developing a georeferenced database of selected threatened...

Philippine Journal of Science141 (2): 165-177, December 2012ISSN 0031 - 7683Date Received: 15 Nov 2011

Key Words: database, georeference, species occurrence, threatened forest trees

*Corresponding author: [email protected]; [email protected]

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Lawrence Tolentino Ramos1, Alfie Misena Torres1,Florencia Bacani Pulhin1,2, and Rodel Diaz Lasco1

Developing a Georeferenced Database of SelectedThreatened Forest Tree Species in the Philippines

1World Agroforestry Centre, 2F Khush Hall, IRRI Campus,Los Baños, Laguna, Philippines

2Forestry Development Center, College of Forestry and Natural Resources,University of the Philippines Los Banos, College, Laguna, Philippines

Georeferenced species occurrence is a prerequisite in species distribution modeling and species-ecosystem correlation analysis and also aids in tracking plant species and prioritizing scarce resources for conservation. The Global Biodiversity Information Facility, legacy literature of biodiversity, contemporary literature, technical reports and biodiversity surveys are important sources of species occurrence data waiting to be georeferenced. In this paper, we discussed a method used to georeference occurrences of threatened forest tree species from the above sources. Locality descriptions were initially narrowed down in geographic information system using administrative maps and further confined using two criteria: 1) elevation and 2) surface cover information from remotely-sensed images. The result was a georeferenced database of 2,067 occurrence records of 47 threatened forest species on a national scale. Each record had a unique point feature per species and enough metadata directing the database user to the source of occurrence data. The database can be used as a tool in determining priority species for specimen or germplasm collection, for taxonomic identification and historical mapping. It also serves as an integral component in spatially modeling the distribution of tree species and forest formations in the past and in a possible future scenario.

INTRODUCTIONThe Philippines is a tropical country hosting a high concentration of plant species diversity, ranking 5th in the world, and housing 5% of the world’s flora (RP 2009). Yet ironically, it is also a leading biodiversity hotspot of threatened forest trees in the world due to anthropogenic habitat alteration (Myers et al. 2000). The environment department’s administrative order (DAO) 2007-01 (DENR 2007), which constitutes the official country listing of threatened plant species, lists 174 vulnerable species, 101 critically-endangered species, 187 endangered species

and 64 other threatenedplant speciesin the Philippines. The International Union for Conservation of Nature (IUCN 2003) Red list of threatened plant species also provides an annually updated listing. Forest tree species are particularly threatened due mainly for their timber resource.

There is much information contained in legacy literature as well as in herbaria collections, domestic and abroad. A great challenge now is the process of translating these sources from analogue to digital form to enable access by a wider public. In the case of legacy literature, digitization has made some headway already under the Biodiversity Heritage Library (BHL) portal, which

Ramos LT et al.: Georeferenced Database of Selected Threatened Forest Tree in the Philippines

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aims to make legacy literature accessible to the public (Gwinn & Rinaldo 2008). For instance, the voluminous publications of Merrill, which document the diversity and spatial distribution Philippine forests used to have are already digitized and can be accessed through the BHL portal. Among Merrill’s significant publications is the Enumeration of Philippine Flowering Plants Vol I-IV (1923-1926) which is the most comprehensive species list on a national scale to date.

Extensive work needs to be done, however, for the case of herbarium specimen collections which remain largely analogue in form, if not entirely raw specimen tags. The discrepancy is easily appreciated if they are to be compared with biological information in the molecular and ecosystem levels which are largely digitized (Lane & Edwards 2007). Another issue is access, for although biodiversity is concentrated in the developing countries, the wealth of scientific information in digitized form is concentrated in the libraries and natural history institutions of developed countries, probably very remote from the specimen’s origin (Edwards 2000).

Specimens contain basic, yet important information, i.e. scientific name, collector name, collection date, and locality description, at the least. Traditionally, locality descriptions are based on names of places or situational landmarks that change over time (Beaman et al. 2004). At best, a species locality description should be specific enough, leaving no room for uncertainty in interpretation (Chapman & Wieczorek 2006).

The need for such primary scientific information has been growing (Lane & Edwards 2007), especially as conservation efforts scramble to preserve the remaining tracts of undisturbed ecosystems. Chapman (2005) discusses the multiple uses of primary species occurrence data.

To satisfy such a growing demand for biodiversity information, not only does information need to be digitized for ease of access, but also the framework for information sharing requires interoperability in searching through multiple online databases seamlessly (Edwards 2000). This was the rationale for the establishment of the Global Biodiversity Information Facility (GBIF), a worldwide network to make primary information of all species freely and universally available via the internet. Its current focus for the next years is the digitization of natural history specimens collected over the last 300 years and its migration into modern information management systems and platforms (Lane & Edwards 2007).

Legacy literature and GBIF records both contain occurrence information, but these are not necessarily georeferenced information. Georeferencing means putting in the map the exact location of species. For the past 300 years, specimen locality descriptions had been recorded

as cardinal offsets to political or geographic features (Beaman & Conn 2003). This unstandardized way of describing localities poses many challenges to automated parsing and interpretation (Beaman et al. 2004).

More than 90% of the billion or more occurrence records found in biological specimens worldwide are not georeferenced (Duckworth et al. 1993; Beaman & Conn 2003; Beaman et al. 2004; Guralnick et al. 2006). Relatively accurate georeferenced information associated with specimen collections only came with the use of GPS (Global Positioning System) devices.

In the case of the Philippines, there are only a few, recent records in GBIF which have specific fine-scale location information. Species occurrence data before GPS only mention of localities, usually in the scale of provinces and municipalities. To compound the problem, names or boundaries of some localities have changed over the last hundred years. Formerly large provinces and districts have been subdivided giving way to the creation of new government units.

Various authors have already contributed in documenting threatened forest tree species occurrence on a national scale. The works of De Guzman et al. (1986) and Rojo (1999) are contemporary examples of these. There are also numerous peer-reviewed journal articles, technical reports, and biodiversity surveys done on watershed or protected area scale that are good sources of occurrence data. But these are often analogue in form. If there happens to be georeferenced information as is the usual practice today, the location data often stays with the collector and is not indicated in the specimen tag.

In compliance to the Convention of Biological Diversity, the Philippines is an active participant of the ASEAN Clearing House Mechanism (CHM). The ASEAN CHM is envisioned as a harmonized regional gateway of publicly available biodiversity information held by ASEAN member countries. However, the ASEAN CHM is still in its infancy stage and is not yet included in the GBIF network. There is yet to be a holistic, georeferenced and validated biodiversity information system on a national scale in the Philippines that aids decision-making and knowledge sharing.

A database of georeferenced species occurrence paves the way for visualization and higher forms of analysis like modeling the potential distribution of a species and ecological analysis (Beaman & Conn 2003). It can also serve practical conservation efforts like tracking plant species, protected area planning, and prioritizing resources for biodiversity surveys and specimen collections.

However, georeferencing can be tedious and suffers from major limitations as summarized by Guralnick et



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al. (2006): 1) It is slow; 2) The accuracy and precision of assigned coordinates are usually unknown; 3) A large fraction of available coordinates are inconsistent with locality information; 4) Materials and methods are poorly documented; and 5) many localities are georeferenced many times over with different results.

The development of georeferencing solutions which are automated, interoperable, and process-documented has long been sought by the scientific communities that rely on biological collections (Beaman & Conn 2003; Beaman et al. 2004; Graham 2004; Wieczorek et al. 2006). The same sentiment has given rise to various projects which aim to provide tools for batch processing of locality descriptions and protocols for georeferencing biodiversity information, (e.g. MaNIS, MapSteDI, NatureServe, ERIN, CONABIO, BioGoemancer) (Chapman & Wieczorek 2006).

This paper addresses the need to georeference the occurrence of threatened tree species in the Philippines based on locality descriptions, established species elevation range requirement, recent surface cover, administrative and protected area boundaries. It addresses the limitations cited by Guralnick et al. (2006) except for automated flow. Species occurrence information are taken from GBIF entries and legacy literature, primarily. GBIF was established by the governments in 2001 to encourage free and open access to biodiversity data. Through a global network of countries and organizations, GBIF promotes and facilitates the mobilization, access, discovery and the use of information about the occurrence of organisms over time and across planet. Legacy literature include the works of Elmer (1906-1908, 1908-1910, 1912-1913 and 1915-1919), Merill (1906, 1908, 1909, 1910, 1918, 1923-1926) and Merritt and Whitford (1906). Some occurrence reported in recent published literature of Abraham et al. (2010), De Guzman et al. (1986), Fernando et al. (2009), Fernando et al. (2008), Gruezo (2009), Hamann (2002), Lagenberger et al. (2006). Others are from the technical and biodiversity surveys of Garcia (2002) and Clemeno et al. (2005).The result is a georeferenced database of occurrence records of threatened forest species, each with a unique location per species and enough metadata directing the database user to the source of occurrence data.

OBJECTIVESThe study aims to 1) Georeference the occurrence of threatened tree species in the Philippines based on locality descriptions, established species elevation range requirement, recent surface cover, administrative and protected area boundaries; 2) Provide information that can be useful in determining which species should be prioritized for specimen or germplasm collection and

taxonomic identification; 3) Give information where and when tree species were surveyed; and, 4) Provide some insight to the possible distribution of forest formations in the Philippines by using indicator tree species occurrence as proxy.

MATERIALS AND METHODSThe method described below applied the point method in georeferencing locality descriptions. Every mention of a species occurrence assigned a unique location or a single coordinate pair.. No two locations are used twice for the same species to avoid duplicity. For instance a point feature is not a georeferenced representation of a locality, this method ignores the fact that a locality record always describes an area rather than a dimensionless point and that collecting may have occurred anywhere within the area denoted (Wieczorek et al. 2004). The method applied projection and geographic coordinates where point features and elevation and surface cover were used, respectively. The database followed the element occurrence (EO) concept (Gaul 1997) for representing rare, threatened or endangered species first used by the California Natural Diversity Database. An EO is defined as an abstraction describing an existing or historical species population or a subset of it. Each EO record contained both spatial and non-spatial attributes that represent the mappable position in space and the supporting source of a species occurrence, respectively.

The spatial attribute of each record consisted of an assigned point feature (latitude, longitude) and its corresponding location accuracy index (LAI). Each point feature may either be a georeferenced representation of a locality or the original location coordinates of a species (when available) converted to decimal degrees. The LAI referred to how well a point feature represented the true location where species were collected or observed. It was ranked from 1-5, 1 being the most accurate (Table 1).

Step 1: Search species and localities from sourcesAn initial list of forest tree species was selected from DAO 2007-01 based on the criteria of ecological and economic importance. This was composed of 57 vulnerable species (VU), 15 critically-endangered species (CR), 42 endangered species (EN), and 30 other threatened species (OTS). Also added to this initial list were 16 tree species listed under the 2003 IUCN Red List which we also considered ecologically and economically important trees. The total number of species in the initial list was 160.

All the plant specimen records in the GBIF portal under Phylum Magnoliophyta, which occur in the Philippines, were filtered and then downloaded as a spreadsheet.



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Table 1. Example of localities and corresponding LAI*.

LAI Definition Example of Locality

1 coordinate-specific as lifted from the source

Digallorin (16.8333333333°, 122.433333333°)

2 specific to a sitio, village, tractor or plot

Bicobian, Brgy Villa Imelda, Ilagan, IsabelaBrgy Bugsoc, Sierra Bullones 600 mPort Banga TractPlot 1, Brgy Maria Concepcion, Socorro, Mindoro Oriental

3 specific to mountain / protected area / watershed

Foothills of Mt Pangasugan, Leyte

Sibulan watershed, Polilio

4 specific to a town or city

Antipolo, Rizal

5 province Pangasinan

LAI* means location accuracy index and refers to how well a point feature represents the true location where species were collected or observed.

This spreadsheet was searched iteratively for matching scientific names of species and their localities.

Merrill’s Enumeration of Philippine Flowering Plants Vol I-IV (1923-1926) was the main source of species occurrence published in legacy literature. This was PDF-searched for every mention of a species‘ name. Earlier literature cited by Merrill were also PDF-searched for more fine-scaled locality descriptions like the one below:

“...the area lying between the Himugaan and Hitalon Rivers, where the surveys were made...The land is characterized by gentle slopes, with alternating ridges at the base of Mount Silay. The elevation ranges from 100 feet at points on the northern boundary to 1,200 feet at the highest point on the southern boundary. Small streams and arroyos are scattered profusely over the tract...” (Everett &Whitford 1906)

The same search for any mention of species was done for a selected number of journal articles, books, technical reports, and biodiversity surveys.

Step 2: Narrow down localityIf GBIF records of the same species included multiple mentions of reported localities in the same scale, then only one GBIF record was considered as an EO record. Preference was given to primary species information reported by GBIF over the species information provided by legacy literature, if both sources reported the same locality in the same scale.

Using a geographic information system (GIS), the reported locality from sources was queried from GADM (Database

of Global Administrative Areas) maps of the country from the provincial level to the village level.

If more than one locality pops up in the query-search, the correct locality was determined by cross-checking with the collector’s collecting localities and the date. This was done in two sub-steps. First, we referred to the National Herbarium Nederland’s online query of Cyclopaedia of Malesian Collectors (CMC) (http://www.nationaalherbarium.nl/fmcollectors/). If this was not enough, we reviewed the GBIF records under the same collector.

A good example of this was a specimen by Merrill in 1916 collected in the locality “Antipolo” (http://data.gbif.org/occurrences/250855958/). Our initial hypothesis was that this referred to Antipolo Town in Rizal Province because it was uncommon practice among collectors during that period to include the name of the village in the locality description. If one referred to contemporary administrative units in the Philippines with the name “Antipolo”, a query-search will result to 31 villages dispersed in more than 20 provinces and 1 city (i.e., Antipolo City in Rizal Province). The CMC did not positively state that Merrill collected in “Antipolo’ during 1916 but did state “vicinity of Manila”. The matter was concluded when, upon reviewing other GBIF records, a number of locality descriptions under Merrill’s 1916 collections specifically stated “Antipolo, Rizal”.

In the case where a query-search from GADM maps returned nothing, similar to cases wherein a place was actually a natural geographic feature (e.g. mountain, river), an internet search for any mention of a place was resorted to. An example of this was a specimen collected by Conklin in the locality “Yagaw.” The CMC informed that Yagaw is a mountain in Mindoro Island. In order to determine which specific place in Mt Yagaw Conklin collected from, we referred to his 1954 publication showing a village map of the Hanunoo Mangyan tribe where he conducted his study.

In the case of formerly larger provinces and districts that exist now as subdivided government units (e.g., Negros, Zamboanga, Agusan, Camarines, Davao, Lanao, Misamis, Cotabato, Mindoro, and Surigao), the province retaining the former capital was used. In the case of reported localities which had a different contemporary name, historical research was used (e.g., formerly Magallanes is now Magdiwang Town in Sibuyan Island, Romblon).

GBIF records were dropped from the compilation when we failed to georeference its reported locality due to ambiguity or lack of other information.



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Step 3: Narrow down further using elevation and surface coverWithin a locality, the area for assigning a point feature was further narrowed down using two parameters from remotely-sensed images, i.e., elevation and surface cover. Surface cover for year 2009 was used in georeferencing following the assumption that after a hundred years or more, the original sites of collection may have already been converted to other land uses, thus we looked for remnants of secondary and primary forests where there is high likelihood that threatened forest tree species can still be found.

Figure 1. Illustrative flow of the georeferencing process.

Firstly, a point feature for a locality should satisfy the species’ known elevation range based on literature. When a qualitative elevation description such as “low elevation forest” was given instead of a numeric range, we assumed that this meant elevations 500 masl and below. By “medium elevation forest,” we assumed 500 > 900 masl. Such qualitative elevation descriptions abound in many legacy literature, especially because in the early 20th century elevation was often measured inaccurately (MaPStedi 2004 as cited by Chapman & Wieczorek 2006). Elevation in meters was determined using Shuttle Radar Topographic Mission (SRTM v. 4) image (Jarvis et al. 2008; www.srtm.csi.cgiar.org). Next, this range was further confined to areas where secondary or primary forests exist using GlobCover 2009 v2.3 (http://ionia1.esrin.esa.int). Particularly used was Class 40: closed to open broadleafed evergreen semi-deciduous forest. Georeferencing for a large geographic scale, i.e. provincial boundaries, was made convenient by overlaying the country’s protected area maps in GIS.

Step 4: Limited validation using Google EarthLimited validation was done by cross checking in Google Earth if a point feature was indeed forested. From our experience, GlobCover 2009 v2.3 tended to non-differentiate forests from dense coconut plantations. A Google Earth placemark was then added to mark a location and its latitude and longitude in decimal degrees were included in the database. Each placemark preserved the hierarchy of administrative units and indicated an approximate elevation (e.g. Bicobian, Brgy Villa Imelda, Ilagan, Isabela 80 m).

Step 5: Georeference quality check and include metadataWhen a location coordinate was provided in the original data source, the given point feature is plotted in GIS and counter-checked if it was consistent with the reported locality and known elevation range. If an anomaly existed such as when a given coordinate actually fell on the sea or was unbelievably far from the reported locality, the original coordinate was discarded and the anomaly noted in the record. Although we also referred to reported elevations where species were collected or observed, more weight was given to the name of the locality rather than reported elevation.

In the case of reported localities which had a different contemporary name, its contemporary name was used to identify its associated Google Earth placemark. No two identical point features were used for the same species.

Each EO record contained the sources where either locality or location was based from. If additional information were used to georeference, this were also noted in the record. Additional metadata fields were also populated.

Step 1: Search species name and locality from sourcesExample: Hopea ocuminota - Cagayan, Ilocos Norte, Nueva Vizcaya, La Union, Pangasinan, Tarlac, Nueva Ecija, Bulacan, Laguna, Tayabas, Camarines, Albay, Sorsogon, Mindoro, Leyte, Mindanao: Misamis, Davao (Merrill, 1923-1926, Vol II).

GADM provincialboundary

GADM boundary+

SRTM v4+

GlobCover 2009

GADM boundary+

SRTM v4

GADMboundary

Village boundaryTown boundary

Step 2: Narrow down locality

Step 3: Narrow down further using elevation and surface cover

Step 4: Validate and add placemark in Google Earth

Step 5: Georeference quality check and include metada



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RESULTSVersion 1.0 of the database contained 2,067 EO records (Table 2) compiled from 40 sources and 312 GBIF records. It included 47 species across 13 families, constituting 29% of an initial listing of 160 threatened species. Each EO record contained several fields for metadata purposes (Table 4).

Table 3 summarized the number of point features while Fig. 2 showed their position in space. The total number of point features was 329 and was unevenly distributed across the archipelago. Six percent of these were found within the Northern Sierra Madre Natural Park (NSMNP), indicating that NSMNP was the most surveyed protected area in the country. The highest density of point features was found in the Polilio Islands in Quezon province, attributed mainly

Table 2. Species included in Version 1.0 of the database, including family, conservation status, and number of records per LAI.

Family Species DAO 2007-01

IUCN 2003 LAI =1 LAI =2 LAI =3 LAI =4 LAI=5 Total

Anacardiaceae Dracontomelon dao VU - 1 19 6 0 0 26Anacardiaceae Koordersiodendron pinnatum VU - 0 21 12 2 54 89Anacardiaceae Mangifera altissima VU VU 4 19 4 3 9 39Araucariaceae Agathis philippinensis VU VU 1 9 3 1 59 73Burseraceae Canarium luzonicum OTS VU 0 17 2 3 1 23Burseraceae Canarium ovatum OTS VU 0 15 3 2 9 29Cannabaceae Celtis luzonica - VU 2 18 0 0 59 79Dipterocarpaceae Dipterocarpus gracilis VU CR 1 21 12 8 33 75Dipterocarpaceae Dipterocarpus grandiflorus - CR 2 23 11 3 36 75Dipterocarpaceae Dipterocarpus hasseltii VU CR 5 2 5 5 6 23Dipterocarpaceae Dipterocarpus validus - CR 4 0 2 2 0 8Dipterocarpaceae Hopea acuminata CR CR 5 21 4 3 18 51Dipterocarpaceae Hopea foxworthyi CR VU 0 1 0 1 15 17Dipterocarpaceae Hopea malibato - CR 2 16 1 1 5 25Dipterocarpaceae Hopea plagata EN CR 0 2 0 2 15 19Dipterocarpaceae Parashorea malaanonan - CR 0 17 4 2 18 41Dipterocarpaceae Shorea almon VU CR 0 20 3 4 17 44Dipterocarpaceae Shorea astylosa CR CR 0 15 1 0 8 24Dipterocarpaceae Shorea contorta VU CR 12 36 15 9 17 89Dipterocarpaceae Shorea guiso - CR 7 25 10 7 34 83Dipterocarpaceae Shorea negrosensis VU CR 3 20 0 2 18 43Dipterocarpaceae Shorea palosapis - CR 4 24 2 3 24 57Dipterocarpaceae Shorea polysperma VU CR 10 26 5 9 30 80Dipterocarpaceae Vatica mangachapoi VU EN 5 21 3 10 26 65Dipterocarpaceae Vatica pachyphylla CR CR 0 19 0 2 1 22Ebenaceae Diospyros blancoi CR VU 1 15 5 5 66 92Ebenaceae Diospyros curanii VU - 0 3 4 6 13 26Ebenaceae Diospyros pilosanthera EN - 0 19 4 1 64 88Euphorbiaceae Macaranga bicolor - VU 4 18 1 2 18 43Euphorbiaceae Mitrephora lanotan - VU 1 17 0 3 14 35Fabaceae Adenanthera intermedia OTS VU 2 3 4 4 44 57Fabaceae Afzelia rhomboidea EN VU 4 26 8 25 36 99Fabaceae Kingiodendron alternifolium EN - 0 1 0 8 10 19Fabaceae Pterocarpus indicus forma indicus CR VU 1 19 6 2 10 38Fabaceae Sindora supa EN VU 1 2 0 5 7 15Fabaceae Wallaceodendron celebicum EN - 5 1 1 6 10 23Lauraceae Cinnamomum mercadoi VU VU 1 25 6 7 44 83Lauraceae Cinnamomum oroi EN - 1 1 0 0 0 2Meliaceae Aglaia edulis VU NT 6 0 0 0 0 6Meliaceae Aglaia rimosa VU NT 1 17 3 2 10 33

Table 2 continued next page



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Table 3. Summary of point features per LAI.LAI Number of point features %1 38 122 78 243 60 184 80 245 73 22Total 329 100

Table 4. Fields, field codes, and field descriptions used in the database.

Field Field code Description

Scientific name Species Scientific name excluding authors

Synonym Synonym includes heterotypic synonyms based from GBIF classification

Occurrence source Source May either be in the form Author,year,page; Author, year; or GBIF portal URL

Reported locality (location) Rep_loc May include name of province, town, village, sitio, mountain or watershed where species were collected or observed

Reported description Rep_desc May include field notes, distribution, qualitative elevation range, elevation in meters, endemicity

Point feature longitude Long Assigned longitude in decimal degrees

Point feature latitude Lat Assigned latitude in decimal degrees

Location accuracy index LAI In order of 1-5, 1 being most accurate

Additional georeferencing source Add_source Other information used for determining location

Placemark name KML_name Placemark name given as administrative unit or mountain name and its approximate elevation in meters

Georeference remarks Geof_rem Issues encountered in georeferencing like inconsistencies with given coordinates or changed names of localities

Georeferencer name Geof_by Geoferencer's name

Georeferencer institution Geof_insti Geoferencer's institution

Georeferencer email Geof_email Geoferencer's email

Georeference determined date Geof_date Format: MM-DD-YEAR

Version number Vers Database version number

Moraceae Artocarpus rubrovenius VU VU 2 15 1 0 0 18Meliaceae Aphanamixis polystachya VU VU 3 16 5 2 0 26Meliaceae Toona calantas CR - 1 0 7 4 27 39Moraceae Xanthostemon verdugonianus VU VU 0 1 3 4 3 11Sapotaceae Palaquium luzoniense VU VU 1 21 8 3 4 37Lamiaceae Tectona philippinensis CR EN 0 2 0 1 4 7Lamiaceae Vitex parviflora EN EN 0 4 2 7 58 71Total 103 653 176 181 954 2067% Total 4.98 31.6 8.51 8.76 46.2 100

Legend: EN – Endangered; CR – Critically endangered; VU – Vulnerable; OTS – Other Threatened Species; NT – Near Threatened



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Figure 2. Georeferenced species occurrences of threatened forest trees in the Philippines. A point feature corresponds to one or more occurrence by different species.



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to the surveys documented by Clements (2001). Forty percent of point features were found in the main island of Luzon. Although many expeditions were done in Mindanao and the Visayas Islands during the last hundred years, the localities where specimens were found were predominantly described under the name of provinces or an island group. These provinces and island groups eventually subdivided into smaller provinces, hence georeferencing localities with this condition tended to be conservative, the species occurrences underrepresented.

DISCUSSION

Tool for conservation and researchThe database is an indicator of how well a species is known based from species occurrences. For instance, Cinnamomum oroi is the least known species in the database, having been collected only in two localities: Kagaskas in Quezon Province in 1929 (http://data.gbif.org/occurrences/216518942/) and Calayan Island in Cagayan Province in 2005 (Clemeno et al. 2005). On the other extreme is Afzelia rhomboidea, a widely distributed species found in the Philippines and Southeast Asia.

This information can be useful in determining which species should be prioritized for specimen or germplasm collection and taxonomic identification.

This study paves the way for many museums particularly in the Philippines to start the process of georeferencing their collection data to provide network access to vast amounts of collection-based data and the tools to make sense of that data especially for some geospatial analyses.

The database is also convenient for visualizing species occurrence on a country scale and can also be used for historical spatial mapping. It contains 2 key identifiers: the scientific name that links a species’ taxonomic attributes and the placemark name that links a species to georeferenced locations in space. These two identifiers, in combination with herbaria records, can be used to extend the functionality of the database for historical mapping of specimen collections. Fig. 3a illustrates this for the species Shorea negrosensis, an endemic dipterocarp species, using GBIF records.

The database can also provide some insight to the possible distribution of forest formations in the Philippines by using indicator tree species occurrence as proxy. For instance, 3 species were used to illustrate this (Fig. 3b-3d). The spatial occurrence of Dipterocarpus grandiflorus

Figure 3. Map showing where and when Shorea negrosensis specimens have been collected in the Philippines in the last century based on GBIF.Fig. 3b, 3c, and 3d. Occurrence map of Dipterocarpus grandiflorus, Agathis philippinensis, and Vitex parviflora, respectively.



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is indicative of the occurrence of tropical lowland evergreen forest formations. The occurrence of Agathis philippinensis and Vitex parviflora meanwhile indicates tropical lower montane forest formations and forest over limestone formations, respectively (Fernando et al 2009).

If coupled with various environmental data as well as projected climate, the database serves as an integral component in spatially modeling the distribution of tree species and forests formations in the recent past as well as in a possible future scenario. With this output, the behavior of selected threatened species can be determined under different climate change scenarios.

Sources of uncertaintyThe user should be aware of the sources of uncertainty when using the database. Underscored here is the fact that the database is not a validated compilation of georeferenced species occurrences. Although we referred here to sources which explicitly state the occurrence of species in terms of latitude and longitude coordinates, i.e. LAI = 1, consideration should be given to the level of accuracy of location information during the time of specimen collection or observation, as well as to the sampling method. Field surveys are traditionally conducted either by plots which may have an area ranging from 10 m x 10 m to a hectare or by

transects which may stretch from a distance of 100 m to a kilometer. The method which we used utilized only a dimensionless point feature that neither captures extent in terms of length or area. As for EO records with LAI = 2 or higher, the point features therein are only estimates based on what is known about the species’ elevation range and recent surface cover, since it is impossible to determine where exactly a species occurred without access to the collector’s field notes. Other topographic parameters which may also be important such as aspect, slope or proximity to rivers were ignored in the georeferencing process. The datum of all reported locations were assumed to have a datum of WGS (World Geodetic System) 1984 eventhough the said ellipsoid is only applicable from 1984 up to present time.

CONCLUSION AND RECOMMENDATIONThis paper demonstrates that there is a rich source of species occurrence data that could provide useful information for research as well as practical conservation. However, there is still a wealth of data sources that the compilation was not able to include due to time constraint.

This paper echoes the need for a holistic, georeferenced, and validated biodiversity information system on a national



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scale that aids knowledge sharing and decision-making process. However, some challenges need to be addressed to achieve this. Foremost of this is digitized access. We resorted to using GBIF records particularly because local herbaria that house the largest specimen collections in the post-war years are still largely in analogue form. The next challenge is to develop methods for georeferencing occurrence data which is documented and interoperable, and ideally, semi-automated. The third challenge is the provision of a cyber-infrastructure and protocols to enable different institutions and individuals to contribute, verify and validate georeferenced species occurrence information. This paper only contributes to a small subset of solutions to these challenges. An effort like this on a national scale will probably take decades or so to fully complete. But the need to come up with a georeferenced database of threatened species occurrence becomes more imperative as human activities encroach more and more to fragile ecosystems which are fast disappearing.

ACKNOWLEDGEMENTSThe authors would like to thank the United States Agency for International Development (USAID) for funding support to the project. Invaluable assistance was also provided by the Philippine Tropical Forest Conservation Foundation (PTFCF) in sharing their PDF copies of botanical legacy literature and the Department of Environment and Natural Resources-Protected Area and Wildlife Bureau (DENR-PAWB) in sharing their map of protected areas. The views expressed here are of the authors and do not necessarily reflect those of the above-mentioned organizations.

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