectively.

The largest uncertainties pertain to the statistical sampling error, followed by measurement errors when height measurements are involved (for the estimation of V/ha) and estimation design uncertainties. The statistical sampling error can be reduced by augmenting the number of sampling units. However, one has to consider that to halve the statistical sampling error, four times more sampling units must be measured, since the sampling error is inversely proportional to the square root of the number of sampling units.

Table 28. Overall error budget for V/ha

Source of uncertainty Stratum Uncertainty Statistical sampling error Closed Forests ± 26.0 % Open Forests ± 30.4 % Representativeness of the sampling network Closed & Open Forests ± 10.0 % Measurement errors Closed & Open Forests ± 42.9 % Data entry errors Closed & Open Forests ± 8.3 % Estimation design uncertainties Closed & Open Forests ± 15.00 % Figures in italic are based on an insufficient number of observations and are not reliable * 90% confidence level

Table 29. Overall error budget for AGB/ha

Source of uncertainty Stratum Uncertainty Statistical sampling error Closed Forests ± 16.9 % Open Forests ± 20.7 % Representativeness of the sampling network Closed & Open Forests ± 10.0 % Measurement errors Closed & Open Forests ± 13.2 % Data entry errors Closed & Open Forests ± 4.2 % Estimation design uncertainties Closed & Open Forests ± 10.00 % Figures in italic are based on an insufficient number of observations and are not reliable * 90% confidence level

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Lasco RD, MacDicken KG, Pulhin FB, Guillermo IQ, Sales RF, Cruz RVO (2006): Carbon stocks assessment of a selectively logged Dipterocarp forest and wood processing mill in the Philippines. Journal of Tropical Forest Science 18 (4): 201 - 221 https://www.jstor.org/stable/43594677?seq=1#page_scan_tab_contents. Accessed 20 Feb 2017 Lennertz R, Fiel R, Megraso CP (2014): Manual for the forest resources assessments. National REDD+ System Philippines Project, Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH, Metro Manila, Philippines http://forestry.denr.gov.ph/redd-plus-philippines/publications-pdf/unpublished/FRA_Field_Manual.pdf. Mandallaz D (2008): Sampling techniques for forest inventories. Chapman & Hall/CRC, Boca Raton, Florida, USA Reyes G, Brown S, Chapman J, Lugo AE (1992): Wood densities of tropical tree species. United States Department of Agriculture, Forest Service, Southern Forest Experiment Station, New Orleans, Louisiana, USA http://www.fs.fed.us/global/iitf/pubs/gtr_so088_1992.pdf. Accessed 20 Feb 2017 Schade J, Ludwig R (2013): Forest carbon baseline study in Leyte. Climate-relevant Modernization of Forest Policy and Piloting of REDD Project, Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH, Metro Manila, Philippines http://faspselib.denr.gov.ph/sites/default/files//Publication%20Files/Forest%20Carbon%20Baseline%20Stu dy%20in%20Leyte.pdf. Accessed 20 Feb 2017 Schreuder HT, Richard E, Ramirez-Maldonadi H (2004): Statistical techniques for sampling and monitoring natural resources. United States Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Colins, Colorado, USA https://www.fs.fed.us/rm/pubs/rmrs_gtr126.pdf. Accessed 20 Feb 2017 Thiele T, Mussong M, Mateboto J (2010): Monitoring, assessment and reporting for sustainable management in Pacific Island Countries manual. http://theredddesk.org/sites/default/files/resources/pdf/MAR- SFM%20in%20Pacific%20Island%20Countries%20-%20Manual_1.pdf. Accessed 20 Feb 2017 Zanne AE, Lopez-Gonzalez G, Coomes DA, Ilic J, Jansen S, Lewis SL, Miller RB, Swenson NG, Wiemann MC, Chave J (2009): Global wood density database. Dryad, Durham, North Carolina, USA http://datadryad.org/handle/10255/dryad.235. Accessed 20 Feb 2017 Zemek OJ (2009): Biomass and carbon stocks inventory of perennial vegetation in the Chieng Khoi watershed, northwest Viet Nam. M.Sc. thesis, University of Hohenheim, Stuttgart, Germany. https://www.uni-hohenheim.de/sfb564/public/c4_files/zemek_msc.pdf. Accessed 20 Feb 2017 Zöhrer F (1980): Forstinventur: Ein Leitfaden für Studium und Praxis. Parey, Hamburg, Berlin, Germany

Appendix 1: Field data forms Forest and Climate Protection (ForClim) Project - Forest Resources Assessment

Sample Point No. Date 2 0

Team Leader

Region Assistant

Province Helper 1

City / Municipality Helper 2

Barangay Helper 3

Measurements / observations at the Sample Point

Target coordinates Zone North m E m N

Actual coordinates Zone North m E m N

Elevation m Slope % Slope orientation °

Terrain Land classification

01 = Plateau 02 = Summit / crest 03 = Upper slope 04 = Middle slope 05 = Lower slope 06 = Bench / terrace 1 = Forest land 07 = Valley 08 = Plain 09 = Narrow depression 10 = Water course 11 = Dunes 2 = Alienable & Disposable land

Observations within a radius of 25 m horizontal distance around the Sample Point

Land cover Forest type Tree crown cover

01 = Forest 02 = Marshland / swamp 03 = Fallow 01 = Dipterocarp old growth forest 02 = Dipterocarp residual forest 1 = non-forest (tree crown cover <= 10 %) 04 = Shrubs 05 = Wooded grassland 03 = Mossy forest 04 = Submarginal forest 2 = open forest (10 % < tree crown cover <= 40 %) 06 = Grassland 07 = Annual crop 05 = Closed pine forest 06 = Open pine forest 3 = closed forest (tree crown cover > 40%) 08 = Perennial crop 09 = Open / barren land 07 = Mangrove old growth forest 08 = Mangrove reproduction forest 10 = Built-up area 11 = Fishpond 12 = Inland water 09 = Native tree plantation 10 = Other plantation forest

Sketch map of the approach to the Sample Point / Remarks

Forest and Climate Protection (ForClim) Project - Forest Resources Assessment

Sample Point No. Date 2 0

Plot re-located Page Team Leader

Region Assistant

Province Helper 1

City / Municipality Helper 2

Barangay Helper 3

Measurement / observations at the Plot Center

Actual coordinates Zone North m E m N

Elevation m Slope % Slope orientation °

Terrain Land classification

Observations within a radius of 10 m horizontal distance around the Plot Center

Land cover Forest type Tree crown cover

Observations within a radius of 5 m horizontal distance around the Plot Center

Plant diversity (number of distinct plant species)

Grass, herbs, mosses < 50 cm Plants 50 cm - 130 cm Plants 130 cm - 200 cm

Plants 2.0 - 4.0 m Plants 4.0 m - 10.0 m Plants > 10.0 m Ground coverage classes: 0 = none 1 = coverage <= 10 % 2 = 10 % < Coverage <= 50 % 3 = coverage > 50 %

Assessment of litter within a radius of 5 m horizontal distance around the Plot Center

Ground coverage % Average depth cm

Inventory of lying dead wood with D >= 5.0 cm* # Mid-Diameter Length # Mid-Diameter Length # Mid-Diameter Length [cm] [m] [cm] [m] [cm] [m] ...... Continuation on page * within a radius of 5 m horizontal distance around the Plot Center

Forest and Climate Protection (ForClim) Project - Forest Resources Assessment

Sample Point No. Date 2 0

Plot re-located Page Team Leader

Assistant

Inventory of live trees, bamboos, palms and standing dead wood with Dref >= 5.0 cm* # Species Azimuth Hor. Distance DBH / DAB M. Height**

Name Code [°] [m] [cm] [m] Live Dead . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ . . . □ □ Continuation on page

* live trees, bamboos, palms, rattan and tree ferns with 5.0 cm <= Dref < 20.0 cm within a radius of 5 m horizontal distance around the Plot Center * live trees, bamboos, palms, rattan and tree ferns with Dref >= 20.0 cm within a radius of 10 m horizontal distance around the Plot Center * standing dead wood within a radius of 5 m horizontal distance around the Plot Center ** merchantable height for all standing dead wood with Dref >= 5.0 cm and for live trees with Dref >= 20.0 cm

Annex 1: Coordinates of inventoried sampling units

Sampling UTM Coordinates WGS 84 Geographic Coordinates Unit No. Zone East North Longitude Latitude [m] [m] [°] [°] AKLM0005 51 422,000 1,247,000 122.2853 11.2797 AKLM0010 51 422,000 1,249,000 122.2853 11.2978 AKLM0144 51 426,000 1,271,000 122.3215 11.4968 AKLM0145 51 426,000 1,272,000 122.3214 11.5059 AKLM0183 51 415,000 1,303,000 122.2198 11.7859 AKLM0312 51 418,000 1,257,000 122.2485 11.3700 AKLM0385 51 406,000 1,265,000 122.1383 11.4421 AKLM0393 51 405,000 1,266,000 122.1291 11.4511 AKLM0395 51 407,000 1,266,000 122.1474 11.4511 AKLM0400 51 420,000 1,248,000 122.2670 11.2887 AKLM0501 51 418,000 1,255,000 122.2485 11.3519 AKLM0502 51 420,000 1,255,000 122.2668 11.3520 AKLM0513 51 420,000 1,256,000 122.2668 11.3610 AKLM0589 51 426,000 1,270,000 122.3215 11.4878 AKLM0606 51 427,000 1,272,000 122.3306 11.5059 AKLM0613 51 426,000 1,273,000 122.3214 11.5149 AKLM0693 51 417,000 1,295,000 122.2384 11.7136 AKLM0694 51 418,000 1,295,000 122.2475 11.7137 AKLM0732 51 416,000 1,302,000 122.2290 11.7769 AKLM0763 51 415,000 1,304,000 122.2198 11.7950 AKLM0774 51 415,000 1,305,000 122.2198 11.8040 AKLM0932 51 413,000 1,277,000 122.2021 11.5508 AKLM1006 51 412,000 1,276,000 122.1930 11.5417 AKLM1045 51 417,000 1,296,000 122.2383 11.7227 AKLM1046 51 418,000 1,296,000 122.2475 11.7227 AKLM1049 51 418,000 1,297,000 122.2475 11.7317 AKLM9920 51 415,000 1,275,000 122.2205 11.5327 ANTM0000 51 412,000 1,196,000 122.1950 10.8183 ANTM0001 51 412,000 1,197,000 122.1950 10.8273 ANTM0002 51 413,000 1,199,000 122.2041 10.8454 ANTM0004 51 414,000 1,200,000 122.2132 10.8545 ANTM0005 51 414,000 1,201,000 122.2132 10.8635 ANTM0036 51 408,000 1,194,000 122.1584 10.8001 ANTM0038 51 407,000 1,195,000 122.1493 10.8091 ANTM0042 51 408,000 1,196,000 122.1584 10.8182 ANTM0044 51 411,000 1,197,000 122.1858 10.8273 ANTM0046 51 413,000 1,198,000 122.2041 10.8364 ANTM0047 51 412,000 1,199,000 122.1949 10.8454 ANTM0048 51 412,000 1,200,000 122.1949 10.8544 ANTM0049 51 413,000 1,200,000 122.2040 10.8545 ANTM0184 51 421,000 1,223,000 122.2767 11.0626 ANTM0202 51 421,000 1,226,000 122.2766 11.0898 ANTM0210 51 420,000 1,227,000 122.2675 11.0988

Sampling UTM Coordinates WGS 84 Geographic Coordinates Unit No. Zone East North Longitude Latitude [m] [m] [°] [°] ANTM0224 51 422,000 1,228,000 122.2858 11.1079 ANTM0290 51 406,000 1,256,000 122.1385 11.3607 ANTM0299 51 405,000 1,257,000 122.1293 11.3697 ANTM0300 51 460,000 1,257,000 122.6334 11.3708 ANTM0315 51 405,000 1,258,000 122.1293 11.3787 ANTM0329 51 406,000 1,259,000 122.1384 11.3878 ANTM0330 51 407,000 1,259,000 122.1476 11.3878 ANTM0360 51 404,000 1,262,000 122.1200 11.4149 ANTM0392 51 404,000 1,266,000 122.1199 11.4511 ANTM0403 51 405,000 1,267,000 122.1290 11.4601 ANTM0410 51 402,000 1,268,000 122.1015 11.4691 ANTM0411 51 403,000 1,268,000 122.1107 11.4691 ANTM0412 51 405,000 1,268,000 122.1290 11.4692 ANTM0413 51 401,000 1,249,000 122.0929 11.2972 ANTM0417 51 403,000 1,269,000 122.1106 11.4782 ANTM0418 51 404,000 1,269,000 122.1198 11.4782 ANTM0426 51 403,000 1,271,000 122.1106 11.4962 ANTM0427 51 401,000 1,251,000 122.0928 11.3153 ANTM0429 51 401,000 1,250,000 122.0929 11.3063 ANTM0430 51 404,000 1,272,000 122.1197 11.5053 ANTM0431 51 405,000 1,272,000 122.1289 11.5053 ANTM0432 51 406,000 1,272,000 122.1381 11.5054 ANTM0437 51 406,000 1,273,000 122.1380 11.5144 ANTM0438 51 407,000 1,273,000 122.1472 11.5144 ANTM0439 51 402,000 1,251,000 122.1020 11.3154 ANTM0442 51 406,000 1,274,000 122.1380 11.5235 ANTM0455 51 400,000 1,252,000 122.0836 11.3243 ANTM0456 51 401,000 1,252,000 122.0928 11.3244 ANTM0491 51 405,000 1,287,000 122.1285 11.6410 ANTM0514 51 404,000 1,257,000 122.1201 11.3697 ANTM0522 51 405,000 1,260,000 122.1292 11.3968 ANTM0666 51 405,000 1,288,000 122.1285 11.6500 ANTM0670 51 405,000 1,289,000 122.1284 11.6591 ANTM0712 51 398,000 1,300,000 122.0639 11.7583 ANTM0717 51 398,000 1,302,000 122.0638 11.7764 ANTM0725 51 398,000 1,302,000 122.0638 11.7764 ANTM0726 51 399,000 1,302,000 122.0730 11.7764 ANTM0727 51 400,000 1,302,000 122.0822 11.7765 ILIM0060 51 421,000 1,202,000 122.2772 10.8727 ILIM0064 51 420,000 1,203,000 122.2680 10.8817 ILIM0065 51 421,000 1,203,000 122.2772 10.8818 ILIM0068 51 420,000 1,204,000 122.2680 10.8908 ILIM0069 51 421,000 1,204,000 122.2771 10.8908 ILIM0074 51 422,000 1,205,000 122.2863 10.8999 ILIM0078 51 422,000 1,206,000 122.2862 10.9089

Annex 2: List of recorded species by common name

Common name Scientific name Family Gravity* [gr /cm³] Aglaia tomentosa Aglaia tomentosa Teijsm. & Binn. Meliaceae 0.68 Agoho Casuarina equisetifolia L. Casuarinaceae 0.80 Agoho del Monte Gymnostoma rumphianum (Miq.) L.A.S. Casuarinaceae 0.86 Johnson Agus-us Paratrophis philippinensis Fern. - Vill. Moraceae 0.54 Akleng-parang Albizia procera (Roxb.) Benth. Leguminosae 0.51 Alagasi Leucosyke capitellata Wedd. Urticaceae Alagau Premna odorata Blanco Lamiaceae Alahan Guioa koelreuteria (Blanco) Merr. Sapindaceae Alas-as Pandanus luzonensis Merr. Pandanaceae Alilaua Oreocnide trinervis (Wedd.) Miq. Urticaceae Alim Melanolepis multiglandulosa (Reinw. ex Euphorbiaceae 0.34 Blume) Rchb. & Zoll. Almaciga Agathis philippinensis Warb. Araucariaceae 0.45 Almon Shorea almon Foxw. Dipterocarpaceae 0.39 Alupag Dimocarpus longan subsp. malesianus Leenh. Sapindaceae 0.70 Amamali Leea aculeata Blume ex Spreng Vitaceae Amugis Koordersiodendron pinnatum Merr. Anacardiaceae 0.61 Amuyon Goniothalamus amuyon (Blanco) Merr. Annonaceae Anabiong Trema orientalis (L.) Blume Cannabaceae 0.33 Anagap Archidendron scutiferum (Blanco) I.C. Nielsen Leguminosae Anahaw Saribus rotundifolius (Lam.) Blume Arecaceae Aniatam-mali Cleistanthus decurrens Hook. f. Phyllanthaceae Anilao Colona serratifolia Cav. Malvaceae 0.38 Anislag Flueggea flexuosa Muell. Arg. Euphorbiaceae 0.69 Anolang Haplostichanthus lanceolata (S. Vidal) Annonaceae Heusden Anonang Cordia dichotoma G. Forst. Boraginaceae 0.38 Antipolo Artocarpus blancoi (Elmer) Merr. Moraceae 0.43 Anubing Artocarpus ovatus Blanco Moraceae 0.61 Anuling Pisonia umbellifera (J.R. Forst. & G. Forst.) Nyctaginaceae 0.24 Seem. Apitong Dipterocarpus grandiflorus (Blanco) Blanco Dipterocarpaceae 0.67 Aplas Ficus ampelas Burm.f. Moraceae 0.38 Avocado Persea americana Mill. Lauraceae Badlan Radermachera gigantea (Blume) Miq. Bignoniaceae 0.48 Badling Astronia cumingiana S. Vidal Melastomataceae Bagalunga Melia azedarach L. Meliaceae 0.46 Bago Gnetum gnemon L. Gnetaceae 0.61 Bagtikan Shorea malaanonan Blume Dipterocarpaceae 0.51 Bahai Ormosia calavensis Blanco Leguminosae 0.43 Bakan Litsea philippinensis Merr. Lauraceae Bakayau Cleistanthus oblongifolius (Roxb.) Muell. Arg. Phyllanthaceae 0.53 Balanti Homalanthus populneus (Geiseler) Pax Euphorbiaceae 0.29 Balete Ficus balete Merr. Moraceae 0.65

Common name Scientific name Family Gravity* [gr /cm³] Balik Hydnocarpus heterophylla Blume Achariaceae Balinghasai Buchanania arborescens (Blume) Blume Anacardiaceae 0.45 Balobo Diplodiscus paniculatus Turcz. Malvaceae 0.63 Balukanag Chisocheton cumingianus (C.DC.) Harms Meliaceae 0.55 Banaba Lagerstroemia speciosa (L.) Pers. Lythraceae 0.55 Banai-banai Radermachera pinnata (Blanco) Seem. Bignoniaceae 0.46 Bangkal Nauclea orientalis (L.) L. Rubiaceae 0.47 Bangkal, Southern / Neonauclea formicaria (Elmer) Merr. Rubiaceae Hambabalud Banitlong Cleistanthus pilosus C.B. Rob. Phyllanthaceae Bansalangin Mimusops elengi L. Sapotaceae 0.82 Basikong Ficus botryocarpa Miq. Moraceae 0.43 Batino Alstonia macrophylla Wall. ex G.Don Apocynaceae 0.64 Batitinan Lagerstroemia piriformis Koehne Lythraceae 0.50 Bayag-usa Voacanga globosa (Blanco) Merr. Apocynaceae Bayanti Aglaia rimosa (Blanco) Merr. Meliaceae 0.69 Bayog Dendrocalamus merrillianus (Elmer) Elmer Poaceae Bayok Pterospermum diversifolium Blume Sterculiaceae 0.57 Bayuko Artocarpus fretessii Teijsm. & Binn. ex Hassk. Moraceae 0.51 Benguet Pine Pinus kesiya Royle ex. Gordon Pinaceae 0.48 Bignai Antidesma bunius (L.) Spreng. Phyllanthaceae 0.51 Bignai-pogo Antidesma montanum Blume Phyllanthaceae 0.59 Binaton Falcatifolium falciforme (Parl.) de Laub. Podocarpaceae 0.57 Bingliu Polyscias cenabrei (Merr.) Lowry & G.M. Araliaceae Plunkett Binoloan Syzygium acuminatissimum (Blume) DC. Myrtaceae 0.63 Binucao Garcinia binucao (Blanco) Choisy Clusiaceae 0.75 Binunga Macaranga tanarius (L.) Muell. Arg. Euphorbiaceae 0.43 Bitanghol Calophyllum blancoi Planch. & Triana Clusiaceae 0.46 Bitanghol-sibat Calophyllum lancifolium Elmer Clusiaceae 0.53 Boga Alseodaphne philippinensis (Elmer) Kosterm. Lauraceae Bogaiat Garcinia rhizophoroides Elmer Clusiaceae 0.75 Bogo Garuga floribunda Decne. Burseraceae 0.51 Bolon Platymitra arborea (Blanco) P.J.A. Kessler Annonaceae 0.74 Botree Ficus religiosa L. Moraceae 0.44 Bridelia stipularis Bridelia stipularis (L.) Blume Phyllanthaceae Bugawak Melicope confusa (Merr.) P.S. Liu Rutaceae 0.38 Bulala (Wild Rambutan) Dimocarpus fumatus (Blume) Leenh. Sapindaceae Bulalog Parishia maingayi Hook.f. Anacardiaceae 0.51 Bunga Areca catechu L. Arecaceae Buri Corypha utan Lam. Arecaceae Butlig-babui Canthium gynochthodes Baill. Rubiaceae Butlo Aquilaria cumingiana (Decne.) Ridl. Thymelaeaceae Caimito Chrysophyllum cainito L. Sapotaceae Coconut Cocos nucifera L. Arecaceae Dacrycarpus cumingii Dacrycarpus cumingii (Parl.) de Laub. Podocarpaceae Dalingdingan Hopea foxworthyi Elmer Dipterocarpaceae 0.51 Dalinsi Terminalia pellucida C. Presl Combretaceae Dalunot Pipturus arborescens (Link) C.B. Rob. Urticaceae Dangkalam Calophyllum obliquinervium Merr. Clusiaceae 0.64

Common name Scientific name Family Gravity* [gr /cm³] Danglin Grewia multiflora Juss. Malvaceae 0.48 Dangula (Sasalit) Teijsmanniodendron ahernianum (Merr.) Bakh. Lamiaceae 1.03 Dao Dracontomelon dao (Blanco) Merr. & Rolfe Anacardiaceae 0.40 Dins Ochrosia glomerata (Blume) F. Muell. Apocynaceae 0.57 Dita Alstonia scholaris (L.). R. Br. var. scholaris Apocynaceae 0.39 Duguan Myristica philippinensis Gand. Myristicaceae 0.36 Dungau-bundok Astronia lagunensis Merr. Melastomataceae Dungo Ficus nervosa subsp. pubinervis (Blume) C.C. Moraceae 0.28 Berg Eurya nitida Eurya nitida Korth. Pentaphylacaceae 0.53 Galo Anacolosa frutescens (Blume) Blume Olacaceae Ganophyllum falcatum Ganophyllum falcatum Blume Sapindaceae Gatasan Garcinia venulosa (Blanco) Choisy Clusiaceae Gisok-Gisok Hopea philippinensis Dyer Dipterocarpaceae 0.67 Guijo Shorea guiso Blume Dipterocarpaceae 0.71 Gulob Leea aequata L. Vitaceae Gumunan Diospyros buxifolia (Blume) Hiern Ebenaceae 0.78 Hagimit Ficus minahassae (Teijsm. & Vriese) Miq. Moraceae 0.32 Hamindang Macaranga bicolor Muell. Arg. Euphorbiaceae 0.30 Haras / Ituman Garcinia ituman Merr. Clusiaceae Hawili Ficus septica Burm.f. Moraceae 0.42 Himbabao Broussonetia luzonica (Blanco) Bureau Moraceae 0.50 Hindang Myrica javanica Blume Myricaceae Igem Dacrycarpus imbricatus (Blume) de Laub. Podocarpaceae 0.41 Igyo Dysoxylum gaudichaudianum (A. Juss.) Miq. Meliaceae 0.45 Ilang-ilang Cananga odorata (Lam.) Hook.f. & Thomson Annonaceae 0.29 Ilo-ilo Aglaia iloilo (Blanco) Merr. Meliaceae 0.53 Inyam Antidesma tomentosum Blume Phyllanthaceae Ipil-ipil Leucaena leucocephala (Lam.) de Wit Leguminosae 0.64 Is-is Ficus ulmifolia Lam. Moraceae 0.38 Itangan Weinmannia luzoniensis S. Vidal Cunoniaceae 0.49 Kahoi dalaga Mussaenda philippica A. Rich. Rubiaceae Kalambug Gordonia luzonica S. Vidal Theaceae Kalantas Toona calantas Merr. & Rolfe Meliaceae 0.29 Kaliantan Leea philippinensis Merr. Vitaceae Kalingag / Cinamomon Cinnamomum mercadoi S. Vidal Lauraceae 0.43 Kalokoi Ficus callosa Willd. Moraceae 0.29 Kalomala Elaeocarpus calomala (Blanco) Merr. Elaeocarpaceae Kalulot Artocarpus rubrovenius Warb. Moraceae 0.58 Kalumpit Terminalia microcarpa Decne. Combretaceae 0.53 Kamagong Diospyros discolor Willd. Ebenaceae 0.88 Kamandiis Garcinia rubra Merr. Clusiaceae Kamatog Sympetalandra densiflora (Elmer) Steenis Leguminosae 0.76 Kamuling Microcos stylocarpa Burret Malvaceae 0.41 Kanapai Ficus magnoliifolia Blume Moraceae 0.28 Kansulud Aglaia argentea Blume Meliaceae 0.63 Kape Coffea arabica L. Rubiaceae Karaksan Chionanthus ramiflorus Roxb. Oleaceae 0.67 Katagpo Psychotria luzoniensis (Cham. & Schltdl.) Rubiaceae Fern.-Vill.

Common name Scientific name Family Gravity* [gr /cm³] Katmon Dillenia philippinensis Rolfe Dilleniaceae 0.63 Katong-matsin Chisocheton pentandrus (Blanco) Merr. Meliaceae 0.51 Kubi Artocarpus nitidus Trécul Moraceae 0.48 Kuela Bhesa paniculata Arn. Centroplacaceae 0.66 Kulatingan Pterospermum obliquum Blanco Sterculiaceae Kurutan Olea borneensis Boerl. Oleaceae Labayanan Radermachera coriacea Merr. Bignoniaceae Lago Prunus grisea (Blume ex Muell. Berol.) Rosaceae 0.55 Kalkman Lamio Dracontomelon dao (Blanco) Merr. & Rolfe Anacardiaceae 0.40 Lamog Planchonia spectabilis Merr. Lecythidaceae 0.58 Lanete Wrightia pubescens subsp. laniti (Blanco) Apocynaceae Ngan Lanipga Toona philippinensis Elmer Meliaceae Lanutan Mitrephora lanotan (Blanco) Merr. Annonaceae Lanutan-dilau Polyalthia flava Merr. Annonaceae 0.51 Lingatong Laportea brunnea Merr. Urticaceae Lingo-lingo Vitex turczaninowii Merr. Lamiaceae 0.49 Lipang-kalabaw Dendrocnide meyeniana (Walp.) Chew Urticaceae Lisak Neonauclea bartlingii (DC.) Merr. Rubiaceae Litsea cordata Litsea cordata (Jack) Hook. f. Lauraceae 0.36 Lubeg Syzygium lineatum (DC.) Merr. & L.M. Perry Myrtaceae 0.73 Lumbayao Heritiera javanica (Blume) Kosterm. Malvaceae 0.62 Lunas Lunasia amara Blanco Rutaceae Macaranga Macaranga dipterocarpifolia Merr. Euphorbiaceae dipterocarpifolia Magabuyo Celtis luzonica Warb. Cannabaceae 0.55 Mahogany Swietenia mahagoni (L.) Jacq. Meliaceae 0.51 Malabignai Aporosa symplocifolia Merr. Phyllanthaceae Malabuho Sterculia oblongata R. Br. Sterculiaceae 0.22 Malabunga Alseodaphne malabonga (Blanco) Kosterm. Lauraceae Malaikmo Celtis philippensis Blanco Cannabaceae 0.69 Malak-malak Palaquium philippense (Perr.) C.B. Rob. Sapotaceae 0.46 Malakadios Dehaasia cairocan (Vidal) C.K. Allen Lauraceae Malakape Psydrax dicoccos Gaertn. Rubiaceae Malakatmon Dillenia luzoniensis (Vidal) Merr. Dilleniaceae 0.69 Malapapaya Polyscias nodosa (Blume) Seem. Araliaceae 0.32 Malaruhat / Panglomboyen Syzygium claviflorum (Roxb.) Wall. ex A.M. Myrtaceae 0.64 Cowan & Cowan Malaruhat-puti Syzygium bordenii (Merr.) Merr. Myrtaceae 0.73 Malasangki Euonymus indicus B. Heyne ex Wall. Celastraceae 0.55 Malasantol Sandoricum vidalii Merr. Meliaceae 0.45 Malugai Allophylus cobbe (L.) Raeusch. Sapindaceae 0.58 Mangium Acacia mangium Willd. Leguminosae Mangkas Planchonella obovata (R.Br.) Pierre Sapotaceae 0.81 Matang-araw Melicope triphylla (Lam.) Merr. Rutaceae 0.39 Matang-hipon Breynia vitis-idaea (Burm.f.) C.E.C. Fisch. Euphorbiaceae Milipili Canarium hirsutum Willd. Burseraceae 0.49 Molave Vitex parviflora A. Juss. Lamiaceae 0.70 Nangka Artocarpus heterophyllus Lam. Moraceae 0.49

Common name Scientific name Family Gravity* [gr /cm³] Narra Pterocarpus indicus Willd. Leguminosae 0.53 Nato Palaquium luzoniense (Fern.-Vill.) Vidal Sapotaceae 0.55 Natong-linis Palaquium glabrifolium Merr. Sapotaceae 0.55 Niog-niyogan Ficus pseudopalma Blanco Moraceae Pagsahingin-bulog Canarium asperum Benth. Burseraceae 0.47 Paguringon Cratoxylum sumatranum (Jack) Blume Hypericaceae 0.59 Paho Mangifera philippinensis Mukherji Anacardiaceae 0.52 Pahutan Mangifera altissima Blanco Anacardiaceae 0.59 Pakiling Ficus odorata (Blanco) Merr. Moraceae 0.32 Pakong buwaya Cyathea contaminans (Wall. ex Hook.) Copel. Cyatheaceae Pangnan Lithocarpus sulitii Soepadmo Fagaceae 0.86 Philippine Ash Fraxinus griffithii C.B. Clarke Oleaceae 0.60 Pili Canarium ovatum Engl. Burseraceae Pugahan Caryota cumingii Lodd. ex Mart. Arecaceae Puso-puso Neolitsea vidalii Merr. Lauraceae Putian Alangium javanicum (Blume) Wang. var. jaheri Cornaceae 0.73 Bloem. Rain Tree (Acacia) Albizia saman (Jacq.) Merr. Leguminosae 0.49 Red Lauan Shorea negrosensis Foxw. Dipterocarpaceae 0.51 Sablot Litsea glutinosa (Lour.) C.B. Rob. Lauraceae 0.50 Saguimsim Syzygium brevistylum (C.B. Rob.) Merr Myrtaceae Sakat Terminalia nitens C. Presl Combretaceae 0.58 Salinggogon Cratoxylum formosum (Jacq.) Benth. & Hook.f. Hypericaceae 0.72 ex Dyer Salingkugi Albizia saponaria (Lour.) Miq. Leguminosae 0.57 Sarawag Pinanga insignis Becc. Arecaceae Spike pepper Piper aduncum L. Piperaceae Subiang Bridelia insulana Hance. Phyllanthaceae Symplocos lancifolia Symplocos lancifolia Siebold & Zucc. Symplocaceae Tabau Lumnitzera littorea (Jack) Voigt Combretaceae 0.69 Tabian Elaeocarpus monocera Cav. Elaeocarpaceae Tabon-tabon Atuna racemosa Raf. Chrysobalanaceae 0.67 Tagpo Ardisia elliptica Thunb. Primulaceae Taklang-anak Garcinia dulcis (Roxb.) Kurz Clusiaceae 0.72 Talisay-gubat Terminalia foetidissima Griff. Combretaceae 0.60 Taluto Pterocymbium tinctorium Merr. Sterculiaceae 0.25 Tamayuan Strombosia philippinensis S. Vidal Olacaceae 0.70 Tambis Syzygium aqueum (Burm. f.) Alston Myrtaceae Tan-ag Kleinhovia hospita L. Malvaceae 0.39 Tara-tara Dysoxylum cumingianum C.DC. Meliaceae 0.72 Tarangisi Aglaia cumingiana Turcz. Meliaceae Tiagkot Archidendron clypearia subsp. clypearia (Jack) Leguminosae 0.32 I.C. Nielsen Tiaong Shorea ovata Dyer ex Brandis Dipterocarpaceae 0.64 Tibig Ficus nota (Blanco) Merr. Moraceae Tipurus Palaquium polyandrum C.B. Rob. Sapotaceae 0.55 Toog Petersianthus quadrialatus (Merr.) Merr. Lecythidaceae 0.54 Tuai Bischofia javanica Blume Euphorbiaceae 0.61 Tukang-kalau Aglaia pachyphylla Miq. Meliaceae 0.69 Tulo Alphitonia philippinensis Braid Rhamnaceae 0.40

Common name Scientific name Family Gravity* [gr /cm³] Ulayan (Oak) Lithocarpus caudatifolius (Merr.) Rehder Fagaceae Uyok Saurauia elegans Fern.-Vill. Actinidiaceae Wenzel anang Diospyros lanceifolia Roxb. Ebenaceae 0.66 White Lauan Shorea contorta S. Vidal Dipterocarpaceae 0.43 Yemane Gmelina arborea Roxb. Lamiaceae 0.43

* for tree species without specific wood gravity, the average wood specific gravity for tropical tree species in Asia of 0.57 gr/cm³ published by Brown (1997) has been used

Annex 3: List of recorded species by botanical family

Family Genus & Species Type Closed Forests Open Forests Observations Dmax Observations Dmax [cm] [cm] Achariaceae Hydnocarpus Tree 3 46.3 heterophylla Blume Actinidiaceae Saurauia elegans Tree 1 13.5 2 35.5 Fern.-Vill. Anacardiaceae Buchanania Tree 4 37.0 13 60.2 arborescens (Blume) Blume Anacardiaceae Dracontomelon dao Tree 2 15.7 14 81.6 (Blanco) Merr. & Rolfe Anacardiaceae Koordersiodendron Tree 2 30.2 pinnatum Merr. Anacardiaceae Mangifera altissima Tree 3 100.7 Blanco Anacardiaceae Mangifera Tree 3 38.0 philippinensis Mukherji Anacardiaceae Parishia maingayi Tree 1 8.9 Hook.f. Annonaceae Cananga odorata Tree 1 48.2 (Lam.) Hook.f. & Thomson Annonaceae Goniothalamus amuyon Tree 3 55.2 4 10.4 (Blanco) Merr. Annonaceae Mitrephora lanotan Tree 1 7.1 1 43.3 (Blanco) Merr. Annonaceae Platymitra arborea Tree 2 14.1 (Blanco) P.J.A. Kessler Annonaceae Polyalthia flava Merr. Tree 1 7.3 Apocynaceae Alstonia macrophylla Tree 9 40.8 12 23.2 Wall. ex G. Don Apocynaceae Alstonia scholaris (L.) Tree 3 40.2 6 79.1 R. Br. var. scholaris Apocynaceae Ochrosia glomerata Tree 1 24.1 (Blume) F. Muell. Apocynaceae Voacanga globosa Tree 2 7.6 12 10.4 (Blanco) Merr. Apocynaceae Wrightia pubescens Tree 4 34.2 subsp. laniti (Blanco) Ngan Araliaceae Polyscias cenabrei Tree 1 19.4 1 21.5 (Merr.) Lowry & G.M. Plunkett Araliaceae Polyscias nodosa Tree 3 39.0 4 13.2 (Blume) Seem. Araucariaceae Agathis philippinensis Tree 3 18.1 Warb. Arecaceae Areca catechu L. Palm 1 6.9 Arecaceae Caryota cumingii Lodd. Palm 1 6.0 3 13.8 ex Mart. Arecaceae Cocos nucifera L. Palm 2 31.2 19 34.1

Family Genus & Species Type Closed Forests Open Forests Observations Dmax Observations Dmax [cm] [cm] Arecaceae Corypha utan Lam. Palm 2 7.5 Arecaceae Pinanga insignis Becc. Palm 5 12.6 Arecaceae Saribus rotundifolius Palm 3 22.0 6 25.0 (Lam.) Blume Bignoniaceae Radermachera Tree 5 19.4 1 11.7 coriacea Merr. Bignoniaceae Radermachera pinnata Tree 13 146.0 22 41.2 (Blanco) Seem. Boraginaceae Cordia dichotoma G. Tree 1 29.8 2 40.1 Forst. Burseraceae Canarium asperum Tree 18 54.0 31 36.8 Benth. Burseraceae Canarium hirsutum Tree 1 8.0 Willd. Burseraceae Canarium ovatum Engl. Tree 1 9.5 Burseraceae Garuga floribunda Tree 4 26.1 8 38.4 Decne. Cannabaceae Celtis luzonica Warb. Tree 1 5.1 3 18.5 Cannabaceae Celtis philippensis Tree 2 9.8 Blanco Cannabaceae Trema orientalis (L.) Tree 2 47.8 4 38.8 Blume Casuarinaceae Casuarina equisetifolia Tree 13 89.0 11 34.0 L. Casuarinaceae Gymnostoma Tree 2 32.3 rumphianum (Miq.) L.A.S. Johnson Celastraceae Euonymus indicus B. Tree 6 15.0 2 35.0 Heyne ex Wall. Centroplacaceae Bhesa paniculata Arn. Tree 3 17.0 Chrysobalanaceae Atuna racemosa Raf. Tree 1 8.5 Clusiaceae Calophyllum blancoi Tree 5 10.8 9 20.8 Planch. & Triana Clusiaceae Calophyllum lancifolium Tree 1 14.0 Elmer Clusiaceae Calophyllum Tree 1 8.4 obliquinervium Merr. Clusiaceae Garcinia binucao Tree 5 11.0 10 27.8 (Blanco) Choisy Clusiaceae Garcinia dulcis (Roxb.) Tree 4 45.0 4 33.7 Kurz Clusiaceae Garcinia ituman Merr. Tree 1 9.2 Clusiaceae Garcinia rhizophoroides Tree 1 22.0 2 22.5 Elmer Clusiaceae Garcinia rubra Merr. Tree 2 7.5 13 16.5 Clusiaceae Garcinia venulosa Tree 1 7.4 2 22.1 (Blanco) Choisy Combretaceae Lumnitzera littorea Tree 10 15.4 9 62.3 (Jack) Voigt Combretaceae Terminalia foetidissima Tree 5 35.7 Griff. Combretaceae Terminalia microcarpa Tree 1 34.2 Decne.

Family Genus & Species Type Closed Forests Open Forests Observations Dmax Observations Dmax [cm] [cm] Combretaceae Terminalia nitens C. Tree 2 6.2 Presl Combretaceae Terminalia pellucida C. Tree 1 20.5 1 19.0 Presl Cornaceae Alangium javanicum Tree 2 15.4 6 28.0 (Blume) Wang. var. jaheri Bloem. Cunoniaceae Weinmannia Tree 2 32.0 1 28.6 luzoniensis S. Vidal Cyatheaceae Cyathea contaminans Tree 19 65.4 3 14.5 (Wall. ex Hook.) Copel. fern Dilleniaceae Dillenia luzoniensis Tree 1 6.3 (Vidal) Merr. Dilleniaceae Dillenia philippinensis Tree 3 43.5 5 30.9 Rolfe Dipterocarpaceae Dipterocarpus Tree 2 45.7 grandiflorus (Blanco) Blanco Dipterocarpaceae Hopea foxworthyi Tree 1 9.9 Elmer Dipterocarpaceae Hopea philippinensis Tree 2 9.8 Dyer Dipterocarpaceae Shorea almon Foxw. Tree 1 28.9 Dipterocarpaceae Shorea contorta S. Tree 5 68.1 5 103.0 Vidal Dipterocarpaceae Shorea guiso Blume Tree 6 73.5 5 41.5 Dipterocarpaceae Shorea malaanonan Tree 4 66.0 3 26.5 Blume Dipterocarpaceae Shorea negrosensis Tree 6 40.2 Foxw. Dipterocarpaceae Shorea ovata Dyer ex Tree 2 56.9 Brandis Ebenaceae Diospyros buxifolia Tree 2 27.5 (Blume) Hiern Ebenaceae Diospyros discolor Tree 1 16.1 3 64.0 Willd. Ebenaceae Diospyros lanceifolia Tree 4 27.8 Roxb. Elaeocarpaceae Elaeocarpus calomala Tree 13 53.4 6 62.5 (Blanco) Merr. Elaeocarpaceae Elaeocarpus monocera Tree 1 17.0 Cav. Euphorbiaceae Bischofia javanica Tree 21 104.0 17 111.0 Blume Euphorbiaceae Breynia vitis-idaea Tree 1 18.1 (Burm.f.) C.E.C. Fisch. Euphorbiaceae Flueggea flexuosa Tree 1 5.9 Muell. Arg. Euphorbiaceae Homalanthus Tree 2 27.7 7 17.0 populneus (Geiseler) Pax Euphorbiaceae Macaranga bicolor Tree 6 10.4 7 43.0 Muell. Arg. Euphorbiaceae Macaranga Tree 1 5.0 dipterocarpifolia Merr.

Family Genus & Species Type Closed Forests Open Forests Observations Dmax Observations Dmax [cm] [cm] Euphorbiaceae Macaranga tanarius Tree 2 20.5 6 30.3 (L.) Muell. Arg. Euphorbiaceae Melanolepis Tree 1 22.4 1 21.6 multiglandulosa (Reinw. ex Blume) Rchb. & Zoll. Fagaceae Lithocarpus Tree 2 109.0 2 35.6 caudatifolius (Merr.) Rehder Fagaceae Lithocarpus sulitii Tree 1 9.8 1 6.1 Soepadmo Gnetaceae Gnetum gnemon L. Tree 3 20.0 Hypericaceae Cratoxylum formosum Tree 4 24.2 6 27.7 (Jacq.) Benth. & Hook.f. ex Dyer Hypericaceae Cratoxylum Tree 4 25.6 14 47.6 sumatranum (Jack) Blume Lamiaceae Gmelina arborea Roxb. Tree 1 39.1 4 27.4 Lamiaceae Premna odorata Blanco Tree 2 8.0 9 24.9 Lamiaceae Teijsmanniodendron Tree 5 21.0 7 43.6 ahernianum (Merr.) Bakh. Lamiaceae Vitex parviflora A. Juss. Tree 1 44.4 1 10.1 Lamiaceae Vitex turczaninowii Tree 1 33.6 Merr. Lauraceae Alseodaphne Tree 2 29.2 1 34.9 malabonga (Blanco) Kosterm. Lauraceae Alseodaphne Tree 1 5.4 philippinensis (Elmer) Kosterm. Lauraceae Cinnamomum Tree 13 49.0 10 34.4 mercadoi S. Vidal Lauraceae Dehaasia cairocan Tree 4 38.5 8 27.8 (Vidal) C.K. Allen Lauraceae Litsea cordata (Jack) Tree 3 25.5 Hook. f. Lauraceae Litsea glutinosa (Lour.) Tree 1 5.2 C.B. Rob. Lauraceae Litsea philippinensis Tree 8 56.0 6 54.7 Merr. Lauraceae Neolitsea vidalii Merr. Tree 8 22.0 3 28.1 Lauraceae Persea americana Mill. Tree 1 21.1 1 11.1 Lecythidaceae Petersianthus Tree 1 59.4 3 58.3 quadrialatus (Merr.) Merr. Lecythidaceae Planchonia spectabilis Tree 3 48.5 1 55.0 Merr. Leguminosae Acacia mangium Willd. Tree 1 28.0 Leguminosae Albizia procera (Roxb.) Tree 2 20.0 2 35.0 Benth. Leguminosae Albizia saman (Jacq.) Tree 1 70.5 Merr.

Family Genus & Species Type Closed Forests Open Forests Observations Dmax Observations Dmax [cm] [cm] Leguminosae Albizia saponaria Tree 1 13.4 5 40.0 (Lour.) Miq. Leguminosae Archidendron clypearia Tree 2 20.8 6 33.5 subsp. clypearia (Jack) I.C. Nielsen Leguminosae Archidendron Tree 3 6.9 scutiferum (Blanco) I.C. Nielsen Leguminosae Leucaena leucocephala Tree 3 27.0 (Lam.) de Wit Leguminosae Ormosia calavensis Tree 1 6.3 Blanco Leguminosae Pterocarpus indicus Tree 2 47.1 10 55.1 Willd. Leguminosae Sympetalandra Tree 1 37.5 densiflora (Elmer) Steenis Lythraceae Lagerstroemia Tree 1 11.9 piriformis Koehne Lythraceae Lagerstroemia Tree 1 10.3 speciosa (L.) Pers. Malvaceae Colona serratifolia Cav. Tree 1 43.0 5 18.1 Malvaceae Diplodiscus paniculatus Tree 1 5.9 1 29.2 Turcz. Malvaceae Grewia multiflora Juss. Tree 2 30.3 1 17.1 Malvaceae Heritiera javanica Tree 3 47.2 (Blume) Kosterm. Malvaceae Kleinhovia hospita L. Tree 3 20.9 2 36.9 Malvaceae Microcos stylocarpa Tree 3 22.0 Burret Melastomataceae Astronia cumingiana S. Tree 5 26.7 1 5.3 Vidal Melastomataceae Astronia lagunensis Tree 2 31.8 Merr. Meliaceae Aglaia argentea Blume Tree 1 33.7 1 37.4 Meliaceae Aglaia cumingiana Tree 4 6.4 1 26.0 Turcz. Meliaceae Aglaia iloilo (Blanco) Tree 3 11.0 Merr. Meliaceae Aglaia pachyphylla Miq. Tree 2 50.9 Meliaceae Aglaia rimosa (Blanco) Tree 16 56.0 1 6.0 Merr. Meliaceae Aglaia tomentosa Tree 1 25.2 1 9.2 Teijsm. & Binn. Meliaceae Chisocheton Tree 1 104.7 cumingianus (C.DC.) Harms Meliaceae Chisocheton Tree 1 9.3 pentandrus (Blanco) Merr. Meliaceae Dysoxylum Tree 6 18.7 cumingianum C. DC. Meliaceae Dysoxylum Tree 1 13.4 3 44.1 gaudichaudianum (A. Juss.) Miq.

Family Genus & Species Type Closed Forests Open Forests Observations Dmax Observations Dmax [cm] [cm] Meliaceae Melia azedarach L. Tree 2 28.0 Meliaceae Sandoricum vidalii Tree 2 20.1 3 24.5 Merr. Meliaceae Swietenia mahagoni Tree 7 30.5 (L.) Jacq. Meliaceae Toona calantas Merr. & Tree 1 7.3 Rolfe Meliaceae Toona philippinensis Tree 3 73.2 3 60.5 Elmer Moraceae Artocarpus blancoi Tree 5 35.8 (Elmer) Merr. Moraceae Artocarpus fretessii Tree 5 31.6 2 12.8 Teijsm. & Binn. ex Hassk. Moraceae Artocarpus Tree 1 12.5 heterophyllus Lam. Moraceae Artocarpus nitidus Tree 1 24.2 Trécul Moraceae Artocarpus ovatus Tree 1 48.0 Blanco Moraceae Artocarpus rubrovenius Tree 6 134.0 1 30.2 Warb. Moraceae Broussonetia luzonica Tree 1 24.1 (Blanco) Bureau Moraceae Ficus ampelas Burm.f. Tree 1 23.0 Moraceae Ficus balete Merr. Tree 6 82.0 8 68.0 Moraceae Ficus botryocarpa Miq. Tree 1 11.2 1 7.3 Moraceae Ficus callosa Willd. Tree 2 16.2 Moraceae Ficus magnoliifolia Tree 4 33.4 4 100.0 Blume Moraceae Ficus minahassae Tree 1 23.1 4 35.8 (Teijsm. & Vriese) Miq. Moraceae Ficus nervosa subsp. Tree 2 57.3 pubinervis (Blume) C.C. Berg Moraceae Ficus nota (Blanco) Tree 17 34.0 10 30.2 Merr. Moraceae Ficus odorata (Blanco) Tree 2 9.1 Merr. Moraceae Ficus pseudopalma Tree 3 7.6 1 5.6 Blanco Moraceae Ficus religiosa L. Tree 2 35.1 1 5.7 Moraceae Ficus septica Burm.f. Tree 10 12.2 14 13.3 Moraceae Ficus ulmifolia Lam. Tree 1 11.1 Moraceae Paratrophis Tree 6 39.0 philippinensis Fern. - Vill. Myristicaceae Myristica philippinensis Tree 8 52.0 7 36.5 Gand. Myrtaceae Syzygium Tree 5 20.3 3 17.6 acuminatissimum (Blume) DC. Myrtaceae Syzygium aqueum Tree 1 10.0 (Burm. f.) Alston

Family Genus & Species Type Closed Forests Open Forests Observations Dmax Observations Dmax [cm] [cm] Myrtaceae Syzygium bordenii Tree 5 23.5 (Merr.) Merr. Myrtaceae Syzygium brevistylum Tree 1 5.6 (C.B. Rob.) Merr Myrtaceae Syzygium claviflorum Tree 10 45.1 10 46.5 (Roxb.) Wall. ex A.M. Cowan & Cowan Myrtaceae Syzygium lineatum Tree 1 8.8 (DC.) Merr. & L.M. Perry Nyctaginaceae Pisonia umbellifera Tree 1 8.0 2 32.1 (J.R. Forst. & G. Forst.) Seem. Olacaceae Strombosia Tree 2 6.5 philippinensis S. Vidal Oleaceae Chionanthus ramiflorus Tree 1 11.1 Roxb. Oleaceae Fraxinus griffithii C.B. Tree 1 42.0 2 32.3 Clarke Oleaceae Olea borneensis Boerl. Tree 4 37.8 2 27.0 Pandanaceae Pandanus luzonensis Palm 6 25.2 7 29.0 Merr Pentaphylacaceae Eurya nitida Korth. Tree 1 16.0 1 8.2 Phyllanthaceae Antidesma bunius (L.) Tree 1 11.5 Spreng. Phyllanthaceae Antidesma montanum Tree 1 28.1 3 13.0 Blume Phyllanthaceae Antidesma tomentosum Tree 3 9.1 1 9.5 Blume Phyllanthaceae Aporosa symplocifolia Tree 4 24.3 Merr. Phyllanthaceae Bridelia insulana Tree 1 10.5 Hance. Phyllanthaceae Bridelia stipularis (L.) Tree 1 18.9 Blume Phyllanthaceae Cleistanthus decurrens Tree 3 34.6 4 10.1 Hook. f. Phyllanthaceae Cleistanthus Tree 5 22.1 oblongifolius (Roxb.) Muell. Arg. Phyllanthaceae Cleistanthus pilosus Tree 2 39.0 C.B. Rob. Pinaceae Pinus kesiya Royle ex. Tree 1 24.8 Gordon Piperaceae Piper aduncum L. Tree 1 7.0 1 6.2 Poaceae Dendrocalamus Bamboo 1 9.0 merrillianus (Elmer) Elmer Podocarpaceae Dacrycarpus cumingii Tree 2 24.8 (Parl.) de Laub. Podocarpaceae Dacrycarpus imbricatus Tree 1 23.1 3 52.5 (Blume) de Laub. Podocarpaceae Falcatifolium falciforme Tree 1 27.2 1 7.7 (Parl.) de Laub. Primulaceae Ardisia elliptica Thunb. Tree 1 7.2 8 14.7

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Family Genus & Species Type Closed Forests Open Forests Observations Dmax Observations Dmax [cm] [cm] Rhamnaceae Alphitonia Tree 1 16.2 philippinensis Braid Rosaceae Prunus grisea (Blume Tree 2 45.5 1 15.7 ex Muell. Berol.) Kalkman Rubiaceae Canthium Tree 2 12.0 gynochthodes Baill. Rubiaceae Coffea arabica L. Tree 1 5.5 3 6.7 Rubiaceae Mussaenda philippica Tree 1 10.5 2 5.8 A. Rich. Rubiaceae Nauclea orientalis (L.) Tree 2 21.2 L. Rubiaceae Neonauclea bartlingii Tree 6 20.5 10 22.4 (DC.) Merr. Rubiaceae Neonauclea formicaria Tree 12 34.9 26 58.2 (Elmer) Merr. Rubiaceae Psychotria luzoniensis Tree 4 11.1 1 6.1 (Cham. & Schltdl.) Fern.-Vill. Rubiaceae Psydrax dicoccos Tree 1 6.5 11 17.0 Gaertn. Rutaceae Lunasia amara Blanco Tree 1 6.4 Rutaceae Melicope confusa Tree 1 8.3 (Merr.) P.S. Liu Rutaceae Melicope triphylla Tree 1 15.0 (Lam.) Merr. Sapindaceae Allophylus cobbe (L.) Tree 5 27.0 1 27.0 Raeusch. Sapindaceae Dimocarpus fumatus Tree 2 29.0 2 15.2 (Blume) Leenh. Sapindaceae Dimocarpus longan Tree 1 13.3 1 28.5 subsp. malesianus Leenh. Sapindaceae Ganophyllum falcatum Tree 3 23.1 Blume Sapindaceae Guioa koelreuteria Tree 1 12.2 1 9.5 (Blanco) Merr. Sapotaceae Chrysophyllum cainito Tree 1 32.0 L. Sapotaceae Mimusops elengi L. Tree 11 46.6 2 27.4 Sapotaceae Palaquium glabrifolium Tree 1 10.6 Merr. Sapotaceae Palaquium luzoniense Tree 9 42.0 10 65.2 (Fern.-Vill.) Vidal Sapotaceae Palaquium philippense Tree 3 31.7 (Perr.) C.B. Rob. Sapotaceae Palaquium polyandrum Tree 2 13.0 13 49.2 C.B. Rob. Sapotaceae Planchonella obovata Tree 1 12.0 (R.Br.) Pierre Sterculiaceae Pterocymbium Tree 3 42.1 17 85.7 tinctorium Merr. Sterculiaceae Pterospermum Tree 3 42.7 14 54.4 diversifolium Blume

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Family Genus & Species Type Closed Forests Open Forests Observations Dmax Observations Dmax [cm] [cm] Sterculiaceae Pterospermum Tree 1 32.9 obliquum Blanco Sterculiaceae Sterculia oblongata R. Tree 3 10.6 3 55.6 Br. Symplocaceae Symplocos lancifolia Tree 4 8.6 2 7.7 Siebold & Zucc. Theaceae Gordonia luzonica S. Tree 5 30.0 2 8.4 Vidal Thymelaeaceae Aquilaria cumingiana Tree 2 15.0 (Decne.) Ridl. Urticaceae Dendrocnide Tree 4 21.7 2 8.7 meyeniana (Walp.) Chew Urticaceae Laportea brunnea Merr. Tree 6 29.4 5 28.3 Urticaceae Leucosyke capitellata Tree 15 19.1 10 31.5 Wedd. Urticaceae Oreocnide trinervis Tree 6 13.2 (Wedd.) Miq. Urticaceae Pipturus arborescens Tree 10 33.5 1 5.5 (Link) C.B. Rob. Vitaceae Leea aculeata Blume Tree 1 7.0 2 6.2 ex Spreng Vitaceae Leea aequata L. Tree 5 42.1 Vitaceae Leea philippinensis Tree 1 5.5 Merr.

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Annex 4: Detailed results - closed forests

Pdf file with 85 pages accessible from: http://forestry.denr.gov.ph/redd-plus-philippines/publications-pdf/unpublished/Panay_FRA-Appendix4- Closed_%20Forests-Detailed_results_2016-12-07.pdf

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Annex 5: Detailed results - open forests

Pdf file with 121 pages accessible from: http://forestry.denr.gov.ph/redd-plus-philippines/publications-pdf/unpublished/Panay_FRA-Appendix5- Open_Forests-Detailed_results_2016-12-07.pdf

104

Annex 6: Statistical parameters - closed forests

Pdf file with 9 pages accessible from: http://forestry.denr.gov.ph/redd-plus-philippines/publications-pdf/unpublished/Panay_FRA-Appendix6- Closed_Forests-Statistics_2016-12-07.pdf

105

Annex 7: Statistical parameters - open forests

Pdf file with 13 pages accessible from: http://forestry.denr.gov.ph/redd-plus-philippines/publications-pdf/unpublished/Panay_FRA-Appendix7- Open_Forests-Statistics_2016-12-07.pdf

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