Journal of Threatened Taxa |
www.threatenedtaxa.org | 26 November 2021 | 13(13): 20019–20032
ISSN 0974-7907 (Online) | ISSN 0974-7893
(Print)
https://doi.org/10.11609/jott.6904.13.13.20019-20032
#6904 | Received 18 November 2020 | Final
received 21 December 2020 | Finally accepted 15 October 2021
Patterns of forest cover loss in
the terrestrial Key Biodiversity Areas in the Philippines: critical habitat
conservation priorities
Bernard Peter O. Daipan
Department of Forest Biological
Sciences, College of Forestry, Benguet State University, La Trinidad, Benguet
2601, Philippines.
bp.daipan@bsu.edu.ph
Editor: Anonymity
requested. Date of
publication: 26 November 2021 (online & print)
Citation: Daipan,
B.P.O. (2021).Patterns of forest cover loss in
the terrestrial Key Biodiversity Areas in the Philippines: critical habitat
conservation priorities. Journal of Threatened Taxa 13(13): 20019–20032. https://doi.org/10.11609/jott.6904.13.13.20019-20032
Copyright: © Daipan
2021. Creative Commons Attribution
4.0 International License. JoTT allows unrestricted use, reproduction, and
distribution of this article in any medium by providing adequate credit to the
author(s) and the source of publication.
Funding: None.
Competing interests: The authors
declare no competing interests.
Author details: Bernard Peter O. Daipan is currently the department chairperson of the Forest Biological
Sciences (FBS) and research coordinator of the College of Forestry, Benguet
State University in the Philippines. He previously worked with the Conservation
and Development Division of the DENR–CAR for almost seven years before joining
the academy. At present, the author is
pursuing his PhD degree in Forestry Major in Forest Biological Sciences at the
University of the Philippines Los Baños (UPLB).
Acknowledgements: The author would like to
acknowledge the faculty and staff of the Department of Forest Biological
Sciences, College of Forestry-Benguet State University (BSU) for the inclusion
of this study in the College Research Agenda. Also, the author is very grateful
for the DENR-Cordillera Region, Birdlife International, Global Forest Watch, and
the QGIS team for the free accessible data and software. Finally, this study
would not have been possible without the immeasurable support of Ms. Sarah Jane
and Mr. Paul Isaac.
Abstract: The Philippines, home to over
20,000 endemic species of plants and animals, is facing a biodiversity crisis
due to the constant decrease of forest cover. The Key Biodiversity Area (KBA)
approach was developed to conserve species threatened with extinction using a
site-based conservation strategy to select globally important sites using
threshold-based criteria for species irreplaceability and vulnerability. This
study investigates the applicability of remotely sensed data through geospatial
analysis to quantify forest cover loss of the 101 terrestrial KBAs in the country
between 2001 and 2019. Results showed that the study sites had 4.5 million
hectares (ha) of forest in the year 2000. However, these sites have lost about
270,000 ha of forest in nearly two decades, marking a steady decline with an
annual deforestation rate of 14,213 ha per year in these terrestrial KBAs. The
majority of the study sites (58) had a high percentage of forest loss
(>3.13%), and these should be prioritized for conservation. By the year
2030, it is forecast that a total of 331 thousand ha of forest will be lost
unless there is a transformational change in the country’s approach to dealing
with deforestation. The results of this study provide relevant data and
information in forest habitat in near real-time monitoring to assess the impact
and effectiveness of forest governance and approaches within these critical
habitats.
Keywords: Deforestation, forest habitat,
geospatial technology, KBA.
Abbreviations: AZE—Alliance for Zero Extinction
| DENR—Department of Environment and Natural Resources | GIS—Geographic
Information System | IUCN—International Union for the Conservation of Nature |
KBA—Key Biodiversity Area | UNEP—United Nations Environment Programme.
Introduction
Forests are home to over 80% of
the earth’s terrestrial biodiversity (Aerts & Honnay 2011), including almost half of all avian species
(Hilton-Taylor et al. 2009). Forests provide many ecosystem services that
include conservation of threatened and endemic species (Gibson et al. 2011).
However, these forests have undergone remarkable pressure (Drummond &
Loveland 2010) over the past decades, leading to a global biodiversity crisis
(Driscoll et al. 2018) which is even worse than climate change (University of
Copenhagen 2012). There is no doubt that habitat loss, caused by the conversion
of forest to non-forest land uses such as agricultural and built-up areas, is
the predominant threat to biodiversity (Foley et al. 2005; Estavillo
et al. 2013). As a result, many endemic species have either become extinct or
threatened with extinction (Brooks et al. 2002). In the Philippines, there are
more than 20,000 endemic species of plants and animals (Mittermeier et al.
1998; Conservation International Philippines 2020) and the country is home to
20% of all known flora and fauna species (Ambal et
al. 2012). This mega-diverse country has long been recognized as one of the top
biodiversity hotspots in the world (Gaither & Rocha 2013) due to the
constant exploitation and destruction of its forest resources. This habitat
destruction can generate zoonotic diseases (UNEP 2020), such as COVID-19 that
caused a worldwide pandemic (Cucinotta & Vanelli 2020). Biodiversity also protects humans against
infectious disease (Wood et al. 2014; Levi et al. 2016)
To this end, the Key Biodiversity
Area (KBA) approach was developed. This site-based conservation approach is
considered one the most effective means to halt biodiversity loss on global and
regional scales (Eken et al. 2004; UNEP-CBD 2010).
The KBAs are promoted by the International Union for the Conservation of Nature
(IUCN) to identify and delineate important sites for the global persistence of
biodiversity as manageable units (IUCN 2016; Kulberg
et al. 2019), using standard criteria based on the concepts of species
irreplaceability and vulnerability (Langhammer et al.
2007; Melovski et al. 2012).
In the Philippines, the
identification and delineation of KBAs was initiated by Conservation
International Philippines (CIP), the Biodiversity Management Bureau (BMB),
formerly Protected Areas and Wildlife Bureau (PAWB), of the Department of
Environment and Natural Resources (DENR), and the Haribon
Foundation supported by Critical Ecosystem Partnership Fund (CEPF) (CIP et al.
2006). It was started in the country to support the government and other
stakeholders in prioritizing and mainstreaming conservation efforts and
formulating site-based strategies that protect these vulnerable and
irreplaceable species within their habitats (Edgar et al. 2008).
A total of 228 KBAs were
identified and delineated in the Philippines, which cover over 106,000 km2,
around 35% of the total land area of the country. The ecosystem coverage of
these KBAs includes the following: terrestrial only with 101 KBAs (51,249 km2);
marine only with 77 KBAs (19,601 km2); and combinations of terrestrial
and marine with 50 KBAs (35,702 km2). These KBAs are home to over
855 species, 396 of these are globally threatened species, 398 are considered
restricted-range species, and 61 are congregatory
species of birds (CIP et al. 2006; Ambal et al. 2012;
FPE, 2020).
Hence, there is an urgent need
for effective conservation and management of the remaining forest habitats of
these threatened species in the country. One of the essential management
strategies is through near real-time monitoring of the temporal and spatial
trend of forest cover loss in these KBAs to investigate which critical habitats
are more vulnerable to future degradation (Leberger
et al. 2019), to identify biodiversity threats, to develop appropriate
management interventions such as forest protection and reforestation, and
evaluate its effectiveness (Jones et al. 2013). With the advent of remote
sensing technology over the last decade, it is now possible to monitor spatial
and temporal patterns of forest cover losses on a global scale using high-resolution
satellite imaging (Buchanan et al. 2011; Hansen et al. 2013; Turner et al.
2003). Using remotely sensed data for forest monitoring will effectively
contribute to the conservation and management of these habitats. Also, it has
the potential to assess the impact of site-based policy implementation (Leberger et al. 2019).
This study aimed to quantify the
spatial and temporal forest cover loss of the terrestrial KBAs in the
Philippines between 2000 and 2019 using high-resolution satellite imaging of
forest loss produced by Hansen et al. (2013). Also, it aimed to aid in
monitoring efforts and identify the most critical terrestrial KBAs with the
highest loss of forest cover - including percent loss - that need immediate
intervention. A conservation priority ranking was created based on the annual
rate of deforestation, which will demonstrate the applicability of the results
of this study in forest monitoring of these sites. Finally, forecasting of the
future trend of forest cover loss in these critical habitats was performed as
well.
Material
and methods
Study Area
This study was conducted in 101
identified terrestrial KBAs across the 17 regions of the Philippine archipelago
with a total area of 51,298.34 km2 (Image 1) from June to October
2020. The 50 KBAs, with combined terrestrial and marine areas, were not
included in the study because there is a need to delineate first the boundaries
between the terrestrial and marine realms of the KBA prior to the computation
of percentage forest cover of the KBA. If the boundaries will not be
delineated, the marine portion of the KBA will be treated as non-forested areas
and this will result in a very low percentage of forest cover although the
terrestrial portion has a high percentage of forest cover. Due to the
unavailability of the delineated realms of the 50 KBAs, the study was only
limited to 101 terrestrial KBAs.
The Philippines, with more than
7,000 islands, is geographically located in the western Pacific Ocean and part
of the southeastern Asian region which is among the
biodiversity hotspots in the world with the highest concentration of
terrestrial vertebrate species on the planet. According to the Foundation for
the Philippine Environment (FPE) (2020), these terrestrial KBAs in the country
represent several types of forest ecosystems across different elevations,
namely; sub-alpine forest, mossy forest, montane forest (upper and lower), pine
forest, semi-deciduous forest (moist deciduous), lowland evergreen forest,
forest over limestone (karst), forest over ultrabasic soil, forest over
ultramafic rocks, beach forest, and mangrove forest.
Data
Terrestrial key biodiversity
areas shapefile
To investigate the spatial and
temporal forest cover loss within the study sites, the vector maps in shapefile
(.shp) format of the KBAs were requested from the
world database of Key Biodiversity Areas developed and maintained by BirdLife International (2020). After extracting the spatial
data of terrestrial KBAs in Geographic Information System (GIS) software, the
maps were compared with the web-based Philippine KBA maps using the Geoportal
Philippines (2020). Based on the comparative assessment, 21 of the 101
terrestrial KBAs were observed to have notable inconsistencies in terms of area
and its boundaries. Nonetheless, the 21 terrestrial KBA boundaries from the
Geoportal Philippines along with the 80 terrestrial KBAs without discrepancies
from Birdlife International were selected and used in the analysis of this
study, which represents the best sites for biodiversity conservation.
Hansen global forest change
2000–2019 version 1.7
The main dataset in quantifying
the spatial and temporal loss in forest cover of the terrestrial KBAs in the
Philippines, including the initial forest cover dataset for the year 2000, is
the high-resolution global maps of 21st century forest cover change
developed by Hansen et al. (2013). The product used in this study was version
1.7, which is the result of time-series analysis of Landsat data at a spatial
resolution of one arc-second per pixel (30m x 30m) depicting forest extent and
change such as loss (forest to non-forest) and gain (non-forest to forest
state) during the period 2000 to 2019. These data are updated annually based on
a high-end remote sensing technology and can be freely downloaded from the
University of Maryland - Global Land Analysis and Discovery (UM-GLAD) website
as raster data. The data can also be downloaded and visualized from the Google
Earth Engine (GEE) data repository.
Geospatial processing and
statistical analysis of forest cover loss
The software used to quantifying
and process yearly forest cover loss of each terrestrial KBA was the Quantum
Geographic Information System (QGIS) version 3.14 (pi). The KBA shapefiles were
used in clipping the downloaded raster format of forest loss. After clipping,
the raster datasets were converted to vector for an easier geostatistical
calculation such as area determination. To facilitate the editing of the
attribute data, the vector of forest cover loss was split into individual shapefiles
following each KBA boundary. Finally, the area in hectares for annual forest
loss per terrestrial KBA, between the periods 2001 and 2019, were calculated
using the built-in calculate geometry tool. The general overview of the
methodology is presented in Figure 1.
The total forest cover loss or
the area change, percentage area change, and the annual rate of forest cover
loss were computed using the following mathematical formulas by Hansen et al.
(2013) which were also used in the study of Sulieman et al. (2017):
ΔA = A2 – A1
where:
ΔA = forest cover loss or change
in the area
A1 = beginning of the period
(date 1)
A2 = end of the period (date 2)
PAC = ΔA/TA X 100
where:
PAC = percentage area change
TA = the total area of KBA
ARC = ΔA/N
where:
ARC = Annual rate of change
(ha/year)
N = the number of years between
date one and date two of the study period
The percentage of forest cover
loss was categorized from low to high which is adapted from the study of Leberger et al. (2019). The forecasting of the future trend
of forest cover loss from 2020 to 2030 was performed using the forecasting
function in MS Excel based on the existing historical forest loss values.
Results
Spatial and temporal forest cover
loss
The forest cover of the
identified terrestrial KBAs in the Philippines was estimated at around 4.5
million ha in the year 2000, which represents 89% of the total terrestrial KBA
area (Image 2). However, after almost two decades, the forest cover of these
terrestrial KBAs, based on the GIS analysis of high-resolution remotely sensed
data developed by Hansen et al. (2013), had decreased by around 270,000 ha,
which is almost 6% of the total forest cover in the year 2000. It is estimated
that the remaining forest cover within these terrestrial KBAs as of 2019 is around
81% with an area of 4.27 million ha. Moreover, the annual rate of forest cover
loss for these priority areas for biodiversity conservation is computed at
around 14,213 ha/year with an annual average deforestation rate of 6% (Image
3).
The scatter plot shows an
increasing trend in the annual forest cover loss from 2001 to 2019. The period
with the highest recorded rate of deforestation was between 2016 and 2017, but
on a positive note, there has been a notable decrease of these losses in the
last two consecutive years (2018 and 2019) (Figure 2).
The 10 terrestrial KBAs with the
highest percentage of forest loss between 2000 and 2019, except for the KBAs
with lake environments (Malasi Lake and Mungao Lake), are presented in Table 1. The percentage of
forest loss was highest in Tawi-tawi Island, located
in Bangsamoro Autonomous Region in Muslim Mindanao (BARMM) with 27.88%. Based
on the percentage frequency distribution presented in Table 2, the majority of
the study sites (58) had a high percentage of forest loss with more than 3.13%.
On the other hand, only three (3) among the 101 terrestrial KBAs had low
percentage of loss, these are Timpoong and Hibok-hibok Natural Monument in Region 10, Mounts Banahaw and San Cristobal Protected Landscape in Region 4A,
and Mount Kitanglad in Region 10, with 0.31%, 0.27%,
and 0.24%, respectively.
The KBA with the highest net loss
of forest area in nearly two decades was Bislig,
located in Region 13 covering some portion of Region 11, which was around 38.5
thousand ha (Table 3), while the Timpoong and Hibok-hibok Natural Monument had the lowest area of forest
loss (except for KBAs with lake environment) with only 10.59 ha in two decades.
Moreover, the Bislig KBA also had the highest annual
rate of deforestation with a loss of 2,031 hectares per year (ha/year). This
was followed by Mount Mantalingahan in Region 4B and
Samar Island Natural Park in Region 8, with 1,266 ha/year and 738.82 ha/year
forest loss, respectively (Table 4). The conservation priority ranking of the
101 terrestrial KBAs, ranked in terms of forest cover loss and the annual rate
of deforestation, is presented in Appendix 1. This also includes relevant
information such as the region and area of KBAs, forest cover and percent
forest cover in the year 2000 and 2019, and percent forest cover loss.
Discussion
Quantification of spatial and
temporal forest cover loss using Hansen remotely sensed data
In the Philippines, the use of
remote sensing for annual forest cover monitoring and loss detection in
terrestrial KBAs, even on the national scale, is not yet fully developed
compared to other tropical countries like Brazil (Instituto Nacional de Pesquisas Espaciais 2010) and
India (Forest Survey of India 2019). Thus, remotely sensed satellite imagery,
such as the dataset produced by Hansen et al. (2013), can contribute
significantly to biodiversity monitoring (Tracewski
et al. 2016). However, errors are inevitable for these datasets, for example,
forest loss estimation in dry forests may be underestimated, as reported by Achard et al. (2014), but are working well enough in moist
humid forest. Also, the accuracy assessment conducted by Mitchard
et al. (2015) in Ghana showed a significant underestimation of forest change.
Another limitation in the dataset is that it does not distinguish permanent
deforestation from temporary forest disturbance like forest fires, forestry
plantations, and shifting cultivation (Curtis et al. 2018). Nevertheless, the
overall accuracy of forest cover loss of Hansen GFC dataset as shown in different
studies is between 88% (Feng et al. 2016) to 93% (Hirschmugl
et al. 2020) and it represents the best high-resolution, with 30m x 30m spatial
resolution, global assessment of forest cover change that is freely accessible
to the public (Hansen et al. 2010; Tracewski et al.
2016).
Critical habitat conservation
priorities
The Tawi-tawi
Island, identified in this study with the highest percent forest loss (27.88%)
among the terrestrial KBAs, was also recognized as one of the Alliance for Zero
Extinction (AZE) sites (AZE 2010) that holds two critically endangered (CR)
species and one endangered (EN) species (IUCN 2008). The AZE sites are those
that have threatened species constrained to just a single site globally (AZE
2010). Also, this KBA has 45 trigger species identified (Odevillas
2018). Trigger species are those that trigger either the irreplaceability
criterion or vulnerability criterion within the KBAs (Langhammer
et al. 2007), these could also be identified by combining both the endemism and
rarity criteria (Yahi et al. 2012). Based on the
findings of this study, 58 sites recorded a high percentage forest loss which
suggests that these areas should be prioritized in terms of forest conservation
and protection. It is also advisable that the strategies and good practices in
forest conservation of the three (3) sites with the lowest percentage of forest
loss should be adapted to other sites of this study.
The second site with the highest percent
forest loss, which also had the highest annual deforestation rate, and with the
largest area of forest cover loss within the study period is the Bislig KBA in Region 13 (Image 4). This terrestrial KBA has
33 trigger species and one (1) critically endangered species based on the data
from the Haribon Foundation (2020) and red list of
threatened species (IUCN 2008).
Mount Mantalingahan
in Region 4B, with a total of 24,071.86 ha of forest cover loss between 2001
and 2019 and an annual deforestation rate of 1,266 ha/year, has one (1)
endangered species, one (1) vulnerable species (Ambal
et al. 2012), and 38 trigger species (Odevillas
2018). Although this KBA was already removed from the AZE list in 2010 after
the Palawanomys furvus
was reclassified as Data Deficient from Endangered (EN) species in 2008 (Ambal et al. 2012), the threat to biodiversity remains.
This is mainly due to its high annual rate of forest cover loss as observed in
this study.
The Samar Island National Park in
Region 8, which ranked third in this study with the highest rate of forest
cover loss, was also identified as a top priority site for protection due to
its large number of trigger species with 180 species in total, and three (3)
critically endangered species (Odevillas 2018). These
findings suggest that the aforementioned terrestrial KBAs are more likely to
experience species extinction in the coming decades without proper conservation
and protection measures.
Status and trends of forest cover
in the terrestrial key biodiversity areas in the Philippines
The identified terrestrial KBAs
in the Philippines cover at least 17% of the estimated total land area of the
country (30 million ha) and were declared as “critical habitats” under the
Presidential Executive Order 578 in 2006. However, these sites alone are not
enough for biodiversity conservation (FAO & UNEP 2020) especially in a
country regarded as one of the top global biodiversity hotspots (Mittermeier et
al. 1998). Therefore, an expansion of these habitats is necessary to increase
conservation coverage of the threatened species (Kullberg et al. 2019). Also,
there are only 27 protected terrestrial KBAs, 25 are partially protected, while
the remaining 49 are unprotected or not covered with any legislative
interventions (Ambal et al. 2012), which make these
areas more vulnerable to anthropogenic deforestation that has a remarkable
effect on forest cover (Margono et al. 2014).
However, even a protected KBA is still vulnerable to land cover conversion for agro-industrial use, as observed in the buffer zones of
Mount Kalatungan (Azuelo
& Puno 2018).
As reported by the DENR (2000) in
its 2000 Philippine Forestry Statistics (PFS), the country’s forest cover was
around 5.4 million ha in the year 2000 (18% of the total land area), which
implies that 83% of these forests were found in the terrestrial KBAs. Although
forest cover increased in the country between 2000 and 2015, with an estimated
area of seven million ha or a 22% increase (DENR 2019), a consistent decline in
the forest cover of these terrestrial KBAs was detected in this study within
the same period. The decline in forest cover in the country is also reported by
Mongabay (2020) based on deforestation statistics
stating that a total of 1,128,788 ha of forest was lost between 2001 and 2018.
Globally, the rates of forest cover loss in Important Birds and Biodiversity
Areas (IBAs) were highest in South America and southeastern
Asia (Tracewski et al. 2016), which includes the
Philippines. This indicates that the country’s efforts in managing and
protecting these critical habitats, as well as the existing environmental
protection measures, are seriously inadequate (Oliver & Heaney 1996;
Hammond 1997) due to the constant rate of deforestation and forest degradation
within these areas, which are generally caused by logging, mining, and land
conversion (from forest to non-forest) (Lillo et al. 2018). Although a
promising finding was observed in the last two periods (2018 & 2019) due to
the substantial decreased in the forest cover loss, there is still a need for
annual forest cover loss monitoring to identify and evaluate the impact of
policy and conservation interventions in the spatial and temporal forest cover
loss in these areas (Broich et al. 2011).
Since the forest cover loss of
the study sites exhibited an increasing trend, with a similar pattern of
results obtained in the study of Leberger et al.
(2019) on a global scale, it is predicted in this study that by the end of 2030
an area of approximately 331,000 ha of forest will be lost, equivalent to
around 7.3% of the total forest cover in these sites (Figure 3). This immense
decline in forest will leave these critical habitats with only 76% remaining
cover, and in turn escalate the threat to the 25 Critically Endangered (CR), 40
Endangered (EN), and 117 Vulnerable (VU) species (Ambal
et al. 2012) found in these sites. Unless there is a transformational change in
the way the country manages and conserves its forests and biodiversity (FAO
& UNEP 2020) through these terrestrial KBAs, extinction of species is
imminent. For that reason, there is an undeniable need for near real-time
monitoring of forest loss within these areas (Leberger
et al. 2019), and ranking/prioritizing them for conservation based on
vulnerability to degradation (Brooks et al. 2006).
Conclusion
The present study quantified the
spatial and temporal pattern of forest cover loss in 101 terrestrial key
biodiversity areas of the Philippines between the periods 2001 and 2019 using
high-resolution satellite-based earth observation datasets. Remote sensing
technology and geospatial analysis have a high potential for timely monitoring
of the forest cover status of these habitats, an essential component of
biodiversity conservation. The increasing trend of forest loss in the
terrestrial KBAs, as observed in this study, with an annual deforestation rate
of about 14,213 ha per year, clearly suggests that the efforts in the
conservation of these critical habitats need recalibration. Thus a paradigm
shift is necessary to manage these sites in an attempt to prevent the
extinction of 182,000 species or at least improve their conservation status.
There is also a need to expand the terrestrial KBAs in the country taking into
consideration the threatened species of vascular plants since the
identification and delineation of terrestrial KBAs was only based on some
faunal taxonomic groups, such as amphibians, reptiles, birds, and mammals.
Table 1. Top ten KBAs with the
highest percent forest loss between 2001 and 2019.
Region |
Terrestrial Key Biodiversity
Areas |
% Forest Cover Loss |
BARMM |
Tawi-tawi Island |
27.88 |
XIII, XI |
Bislig |
25.75 |
IX |
Mount Sugarloaf |
19.24 |
IV-B |
Mount Mantalingahan |
17.14 |
IX |
Lituban-Quipit Watershed |
14.98 |
IV-B |
Malpalon |
13.01 |
IV-B |
San Vicente-Roxas
Forests |
11.96 |
XI |
Mount Agtuuganon
and Mount Pasian |
11.76 |
IV-B |
Mount Calavite |
11.49 |
IX |
Mount Dapiak
and Mount Paraya |
11.11 |
Table 2. Percentage frequency
distribution of forest loss in the study sites.
Classification |
Percentage of forest loss |
Frequency |
Low |
0–0.76 |
3 |
Moderate |
0.77–3.13 |
40 |
High |
>3.13 |
58 |
Total |
|
101 |
Table 3. Top ten sites with the
highest forest loss between 2001 and 2019.
Region |
Terrestrial Key Biodiversity
Areas |
Forest cover loss (ha) |
XIII, XI |
Bislig |
38,589.02 |
IV-B |
Mount Mantalingahan |
24,071.86 |
VIII |
Samar Island Natural Park |
14,037.57 |
CAR, II, I |
Apayao Lowland Forest |
12,384.94 |
XIII |
Mount Diwata
Range |
10,146.78 |
XI |
Mount Agtuuganon
and Mount Pasian |
9,989.77 |
XIII |
Mount Hilong-hilong |
9,842.84 |
II |
Quirino Protected
Landscape |
9,610.57 |
IV-B |
San Vicente-Roxas
Forests |
9,221.44 |
IV-B |
Victoria and Anepahan Ranges |
8,742.57 |
Table 4. Top ten sites with the
highest annual rate of deforestation.
Region |
Terrestrial Key Biodiversity
Areas |
Annual rate of forest cover
loss (ha/year) |
XIII, XI |
Bislig |
2,031.00 |
IV-B |
Mount Mantalingahan |
1,266.94 |
VIII |
Samar Island Natural Park |
738.82 |
CAR, II, I |
Apayao Lowland Forest |
651.84 |
XIII |
Mount Diwata
Range |
534.04 |
XI |
Mount Agtuuganon
and Mount Pasian |
525.78 |
XIII |
Mount Hilong-hilong |
518.04 |
II |
Quirino Protected
Landscape |
505.82 |
IV-B |
San Vicente-Roxas
Forests |
485.34 |
IV-B |
Victoria and Anepahan Ranges |
460.14 |
For
figures & images - - click here
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Appendix 1. Conservation priority
ranking of the 101 terrestrial key biodiversity areas based on the annual rate
of forest cover loss.
Region |
Terrestrial Key Biodiversity Areas |
Area of KBA* |
Forest cover in 2000* |
% Forest cover in 2000 |
Remaining forest cover in 2019* |
% forest cover in 2019 |
Forest cover loss* |
% Forest cover loss |
Annual rate of forest loss (ha/year) |
Priority ranking |
XIII, XI |
Bislig |
154.12 |
149.85 |
97 |
111.26 |
72 |
-38.59 |
-25.75 |
-2031.00 |
1 |
IV-B |
Mount Mantalingahan |
146.00 |
140.42 |
96 |
116.35 |
80 |
-24.07 |
-17.14 |
-1266.94 |
2 |
VIII |
Samar
Island Natural Park |
333.00 |
330.24 |
99 |
316.20 |
95 |
-14.04 |
-4.25 |
-738.82 |
3 |
CAR, II, I |
Apayao Lowland Forest |
177.37 |
171.43 |
97 |
159.04 |
90 |
-12.38 |
-7.22 |
-651.84 |
4 |
XIII |
Mount Diwata Range |
93.80 |
92.08 |
98 |
81.94 |
87 |
-10.15 |
-11.02 |
-534.04 |
5 |
XI |
Mount Agtuuganon and Mount Pasian |
85.50 |
84.92 |
99 |
74.93 |
88 |
-9.99 |
-11.76 |
-525.78 |
6 |
XIII |
Mount Hilong-hilong |
240.24 |
237.66 |
99 |
227.81 |
95 |
-9.84 |
-4.14 |
-518.04 |
7 |
II |
Quirino Protected Landscape |
164.54 |
149.48 |
91 |
139.87 |
85 |
-9.61 |
-6.43 |
-505.82 |
8 |
IV-B |
San
Vicente-Roxas Forests |
81.16 |
77.11 |
95 |
67.88 |
84 |
-9.22 |
-11.96 |
-485.34 |
9 |
IV-B |
Victoria
and Anepahan Ranges |
164.79 |
163.46 |
99 |
154.72 |
94 |
-8.74 |
-5.35 |
-460.14 |
10 |
XIII, X |
Mount Kaluayan-Mount Kinabalian
Complex |
180.98 |
180.99 |
100 |
172.26 |
95 |
-8.73 |
-4.82 |
-459.62 |
11 |
XI |
Mount Kampalili-Puting Bato |
169.91 |
166.94 |
98 |
158.89 |
94 |
-8.04 |
-4.82 |
-423.23 |
12 |
BARMM, XII |
Mount Piagayungan and Butig Mountains |
154.34 |
148.39 |
96 |
140.73 |
91 |
-7.66 |
-5.16 |
-403.09 |
13 |
IV-B |
Cleopatras Needle |
104.73 |
102.30 |
98 |
95.76 |
91 |
-6.55 |
-6.40 |
-344.64 |
14 |
IX |
Mount
Sugarloaf |
34.42 |
32.73 |
95 |
26.43 |
77 |
-6.30 |
-19.24 |
-331.44 |
15 |
XI, XII |
Mount Latian complex |
95.08 |
87.45 |
92 |
82.40 |
87 |
-5.04 |
-5.77 |
-265.45 |
16 |
IX |
Lituban Quipit Watershed |
33.29 |
32.64 |
98 |
27.75 |
83 |
-4.89 |
-14.98 |
-257.23 |
17 |
XIII |
Agusan Marsh Wildlife Sanctuary |
54.77 |
49.20 |
90 |
44.94 |
82 |
-4.26 |
-8.66 |
-224.33 |
18 |
XII |
Mount Busa-Kiamba |
114.14 |
106.07 |
93 |
102.38 |
90 |
-3.68 |
-3.47 |
-193.74 |
19 |
VI, VII |
Southwestern
Negros |
196.44 |
83.91 |
43 |
80.46 |
41 |
-3.45 |
-4.11 |
-181.36 |
20 |
III, I |
Zambales
mountains |
139.68 |
118.49 |
85 |
115.05 |
82 |
-3.44 |
-2.91 |
-181.19 |
21 |
IV-A, III |
Mounts
Irid-Angilo and Binuang |
115.21 |
114.08 |
99 |
110.71 |
96 |
-3.37 |
-2.95 |
-177.15 |
22 |
XI, XII |
Mount Apo |
99.08 |
85.68 |
86 |
82.48 |
83 |
-3.21 |
-3.74 |
-168.80 |
23 |
X |
Mount Tago Range |
83.42 |
68.33 |
82 |
65.22 |
78 |
-3.10 |
-4.54 |
-163.34 |
24 |
X, BARMM |
Munai/Tambo |
69.84 |
65.39 |
94 |
62.62 |
90 |
-2.77 |
-4.24 |
-145.95 |
25 |
VIII |
Anonang-Lobi Range |
58.05 |
56.98 |
98 |
54.34 |
94 |
-2.63 |
-4.62 |
-138.51 |
26 |
II, III |
Casecnan Protected Landscape |
90.72 |
82.07 |
90 |
79.96 |
88 |
-2.11 |
-2.57 |
-111.05 |
27 |
IV-B |
Puerto Galera |
37.31 |
32.33 |
87 |
30.54 |
82 |
-1.79 |
-5.54 |
-94.29 |
28 |
XII, BARMM |
Mount Daguma |
32.36 |
31.02 |
96 |
29.36 |
91 |
-1.65 |
-5.33 |
-87.09 |
29 |
IV-A |
Polillo Islands |
20.28 |
19.95 |
98 |
18.35 |
91 |
-1.60 |
-8.01 |
-84.11 |
30 |
IV-B |
Iglit-Baco Mountains |
56.30 |
47.19 |
84 |
45.61 |
81 |
-1.58 |
-3.35 |
-83.20 |
31 |
IV-B |
Mount Calavite |
18.15 |
13.50 |
74 |
11.94 |
66 |
-1.55 |
-11.49 |
-81.61 |
32 |
IV-B |
Malpalon |
14.09 |
11.86 |
84 |
10.32 |
73 |
-1.54 |
-13.01 |
-81.23 |
33 |
BARMM |
Tawi-tawi Island |
5.85 |
5.53 |
94 |
3.99 |
68 |
-1.54 |
-27.88 |
-81.11 |
34 |
IX |
Mount Dapiak-Mount Paraya |
14.67 |
13.57 |
92 |
12.06 |
82 |
-1.51 |
-11.11 |
-79.35 |
35 |
BARMM, XII |
Liguasan marsh |
39.42 |
18.10 |
46 |
16.65 |
42 |
-1.45 |
-8.01 |
-76.35 |
36 |
III |
Aurora
Memorial National Park |
47.15 |
42.34 |
90 |
40.91 |
87 |
-1.42 |
-3.36 |
-74.83 |
37 |
VI |
Central
Panay mountains |
105.58 |
94.56 |
90 |
93.27 |
88 |
-1.29 |
-1.36 |
-67.67 |
38 |
III, II |
North
Central Sierra Madre Mountains |
87.48 |
86.21 |
99 |
85.01 |
97 |
-1.20 |
-1.39 |
-62.92 |
39 |
VI |
Mount Silay and Mount Mandalagan
(Northern Negros) |
68.88 |
45.21 |
66 |
44.06 |
64 |
-1.16 |
-2.56 |
-60.85 |
40 |
IV-B |
Lake Manguao |
6.45 |
5.32 |
82 |
4.18 |
65 |
-1.14 |
-21.46 |
-60.05 |
41 |
VIII |
Mount Nacolod |
33.49 |
32.80 |
98 |
31.67 |
95 |
-1.14 |
-3.47 |
-59.88 |
42 |
IV-B |
Mount Halcon |
50.95 |
44.43 |
87 |
43.30 |
85 |
-1.13 |
-2.55 |
-59.64 |
43 |
XI |
Mount Hamiguitan (Tumadgo peak) |
31.88 |
31.27 |
98 |
30.19 |
95 |
-1.08 |
-3.45 |
-56.69 |
44 |
IV-B |
Busuanga Island |
16.33 |
15.94 |
98 |
14.90 |
91 |
-1.04 |
-6.55 |
-54.99 |
45 |
X, IX |
Mount Malindang |
40.69 |
37.11 |
91 |
36.22 |
89 |
-0.90 |
-2.41 |
-47.16 |
46 |
IV-A |
Taal Volcano
Protected Landscape |
65.93 |
31.98 |
49 |
31.10 |
47 |
-0.88 |
-2.76 |
-46.48 |
47 |
IV-B |
Mount Hitding |
17.77 |
16.56 |
93 |
15.70 |
88 |
-0.87 |
-5.24 |
-45.67 |
48 |
IV-B |
Mount Siburan |
11.57 |
9.53 |
82 |
8.68 |
75 |
-0.86 |
-9.00 |
-45.18 |
49 |
XIII |
Mount Kambinlio and Mount Redondo |
28.52 |
27.07 |
95 |
26.27 |
92 |
-0.80 |
-2.95 |
-41.97 |
50 |
VII |
Mount Capayas |
13.61 |
10.44 |
77 |
9.66 |
71 |
-0.78 |
-7.48 |
-41.07 |
51 |
VII, VI |
Ban-ban |
28.54 |
16.13 |
57 |
15.39 |
54 |
-0.74 |
-4.60 |
-39.07 |
52 |
VII |
Central
Cebu Protected Landscape |
29.22 |
19.52 |
67 |
18.79 |
64 |
-0.73 |
-3.73 |
-38.27 |
53 |
VII |
Cuernos de Negros |
23.56 |
21.34 |
91 |
20.63 |
88 |
-0.71 |
-3.33 |
-37.41 |
54 |
XII |
Mount Matutum |
18.89 |
11.82 |
63 |
11.13 |
59 |
-0.69 |
-5.84 |
-36.35 |
55 |
III |
Mount Dingalan |
46.89 |
45.93 |
98 |
45.25 |
97 |
-0.67 |
-1.47 |
-35.49 |
56 |
CAR |
Balbalasang-Balbalan National Park |
81.54 |
77.79 |
95 |
77.12 |
95 |
-0.67 |
-0.86 |
-35.26 |
57 |
V |
Catanduanes Watershed Forest Reserve |
28.24 |
28.00 |
99 |
27.33 |
97 |
-0.67 |
-2.39 |
-35.18 |
58 |
IV-B |
Balogo watershed |
10.50 |
9.38 |
89 |
8.74 |
83 |
-0.63 |
-6.76 |
-33.35 |
59 |
III, NCR |
Manila Bay |
96.34 |
24.20 |
25 |
23.59 |
24 |
-0.60 |
-2.50 |
-31.81 |
60 |
V |
Bacon-Manito |
12.75 |
12.45 |
98 |
11.93 |
94 |
-0.53 |
-4.25 |
-27.84 |
61 |
V |
Caramoan peninsula |
18.85 |
18.72 |
99 |
18.23 |
97 |
-0.49 |
-2.64 |
-26.05 |
62 |
III |
Angat watershed |
15.41 |
13.29 |
86 |
12.82 |
83 |
-0.47 |
-3.52 |
-24.60 |
63 |
BARMM |
Basilan
Natural Biotic Area |
4.48 |
4.45 |
99 |
4.02 |
90 |
-0.43 |
-9.58 |
-22.44 |
64 |
X |
Mount Kalatungan Mountains Ranges Natural Park |
35.77 |
31.90 |
89 |
31.48 |
88 |
-0.42 |
-1.31 |
-22.01 |
65 |
CAR, II |
Mount Pulag National Park |
13.29 |
12.56 |
94 |
12.18 |
92 |
-0.38 |
-3.03 |
-20.04 |
66 |
IX |
Pasonanca Natural Park |
10.42 |
10.03 |
96 |
9.66 |
93 |
-0.36 |
-3.63 |
-19.18 |
67 |
X |
Mount Balatukan |
35.25 |
29.24 |
83 |
28.90 |
82 |
-0.34 |
-1.16 |
-17.78 |
68 |
IV-B |
Romblon
Island |
8.19 |
7.10 |
87 |
6.77 |
83 |
-0.32 |
-4.58 |
-17.10 |
69 |
IV-A |
University
of the Philippines Land Grants (Pakil and Real) |
11.12 |
10.77 |
97 |
10.47 |
94 |
-0.30 |
-2.80 |
-15.87 |
70 |
III |
Bataan
Natural Park and Subic Bay Forest Reserve |
25.25 |
23.47 |
93 |
23.17 |
92 |
-0.29 |
-1.24 |
-15.36 |
71 |
IV-B |
Mount Hinunduang |
8.22 |
8.08 |
98 |
7.79 |
95 |
-0.29 |
-3.59 |
-15.27 |
72 |
III |
Mariveles mountains |
12.10 |
11.23 |
93 |
10.94 |
90 |
-0.29 |
-2.57 |
-15.17 |
73 |
VIII |
Biliran and
Maripipi Island |
12.76 |
12.36 |
97 |
12.07 |
95 |
-0.28 |
-2.29 |
-14.92 |
74 |
VI, VII |
Mount Kanla-on Natural Park |
24.78 |
16.22 |
65 |
15.94 |
64 |
-0.28 |
-1.74 |
-14.86 |
75 |
V, IV-A |
Mount Labo |
13.78 |
13.66 |
99 |
13.38 |
97 |
-0.28 |
-2.02 |
-14.52 |
76 |
VI |
North west
Panay peninsula (Pandan) |
12.06 |
11.70 |
97 |
11.44 |
95 |
-0.26 |
-2.18 |
-13.44 |
77 |
IV-B |
Marinduque
Wildlife Sanctuary (Central) |
8.92 |
8.29 |
93 |
8.04 |
90 |
-0.25 |
-2.99 |
-13.06 |
78 |
VII |
Nug-as and
Mount Lantoy |
10.46 |
6.67 |
64 |
6.47 |
62 |
-0.20 |
-2.96 |
-10.39 |
79 |
VII |
Rajah Sikatuna Protected Landscape |
12.40 |
11.22 |
91 |
11.03 |
89 |
-0.20 |
-1.74 |
-10.28 |
80 |
IV-A |
Mount Makiling |
6.23 |
5.92 |
95 |
5.76 |
93 |
-0.16 |
-2.71 |
-8.46 |
81 |
I |
Kalbario-Patapat National Park |
8.97 |
8.69 |
97 |
8.53 |
95 |
-0.16 |
-1.79 |
-8.17 |
82 |
V |
Mount Isarog National Park |
10.00 |
9.60 |
96 |
9.44 |
94 |
-0.16 |
-1.62 |
-8.16 |
83 |
IV-A |
Pagbilao and Tayabas Bay |
2.69 |
1.79 |
66 |
1.64 |
61 |
-0.15 |
-8.12 |
-7.63 |
84 |
IV-B |
Mount Guiting-guiting Natural Park |
15.34 |
15.22 |
99 |
15.07 |
98 |
-0.15 |
-0.99 |
-7.93 |
85 |
V |
Bulusan Volcano Natural Park |
3.72 |
3.42 |
92 |
3.30 |
89 |
-0.12 |
-3.45 |
-6.21 |
86 |
II |
Buguey wetlands |
10.87 |
2.34 |
22 |
2.26 |
21 |
-0.08 |
-3.52 |
-4.34 |
87 |
BARMM |
Mount Dajo National Park |
3.30 |
3.04 |
92 |
2.97 |
90 |
-0.07 |
-2.29 |
-3.67 |
88 |
X |
Mount Kitanglad |
31.02 |
29.55 |
95 |
29.48 |
95 |
-0.07 |
-0.24 |
-3.73 |
89 |
BARMM |
Lake Lanao |
36.35 |
3.14 |
9 |
3.08 |
8 |
-0.06 |
-2.01 |
-3.33 |
90 |
XII, XI |
Mount Sinaka |
1.75 |
1.54 |
88 |
1.48 |
85 |
-0.05 |
-3.46 |
-2.80 |
91 |
V |
Mount Kulasi |
3.05 |
3.03 |
99 |
2.97 |
98 |
-0.05 |
-1.81 |
-2.89 |
92 |
IV-A |
Quezon
National Park |
1.98 |
1.95 |
98 |
1.90 |
96 |
-0.05 |
-2.39 |
-2.45 |
93 |
VII |
Mount Kangbulagsing and Mount Lanaya |
2.62 |
1.72 |
66 |
1.68 |
64 |
-0.04 |
-2.28 |
-2.07 |
94 |
IV-A |
Mount Palay-Palay-Mataas Na Gulod
National Park |
1.83 |
1.77 |
97 |
1.74 |
95 |
-0.03 |
-1.55 |
-1.44 |
95 |
III |
Candaba swamp |
1.91 |
0.55 |
29 |
0.53 |
28 |
-0.03 |
-4.76 |
-1.39 |
96 |
IX |
Mount Timolan |
1.92 |
1.84 |
96 |
1.80 |
94 |
-0.03 |
-1.87 |
-1.81 |
97 |
IV-A |
Mounts. Banahaw-San Cristobal Protected Landscape |
11.33 |
10.68 |
94 |
10.65 |
94 |
-0.03 |
-0.27 |
-1.54 |
98 |
VII |
Mount Bandila-an |
1.78 |
1.60 |
90 |
1.57 |
88 |
-0.03 |
-1.65 |
-1.39 |
99 |
X |
Timpoong and Hibok-hibok Natural
Monument |
3.73 |
3.45 |
93 |
3.44 |
92 |
-0.01 |
-0.31 |
-0.56 |
100 |
II |
Malasi Lake |
0.16 |
0.01 |
3 |
0.00 |
2 |
0.00 |
-52.57 |
-0.15 |
101 |
|
Grand Total |
5129.8 |
4540.39 |
|
4270.33 |
|
-270.06 |
|
-14213.79 |
|
|
Average |
|
|
86 |
|
81 |
|
-6 |
|
|
* Thousand ha