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Global Ecology and Conservation 50 (2024) e02828
Available online 1 February 2024
2351-9894/© 2024 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Identifying Important Hornbill Landscapes in Sarawak, Malaysia
Shelby Q.W. Wee
a
, Jason J.H. Teo
b
, Batrisyia Teepol
b
, Hilda N.I. Jelembai
b
,
Nyat Jun Au
b
, Chin Aik Yeap
c
,
d
, Anuj Jain
a
,
*
a
BirdLife International Asia, 354 Tanglin Road, #01–16/17, Tanglin International Centre, 247672, Singapore
b
Malaysian Nature Society Kuching Branch, P.O. Box A144 Kenyalang Park, 93824 Kuching, Sarawak, Malaysia
c
Malaysian Nature Society, JKR 641, Jalan Kelantan, Bukit Persekutuan, 50480 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia
d
Faculty of Forestry and Environment, Universiti Putra Malaysia, Jalan Universiti 1, 43400 Serdang, Selangor, Malaysia
ARTICLE INFO
Keywords:
Borneo
Bucerotiformes
Conservation prioritisation
Habitat suitability
Maximum entropy
Species distribution
ABSTRACT
With land use change rapidly increasing in Asia, conservation prioritisation has emerged as an
important tool in identifying critical landscapes for biodiversity to safeguard them from human
pressures. In Peninsular Malaysia, the Malaysian Nature Society (MNS/BirdLife in Malaysia)
developed a set of Criteria to identify Important Hornbill Landscapes (IHLs) – hornbill hotspots
which are conservation priority sites in Malaysia and serve to inform land use planning and
conservation action. Application of the Criteria has so far been restricted to Peninsular Malaysia,
thus in this study, we adapt it to Sarawak, a Malaysian state in Borneo that supports 80% of the
hornbill species diversity in the country. We expand on this conservation prioritisation meth-
odology using Maximum Entropy Species Distribution Modelling (MaxEnt), to validate the Cri-
teria’s applicability and to identify potential IHLs in Sarawak. Our data sources included
literature reviews, citizen science databases and interviews. Expectedly, survey effort was
spatially biased. We identied eight IHLs, mostly concentrated in eastern Sarawak, across na-
tional parks, wildlife sanctuaries and forest management units. Existing published literature on
the distribution of hornbill habitats in Sarawak corroborated with our MaxEnt outputs which
aligned with the results of the IHL Criteria-based assessment, validating the latter and supporting
its use in Sarawak. We additionally identied six potential IHLs based on MaxEnt outputs which
conrmed the value of pairing MaxEnt with the Criteria-based assessment, for such a prioriti-
sation exercise. To our knowledge, this study not only demonstrates the signicance of combining
MaxEnt and the Criteria for IHL identication, but it also represents the rst application of the
IHL Criteria outside of Peninsular Malaysia. Our ndings can, therefore, serve as a case study for
future applications of IHL Criteria in Borneo and potentially for other parts of Asia.
1. Introduction
Conservation prioritisation is widely used and studied as an important tool for the advancement of ecological knowledge and to
inform policy and conservation action (Sinclair et al., 2018). Examples can be found from Australia, to the Middle East, and the
Americas (Ortega-Huerta and Peterson, 2004; Klein et al., 2009; Karimi et al., 2023). Specic to birds, BirdLife International’s
Important Bird and Biodiversity Areas (IBA) programme presents a key example of a global prioritisation effort that has inuenced bird
* Corresponding author.
E-mail address: anuj.jain@birdlife.org (A. Jain).
Contents lists available at ScienceDirect
Global Ecology and Conservation
journal homepage: www.elsevier.com/locate/gecco
https://doi.org/10.1016/j.gecco.2024.e02828
Received 29 September 2023; Received in revised form 14 January 2024; Accepted 28 January 2024
Global Ecology and Conservation 50 (2024) e02828
2
conservation worldwide (Donald et al., 2018; Waliczky et al., 2018). In Asia, there are numerous conservation prioritisation studies
(Ahmadi et al., 2017; Lehtom¨
aki et al., 2018; Macdonald et al., 2019), but few are bird-specic (Han et al., 2018; Hu et al., 2020). Of
these, only a handful are focused on hornbills despite their importance in the region (Jain et al., 2018a).
Hornbills (Family Bucerotidae) are one of the most attractive and charming birds in Asia. They are also important seed dispersers,
especially of large-seeded plants due to their wide gape, large bill, and ability to y great distances (Poonswad et al., 2013; Corlett,
2017). Their disappearance can disproportionally impact tropical forest ecosystems, and their declining populations throughout Asia is
a worrying trend (Datta et al., 2020). More than half of the hornbill species in Asia are threatened (BirdLife International, 2022), and
governments face a challenging task in protecting these birds in vast landscapes with limited budgets.
Researchers and expert groups such as the IUCN SSC Hornbill Specialist Group leverage their networks to support conservation
activities through monitoring and capacity building. With limited resources, conservation direly needs to be targeted in the most
important sites for more effective protection. Hornbill conservation in Asia will likely benet from conservation prioritisation
throughout its range countries, especially in Malaysia, which has close to a third of Asia’s hornbill diversity.
The Malaysian Nature Society (MNS) launched the MNS Hornbill Conservation Project in 2004 which focuses on the Belum-
Temenggor Forest Complex (BTFC) in Peninsular Malaysia. The Project has improved the extent of hornbill knowledge, engaged
local communities and deepened collaborations with focal government agencies (Yeap et al., 2016). Underpinning hornbill conser-
vation in Peninsular Malaysia is the identication of Important Hornbill Landscapes (IHL) – hornbill hotspots which are conservation
priority sites for hornbills in Malaysia. IHLs are identied based on four criteria dened by Yeap and Perumal (2018) with reference to
similar hornbill prioritisation exercises in India (Mudappa and Raman, 2008), Kenya (Musina, 2007) and Thailand (Trisurat et al.,
2013). IHLs in Peninsular Malaysia were conceptualised because the country’s national conservation agendas were previously largely
centred around mammals with priority sites and ecological corridors already identied for tigers and elephants (Department of
Wildlife and National Parks Peninsular Malaysia, 2008, 2013). Whereas to our knowledge, no such exercise had been conducted for
hornbills in Malaysia despite the country’s high hornbill diversity prior to Yeap and Perumal’s (2018) study. The IHL Criteria pri-
oritises sites which are ecologically large enough to support a high diversity of breeding populations of hornbill species. It also selects
sites which align and support the implementation of existing national policies to garner political support and institutional recognition
for its outputs. Since Yeap and Perumal (2018), IHLs have been used in Peninsular Malaysia by MNS to raise awareness about hornbill
conservation and to highlight the importance of sensitively managing forest reserves at IHL sites such as at Temengor forest reserve
where sustainable logging is allowed and practiced by the state.
Although valuable, IHL identication has not been conducted for Malaysian Borneo. This is despite Sarawak and Sabah, the two
Malaysian states in Borneo, being home to large tracts of intact forests that support 8 of the 10 hornbill species found in Malaysia.
Sarawak is even famously known as the ‘Land of Hornbills’, where indigenous cultures and beliefs are deeply tied to hornbills
(Pengiran and Mohd-Azlan, 2021). Conservation is urgently needed in Sarawak as large areas of intact forests have been lost in recent
decades (Gaveau et al., 2014; Jaafar et al., 2020), more are under threat by planned developments (Alamgir et al., 2020), and sub-
stantial knowledge gaps about hornbill priority areas remain (Jain et al., 2018b). Key hornbill habitat may also be lost with Sarawak’s
dependence on forestry (Jaafar et al., 2020), and logging still posing an issue despite the government’s commitment to curb illegal
logging (Pandong et al., 2019). Identifying IHLs in Sarawak is thus critical to the conservation of healthy hornbill populations that are
Fig. 1. Administrative divisions of Sarawak, Malaysia.
S.Q.W. Wee et al.
Global Ecology and Conservation 50 (2024) e02828
3
tightly linked to Sarawakians through their ecological services and cultural signicance.
Our study follows Yeap and Perumal’s (2018) methods, obtaining data on sites which support hornbill populations in Sarawak
through literature reviews, and online citizen science birdwatching databases. We additionally conducted semi-structured interviews
to derive supplementary data. Data was analysed in three ways. First, we applied the IHL Criteria established by Yeap and Perumal
(2018) to assess and prioritise sites for hornbill conservation. Second, we used Kernel Density Estimation to visualise survey effort
across Sarawak. Finally, we validate the efcacy of the Criteria using Maximum Entropy Species Distribution Modelling (MaxEnt) to
identify predicted hotspots of hornbill occurrence.
Through this we aim to (1) identify a set of IHLs based on established criteria, and (2) use MaxEnt to validate the IHL criteria’s
application in Sarawak, given potential gaps in survey effort.
2. Methods
2.1. Study site
Sarawak is Malaysia’s largest state and is located on Borneo (Fig. 1). It is a tropical region with an equatorial climate and expe-
riences a wet season from November to February due to the northeast monsoon. Many types of forests are found here, including the
most extensive hill mixed Dipterocarp forest (Osman et. al, 2014). Eight species of hornbills reside here, the black (Anthracoceros
malayanus), bushy-crested (Anorrhinus galeritus), helmeted (Rhinoplax vigil), oriental pied (Anthracoceros albirostris), rhinoceros
(Buceros rhinoceros), white-crowned (Berenicornis comatus), wreathed (Rhyticeros undulatus), and wrinkled hornbill (Rhabdotorrhinus
corrugatus) (Yeap et. al, 2016). The helmeted, wrinkled and white-crowned hornbills are threatened with the former being Critically
Endangered, and the latter two being Endangered (BirdLife International, 2022). The remaining forests in Sarawak are key habitats for
hornbills, however, deforestation for commercial logging and land clearance for oil palm plantations has resulted in the loss of about
1.6 million ha of forests in the last two decades (Global Forest Watch, n.d.; Jaafar et al., 2020).
2.2. Data collection
We extracted data on hornbill nesting and juvenile records, and the year, sites, species, and geographical coordinates of hornbill
sightings in Sarawak through three methods – a literature review, online bird-watching databases and interviews.
We compiled a list of sites where hornbills were observed based on the geographical coordinates of hornbill sightings. We obtained
the size of each site from SFC (Sarawak Forestry Corporation, n.d.) and from Samling Timber Malaysia, a timber company (Samling
Timber Malaysia, n.d.). To obtain data on each site’s relevance to national policies, we referenced SFC’s list of gazetted TPAs (Sarawak
Forestry Corporation, n.d.) and the IBA list of Malaysia (BirdLife International, 2023a). We also referenced the sites covered by the
Heart of Borneo (HoB) Initiative – a government-recognised initiative undertaken with other Bornean countries aimed at protecting the
remaining tract of contiguous Bornean forest; this includes both protected and non-protected areas (Forest Department Sarawak,
2023).
2.2.1. Literature review
A literature review was conducted in 2021. We referenced published reports and workshop proceedings by the Sarawak Forestry
Corporation (SFC) and Forest Department of Sarawak. The former is a government statutory board that oversees Totally Protected
Areas (TPAs) in Sarawak, while the latter is the government department overseeing sustainable forest management. We referenced
museum records from the Yale Peabody Museum of Natural History and the Sarawak Museum. We also extracted data from grey
literature comprising unpublished bird lists from experienced birdwatchers known to MNS, and unpublished papers and expedition
reports written by MNS. Additionally, data was extracted from published scientic articles and books resulting from a Google Scholar
search using a combination of keywords: “hornbill species”, “Sarawak”, "Bucerotiformes", “bird survey” as well as the common and
scientic names of all eight hornbills in Sarawak.
2.2.2. Online bird-watching databases
Hornbill sightings were obtained from online bird-watching databases Xeno-canto (www.xeno-canto.org), Cloudbirders (www.
cloudbirders.com), and the Global Biodiversity Information Facility (GBIF) (GBIF.org, 2023a). From GBIF, only eBird records were
available. We obtained permission from Sarawak’s eBird reviewers to access helmeted hornbill eBird records which have otherwise
been redacted from the public domain (Sullivan et al., 2009).
2.2.3. Semi-structured interviews
Interviews were conducted for data triangulation and to obtain supplementary information on Forest Management Units (FMUs) –
areas which are designated for timber harvest and privately-owned by timber companies; they are not publicly accessible. A total of 22
people were interviewed, selected through purposive sampling. 13 of the 22 respondents had environment-related jobs in conser-
vation, bird guiding, ornithology and/or biodiversity consulting. They were, therefore, familiar with a variety of sites across Sarawak
and were well-positioned to provide rst-hand accounts of hornbill sightings. Six respondents had jobs related to FMUs and could give
insights based on non-publicly available data. Two respondents were familiar with indigenous practices and were selected because
they could provide information on indigenous sites. The remaining individuals worked at the Sarawak Museum. All interviews were
conducted between October 2020 and June 2021. The interviews followed a semi-structured format which was guided by a set
S.Q.W. Wee et al.
Global Ecology and Conservation 50 (2024) e02828
4
questionnaire (see Appendix A.1). Responses were recorded by the interviewer during the interview. Through this exercise, we also
obtained personal, unpublished bird lists where possible.
2.3. Data analysis
We built a prole for each site comprised of the total number of hornbill species detected, the size of the site, presence of hornbill
breeding records, and the site’s relevance to national policies.
2.3.1. IHL criteria
We assessed each site against the IHL Criteria (Table 1).
Ideally, a site should full all four criteria, however, due to the paucity of breeding and nesting data, we made two modications.
Firstly, we considered a site an IHL if it fullled Criteria 1, 2 and 4. Sites that met all four criteria were classied as higher priority IHLs.
Secondly, we considered Criterion 3 met in our study if at least one species was documented breeding owing to the paucity of hornbill
breeding records from Sarawak. In contrast Yeap and Perumal’s (2018) study, required records of all six breeding species to be met at
IHL sites.
The area of 50,000 ha was selected as a cut off for IHLs based on past experience wherein sites of size 10,000 – 20,000 ha have been
observed to be too small to hold long-term breeding populations of the larger hornbill species such as the Helmeted Hornbill and Great
Hornbill that can y for several tens of kilometres. Sites of at least 50,000 ha are large enough to support such movements, allow
undisturbed core habitats to persist and handle localised extinctions due to stochastic events.
As museum records included historical sightings from the 1900 s, sites were re-assessed against the Criteria using only data from
the most recent 20 years (2001 – 2021). Oil palm expansion accompanied by deforestation increased signicantly post-1990 with more
than 20% of primary humid forests lost between 2002 to 2022 (Global Forest Watch, n.d., Kamlun et al., 2012; Jaafar et al., 2020). We
re-assessed sites as those which supported hornbills in the 1900 s may have been cleared or degraded.
It is important to note that several hornbill records did not provide detailed geographical coordinates. When only the name of the
site surveyed was provided, we used the general geographical coordinates of the site available on Google Maps for analysis. This was
especially for interview data, where interviewees would commonly respond with just the name of the sites they had visited. Addi-
tionally, a minority of museum records stated only the Sarawak administrative division as the location of sighting, for example
“Bintulu”, instead of the specic site surveyed. These records were not analysed as they were too generic.
2.3.2. Survey effort visualization
To visualise survey effort, a heatmap was plotted using Kernel Density Estimation on QGIS version 3.28. We combined geographical
coordinates of hornbill sightings obtained through our methods (described in Section 2.2) with survey coordinates of all other birds in
Sarawak which were obtained from GBIF to form this dataset (GBIF.org, 2023a). Hornbill sightings from Brunei were obtained from
GBIF (GBIF.org, 2023b) and included in the analysis due to the country’s proximity to Sarawak. Each unique geographical coordinate
represented a sampling unit. A kernel radius of 5 km was used for two reasons. Firstly, hornbills are known to produce loud and
distinctive vocalisations which can be heard over several kilometres, and therefore hornbill detections could be within a
several-kilometre radius (Haimoff, 1987; Kennedy et al., 2023). Secondly, our data sources only provide a single set of coordinates for
species sightings instead of transects or area covered. We accounted for these limitations using the kernel radius for more accurate
visualisation of survey effort.
2.3.3. MaxEnt
MaxEnt is a species distribution modelling tool that uses presence-only data along with environmental layers to approximate the
density of species occurrences across a topographical range (Merow et al., 2013). MaxEnt has been used to identify conservation
priorities across tropical Asia such as for hornbills in Thailand (Trisurat et al., 2013), mammals (Clements et al., 2012) and across
various taxonomic groups (Lehtom¨
aki et al., 2018; Huang et al., 2020). The Malaysian government has even acknowledged the
ecological relevance of MaxEnt in conservation planning (Rahman et al., 2019).
MaxEnt requires a set of species presence localities, and environmental layers. In our study, MaxEnt input data included hornbill
survey locations obtained through various data sources (listed in Sections 2.2.1, 2.2.2 and 2.2.3). We constructed ve separate models
for each of the ve non-threatened and most common hornbills in Sarawak – black, bushy-crested, oriental pied, rhinoceros, and
wreathed – using MaxEnt version 3.4.4. We used 19 bioclimatic layers and an elevation layer from World Clim at a resolution
Table 1
The IHL Criteria originally described by Yeap and Perumal (2018) and their relevance to Sarawak.
Number Criteria
1 The area supports at least 60% of the hornbill species found within the state/region of interest. In Sarawak, this would be ve species.
2 The area should be as large as possible (at least 50,000 ha) either as a single block or made up of a cluster of nearly contiguous blocks (forest complex).
3 The hornbills in the area are known to breed either by direct (e.g., active nest tree with seal) and/or indirect evidence(s) (e.g., sighting of resident
juveniles with and/or without parent birds).
4 The area identied supports and assists in the implementation of national conservation policies and/or work programmes. In Sarawak, this includes
totally protected areas (which include national parks, wildlife sanctuaries, nature reserves), government backed conservation initiaves such as the Heart
of Borneo (HOB). Forest Management Units (FMUs) can be included only if they are part of conservation initiatives such as HOB.
S.Q.W. Wee et al.
Global Ecology and Conservation 50 (2024) e02828
5
approximate to 1 km (Fick and Hijmans, 2017) and forest cover data from Global Forest Watch (2016a) at a resolution of 30 m as
environmental variables. We re-sampled forest cover data to obtain a resolution approximate to 1 km before using it in the model.
Cropped environmental layers included Brunei due to its proximity to Sarawak.
An underlying assumption of Maxent is that sampling within the modelled landscape is conducted randomly or representatively
(Yackulic et al., 2012). Maxent is, therefore, vulnerable to sampling bias and if uncorrected, outputs can be unrepresentative of true
species distributions (Phillips et al., 2009). Unequal sampling probability is a particular concern, especially with citizen science data
(Geldmann et al., 2016; Sicacha-Parada et al., 2021). We expected survey effort to be uneven across Sarawak, so we addressed
sampling bias by constructing a bias grid composed of geographical coordinates of all known surveyed localities for all birds in both
Sarawak and Brunei. The bias grid was introduced into the background data of each model so that background points selected in the
modelling process shared the same bias as the presence localities. The effectiveness of this method in reducing sampling bias is
supported in several studies (Phillips et al., 2009; Kramer-Schadt et al., 2013; Syfert et al., 2013). Each MaxEnt model thus had three
inputs – presence localities, environmental variables, and a bias grid.
We compared response curves available in each MaxEnt model’s outputs to identify collinearities. Environmental variables which
were highly correlated were identied as collinear, and we retained the variable with the highest permutation importance while
eliminating the rest to remove collinearities. Afterwards, we removed all environmental variables with a permutation importance of
zero. Finally, we removed any variable which had a permutation importance smaller than 1 if this resulted in improvements in the area
under the Receiver Operating Characteristic curve (AUC) or caused an improvement in permutation importance for the remaining
variables in the model while maintaining the AUC. This resulted in a nal MaxEnt model for each species. The predicted occurrence
probabilities from these nal models were visualised using QGIS. Areas with an occurrence probability of 50% or more were isolated
and overlaid on QGIS, yielding a distribution map of the likely (>50% probability) number of non-threatened hornbills across
Sarawak.
We then identied a list of potential IHLs which are sites that did not meet Criteria 1, but met Criteria 2 and 4, and are shown on the
distribution map to have at least two non-threatened hornbill species. A cut-off of two species was chosen for the maps because to
qualify under Criteria 1, a site needs to support ve species and there are three species – white-crowned, wrinkled, and helmeted
hornbill – which were not modelled but could still be detected in these sites. Although a generous cut-off point, it allows us to identify
as many potential IHLs as possible and enables a more comprehensive assessment of IHLs in Sarawak. MaxEnt models for the three
threatened hornbill species were not constructed in anticipation of fewer sightings due to their threatened status and rarity in both
Sarawak and Brunei compared to the ve non-threatened species. The threatened species were, however, included in IHL assessments.
3. Results
We obtained 130 years’ worth of hornbill records dating from 1891 to 2021, for 119 sites across Sarawak. Our data mostly came
from online bird-watching databases (45.7%), and grey literature (31.0%), followed by interviews (13.2%), published studies (5.2%),
and museum records (4.8%).
Online bird-watching databases were our most important source, of which eBird supplied 642 of 650 records; the remainder were
obtained from CloudBirders and Xeno-canto. Data from our literature review was the second-most important, which showed that grey
literature provided more information compared to published literature. In our study, grey literature comprised two expedition reports
written by MNS in 2019 and 2020, and unpublished bird lists from 14 experienced birdwatchers known to MNS. Published studies
comprised reports from SFC and the Forest Department, workshop proceedings from two national workshops, a report from the World
Wildlife Fund Malaysia and articles from the Sarawak Museum Journal. Interviews were the third-most important source and re-
spondents were familiar with 26 out of 119 sites across Sarawak. Besides providing data on hornbill sightings, 13 out of 22 in-
terviewees also provided rst-person accounts of nesting observations for 11 sites.
The species with the most widespread distribution was the black hornbill, with detections in 65 sites. This was followed by the
rhinoceros and bushy-crested hornbill, both with sightings in 54 sites. Accordingly, these three species had the highest number of
records (Table 2). Although helmeted hornbill data was used in our IHL analysis, it is redacted here due to the sensitivity of the species
Table 2
Overview of hornbill data obtained from various sources in the study.
Species Number of
sites
Administrative division Number of sites with
nesting records
Number of
records
Black Hornbill 65 Betong, Bintulu, Kapit, Kuching, Limbang, Miri, Mukah, Samarahan,
Serian, Sibu, Sri Aman
2 264
Rhinoceros Hornbill 54 Bintulu, Kapit, Kuching, Limbang, Miri, Sibu, Sri Aman 1 501
Bushy-crested
Hornbill
54 Betong, Bintulu, Kapit, Kuching, Limbang, Miri, Mukah, Samarahan,
Sarikei, Sibu, Sri Aman
3 283
Wreathed Hornbill 47 Betong, Bintulu, Kapit, Kuching, Limbang, Miri, Samarahan, Serian,
Sibu, Sri Aman
1 395
Oriental Pied
Hornbill
32 Betong, Bintulu, Kapit, Kuching, Limbang, Miri, Mukah, Sibu, Sri
Aman
1 124
White-crowned
Hornbill
29 Bintulu, Kapit, Kuching, Limbang, Miri, Sibu, Sri Aman, Serian 1 123
Wrinkled Hornbill 15 Bintulu, Kapit, Kuching, Limbang, Miri, Mukah 0 31
S.Q.W. Wee et al.
Global Ecology and Conservation 50 (2024) e02828
6
to poaching and trade.
In total eight IHLs were identied. Hose-Laga National Park, Lanjak Entimau Wildlife Sanctuary, Mulu National Park, Pulong Tau
National Park, and Ravenscourt FMU – met all four criteria and were classied as higher priority IHLs (Table 3). Usun Apau National
Park and Melatai-Para FMU did not meet Criterion 2, however, as their sizes are approximately 50,000 ha, they were deemed to qualify
as an IHL. Additionally, ve out of the eight IHLs, including four higher priority IHLs, are national parks or wildlife sanctuaries and are
recognised as TPAs. Re-assessments of sites based on reFcent data showed that all eight IHLs identied still meet the criteria, with the
same number of species qualifying under Criterion 1 for all sites.
Kernel Density Estimation showed that survey effort was unequal across Sarawak and was concentrated in the south-west, near
Kuching City (Fig. 2a). Compared to protected areas, FMUs were less surveyed, although they cover a bigger proportion of Sarawak’s
land area. Despite the spatial bias towards Kuching city, most of the IHLs were identied in eastern Sarawak, in the relatively few sites
where surveys have been conducted (Fig. 2b).
Diagnostic tests for MaxEnt models were conducted by analysing the AUC. We obtained a value of 0.723 for the black hornbill,
0.796 for the bushy-crested hornbill, 0.756 for the oriental-pied hornbill, 0.809 for the rhinoceros hornbill and 0.784 for the wreathed
hornbill (see Appendix A.2–A.6 for graphical depictions of the MaxEnt results). As all values exceeded 0.7, diagnostics for all ve
models is considered good (Duan et al., 2014). Predictor variables which contributed the most to each model were annual precipitation
(black hornbill), maximum temperature of the warmest month (bushy-crested hornbill), mean diurnal temperature range (oriental-
pied hornbill), and elevation (rhinoceros and wreathed hornbills) (see Appendix A.7 for a list of variables and their contribution to
each model). Overlaying areas with a detection probability of 50% or more resulted in a combined output, showing sites that support
more species of non-threatened hornbills lie in eastern and north-eastern Sarawak (Fig. 3). These sites are situated in most of Sarawak’s
remaining hill mixed dipterocarp forests which according to Osman at al. (2014), also mainly lie in eastern Sarawak. Although this
nding (Fig. 3) contrasts with survey effort (Fig. 2a), it aligns with the nding that most IHLs were identied in eastern and north-
eastern Sarawak (Fig. 2b). Additionally, although there are few areas which support all ve non-threatened species, most of
eastern and north-eastern Sarawak supports at least two, indicating that there are potentially more IHLs yet to be identied.
Six potential IHLs (Table 4) were additionally identied using the MaxEnt output in Fig. 3. These areas had an occurrence prob-
ability of 50% or more for the non-threatened hornbills across Sarawak. Except for Baleh National Park, the remaining ve potential
IHLs are FMUs and are not considered TPAs.
4. Discussion
Our study is the rst conservation prioritisation exercise for hornbills in Sarawak to the best of our knowledge. We hope our study’s
results and takeaways can inform and guide future conservation efforts. Our results indicate that critical hornbill habitats persist in
Sarawak, despite historical deforestation and fragmentation. This justies the need to protect and manage IHLs, especially in areas
where sustainable logging is planned. In that sense, our methodology of identifying IHLs (congruous to Yeap and Perumal, 2018), may
be applicable and pertinent to other parts of Asia where forestry remains a key industry. It is remarkable that the IHL prioritisation
exercise also picked up sites that were previously deemed as knowledge gaps for hornbills such as Hose Laga (Jain et al., 2018b) and
where subsequent surveys yielded the presence of a diverse hornbill fauna.
4.1. Added value of using MaxEnt estimates alongside IHL criteria for conservation prioritisation
We observed large alignment between our MaxEnt outputs and the published literature on forest cover and hornbill habitat
preference. As an example, our MaxEnt outputs showed a high likelihood of occurrence for a greater number of hornbill species to be
found in eastern Sarawak, particularly in hill mixed dipterocarp forests (Osman et al., 2014). These forests are prime hornbill habitat,
dominated by dipterocarp trees which are the principal nesting locations for several hornbill species (Poonswad et. al, 2013; Jain et al.,
2018a; BirdLife International, 2023b, BirdLife International, 2023c).
Table 3
Important Hornbill Landscapes in Sarawak. Sites in bold indicate higher priority IHLs. Sites with an asterisk (*) indicate that their sizes are
approximately 50,000 ha and hence, were deemed to qualify as IHLs.
Administrative Division Site Name IHL
Criterion 1
(Number of species)
IHL
Criterion 2
(size in ha)
IHL
Criterion 3
(Breeding status)
IHL
Criterion 4
(Conservation status)
Miri Mulu National Park 8 85,671 Conrmed NP, IBA, WHS, HoB
Kapit Hose-Laga National Park 7 51,342 Conrmed NP, IBA
Sri Aman Lanjak-Entimau Wildlife Sanctuary 7 182,983 Conrmed WS, IBA, HoB
Limbang Ravenscourt Forest Management Unit 6 117,941 Conrmed HoB
Miri Pulong Tau National Park 6 69,817 Conrmed NP, HoB
Miri Gerenai Forest Management Unit 7 148,305 Unknown HoB
Miri Usun Apau National Park 6 49,355 * Unknown NP
Kapit Melatai-Para Forest Management Unit 5 49,524 * Unknown HoB
Abbreviations: NP =National Park / WS =Wildlife Sanctuary / IBA =Important Bird and Biodiversity Area / WHS =UNESCO World Heritage Site /
HOB =Heart of Borneo
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Furthermore, MaxEnt predicted that few sites can support all ve non-threatened species (modelled in this study) which also agrees
with published literature. These ve species have different habitat preferences, particularly the oriental-pied and black hornbill. The
former prefers coastal forests, mangroves and forest edges which contrasts with inland forest adapted hornbills such as the bushy-
Fig. 2. (a) Kernel Density Estimation of spatial survey effort across Sarawak. Protected areas and Forest Management Units have been overlaid
(Global Forest Watch, 2015; Global Forest Watch, 2016b). (b) Kernel Density Estimation of spatial survey effort overlaid with identied IHLs.
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crested hornbill’s preference for closed-canopy and undisturbed habitats, so they may not occur in the same site (Anggraini et. al,
2000; Gale and Thongaree, 2006, Ng et. al, 2011; Loong et. al, 2021). Similarly, the black hornbill is observed to strongly associate
with forests below an altitude of 200 m and are therefore less likely observed in hill mixed dipterocarp forests which are classied in
Malaysia to exist at altitudes above 300 m (Gale and Thongaree, 2006; Saiful and Latiff, 2017; Forestry Department of Peninsular
Malaysia, 2023).
The congruence between our MaxEnt results and published literature, shows that MaxEnt is a useful tool with representative
outputs that can validate the applicability of the IHL Criteria and therefore, aid conservation prioritisation in Sarawak and beyond
(Kaky et al., 2020; Li et al., 2022).
Sampling bias remains a concern. Survey efforts are rarely equally distributed across large landscapes, and as is the case for
Sarawak, surveys (particularly those involving citizen scientists) are known to be concentrated in more accessible areas, such as near
city centres and main roads (Tulloch et al., 2012; Barbosa et al., 2013; Warton et al., 2013; Mair and Ruete, 2016). IHL identication
using the Criteria can be limited due to spatial bias against remote, inaccessible, and less well-known sites. MaxEnt is equally
vulnerable to spatial bias. In our study, after accounting for sampling bias, we found that MaxEnt identied potentially more IHLs in
eastern and north-eastern Sarawak than the Criteria identied. Intense survey efforts should be undertaken in the potential IHLs to
conrm the persistence of hornbill species.
Detection bias also remains a concern. MaxEnt does not account for detection bias where a species maybe cryptic or has less
probability of detection even if present at a given sampling location (Yackulic et al., 2012). Other methods tackling this exist, such as
conducting repeat surveys and occupancy modelling (Mackenzie and Royle, 2005; K´
ery et al., 2010). Yet, these methods imply new
data collection and technical computational capacities which will limit their application in conservation prioritisation. In this study we
obtained data from three sources for a more comprehensive understanding of hornbill detections. We suggest that this is more
resource-effective compared to new data collection.
We also suggest that MaxEnt with sampling bias corrections is robust in visualising species distributions because it is less data
hungry, easier to use and accounts for sampling bias. More computationally and data intensive methods can be considered if there is
Fig. 3. Predicted distribution of the number of non-threatened hornbill species across Sarawak based on MaxEnt distributions of the black, bushy-
crested, oriental pied, rhinoceros, and wreathed hornbill.
Table 4
Potential Important Hornbill Landscape (IHL) sites in Sarawak, identied using MaxEnt output with these predicted to have more than 50%
occurrence probability for the non-threatened hornbills across Sarawak.
Administrative division Site name IHL
Criterion 1
(Number of
observed species)
IHL
Criterion 2
(size in ha)
IHL
Criterion 3
(Breeding status)
IHL
Criterion 4 (Conservation status)
N T
Limbang Ulu Trusan Forest Management Unit 4 92,751 Unknown IBA, HoB
Kapit Baleh National Park 4 66,721 Unknown NP, HoB
Kapit Danum Forest Management Unit 1 200,383 Unknown HoB
Kapit Kapit Forest Management Unit 1 149,756 Unknown HoB
Kapit Pasin Forest Management Unit 1 132,151 Unknown IBA, HoB
Kapit Linau Forest Management Unit 1 72,685 Unknown HoB
Abbreviations: N =Not threateaned / T =Threatened/ NP =National Park /IBA =Important Bird and Biodiversity Area / HOB =Heart of Borneo
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readily available capacity. Although not perfect, using MaxEnt with the IHL criteria greatly improves prioritisation by validating the
Criteria’s outputs, identifying potential priority areas and streamlining future ground-truthing efforts for under-surveyed landscapes
like Sarawak.
4.2. Expanding survey effort & coverage of forest management units
Data is most needed for FMUs in Sarawak which comprise nearly half of Sarawak’s land mass. FMUs are privately owned by timber
companies and are not publicly accessible. Although the Forest Department of Sarawak conducts wildlife surveys in FMUs, the survey
data is also not publicly available. This emphasises the importance of independent research and data-sharing agreements with timber
companies for the collection of critical biodiversity data. The MNS Kuching Branch has brokered such agreements before and this
signicantly improved the dataset for Ravenscourt FMU, which we identied as a higher priority IHL. Further collaborations would be
essential for hornbill conservation prioritisation in Sarawak. They would also help close a major gap towards the identication of
priority sites for threatened hornbills like the helmeted hornbill (Jain et. al, 2018b).
4.3. Identication of additional higher priority IHLs relies on better nesting data
Our assessment conrms the paucity of hornbill nesting and breeding records in Sarawak. Only ve sites would have qualied as an
IHL if we did not modify our methods from Yeap and Perumal (2018). Knowing that anecdotal breeding records exist from other sites,
we relaxed Criteria 3 but split IHLs into two categories – higher priority IHLs and IHLs – to acknowledge the importance of hornbill
breeding records as an indication of healthy hornbill populations. Moving forward, for identication of additional higher priority IHLs,
a concerted effort is needed to search for and formally document nesting and breeding of hornbills across Sarawak. We found a
particular lack of such data in journals, books, and online resources. We obtained the majority of hornbill nesting data from interviews
with SFC and FMU staff, who had collected important but unpublished nesting data as part of their work. Besides conducting targeted
nest searches, future efforts should also incorporate extensive interviews with a larger number of people who may hold unpublished
nesting data such as birdwatchers, TPA and FMU staff, and local community members.
4.4. Implications for hornbill conservation
While most of the IHLs identied in our study are already designated as TPAs, one of the higher priority IHLs and majority of the
potential IHLs are designated as FMUs, which are not protected for wildlife. Studies have shown that bird populations can exhibit
resilience in selectively logged landscapes, where hornbills can persist (Johns, 1987; Lambert, 1992; Johns, 1996). Our ndings, thus,
strengthens the case to incorporate hornbill conservation into FMU management. This can align with Sarawak government’s move to
reduce the impact of logging in FMUs by mainstreaming Sustainable Forest Management (SFM).
The Sarawak government’s commitment to SFM in FMUs is positive for biodiversity. For example, the government declared that
long-term Forest Timber Licences had to be certied under Forest Management Certication schemes such as the Malaysian Timber
Certication Scheme (Ting et al., 2022). Additionally, the government necessitated the application of Reduced Impact Logging (RIL) in
the SFM of FMUs, making it a pre-requisite to achieving Forest Management Certication (Ting et al., 2022). RIL is an approach in SFM
to reduce damage caused by wholesale logging through strategic planning in all stages of the harvesting operation (Sist, 2000).
Hornbill conservation threats, however, may not be adequately addressed in spite of SFM approaches. An example of a threat which
may persist is the targeted removal of large trees. Being large-sized cavity nesters, the availability of trees with large girths and
consequently large cavities is important in providing nesting sites to sustain hornbill populations (Kaur, 2020). Dipterocarps in
particular, are known to be important nest trees. However, they are also targeted for timber harvest (Poonswad et al., 2013; Hayward
et. al, 2021). The removal of these trees results in a direct loss of nesting opportunities and has indirect effects by impeding the
recruitment and growth of younger Dipterocarps, limiting future nesting options (Yamada et. al, 2016; Diway et. al, 2023). Strategies
such as leaving trees with hornbill nests standing and restricting logging to the non-breeding season, should be incorporated into the
management strategies of FMUs (Meijaard, 2005). As a last resort, the use of articial nest boxes can be explored to avert a signicant
loss of breeding sites for hornbills in identied FMU IHLs.
4.5. Limitations
We wanted to follow the IHL Criteria set out by Yeap and Perumal (2018) in Peninsular Malaysia as closely as possible. However,
we recognise where it may be rather narrow in its scope. For example, IHL Criterion 4 limits prioritisation to sites that are protected or
nationally recognised for wildlife and therefore, have current management plans to prioritise conservation issues. Yet, there may be
sites that meet all other criteria and are, therefore, ecologically important but do not have adequate resources to prioritise conser-
vation. This may particularly apply to community conserved areas (managed by indigenous groups) which make up about 7% of
Sarawak’s land area (Dayak Daily, 2023). One way to address this gap would be to loosen Criterion 4 for future studies and qualify sites
as IHL if they meet Criteria 1 – 3. Sites that are currently community conserved areas, IBAs or Key Biodiversity Areas (KBAs) and may
benet from the allocation of new resources when proled as IHLs. This may aid the identication of sites which are ecologically
important for hornbills but overlooked by national policies from a wildlife perspective.
Additionally, we attempted to conduct a representative analysis of survey effort. However, it is likely that site-level location details
such as exact geographical coordinates were sometimes not accurately captured in our analysis. In particular, we used generic
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geographical coordinates for several interviews and personal bird list records resulting in only indicative survey locations which might
not fully align with the actual surveyed area per site. It is possible, that each site was surveyed more extensively than shown in our
analysis. Bearing this in mind, our results should not be used for survey planning within sites. Yet, it still offers a representative
overview of survey effort coverage throughout Sarawak. Despite its limitations, we feel our macro-level analysis is impactful for
conservation prioritisation as it can direct future survey efforts where it is needed most across Sarawak.
5. Conclusion
In this study, we showed the applicability of IHL work in Sarawak, and demonstrated the value of MaxEnt in IHL identication. Our
analysis found eight IHLs in Sarawak, of which ve are of higher priority. Using MaxEnt, we validated results produced by the IHL
criteria and showed their congruence with existing hornbill and forest cover knowledge, signifying the applicability of the IHL criteria
in Sarawak. Furthermore, we identied six potential IHLs which can benet from targeted surveys and data-sharing agreements with
FMUs. Data gaps in breeding and nest records were also identied as liming factors for IHL identication. This inaugural IHL
assessment can also inform land use and management plans, and guide hornbill conservation planning in Sarawak for the coming
years.
Being similarly situated in Borneo (as Sarawak), IHL identication can also be replicated in Sabah, applying similar methods
outlined in this study, to produce comparable results, and expand hornbill conservation prioritisation in Malaysia. At a regional level,
applicability of the IHL criteria to other Asian contexts could also be explored. Given the urgent need for hornbill conservation in Asia,
we suggest that our results be used as a case study to be built upon and adapted to support hornbill conservation prioritisation,
especially in neighbouring South-east Asian countries facing similar challenges balancing forestry with conservation.
Ethics approval
Free Prior and Informed Consent was obtained from interviewees before interviews were conducted.
CRediT authorship contribution statement
Jelembai Hilda: Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Writing – original
draft. Au Nyat Jun: Project administration, Resources, Supervision, Validation. Teo Jason J. H.: Data curation, Investigation,
Methodology, Validation. Teepol Batrisyia: Data curation, Investigation, Methodology, Validation, Writing – original draft, Formal
analysis. Wee Shelby Q.W.: Data curation, Formal analysis, Project administration, Writing – original draft, Writing – review &
editing, Methodology, Software, Visualization. Yeap Chin Aik: Data curation, Project administration, Resources, Supervision, Vali-
dation, Writing – original draft, Writing – review & editing. Jain Anuj: Conceptualization, Funding acquisition, Resources, Super-
vision, Validation, Writing – original draft, Writing – review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing nancial interests or personal relationships that could have appeared to
inuence the work reported in this paper.
Data availability
Data will be made available on request.
Acknowledgements
We are grateful to BirdLife Species Champion Peter Smith, who provided nancial support for this work. We also like to thank
Oswald Braken Tisen of SFC for facilitating data sharing and providing the relevant permits for eld surveys. We acknowledge the
Director of Forest Department Sarawak, Datu Hamden Bin Haji Mohammad for providing information on Sarawak’s Forest cover. Also
fundamental to our work is the Director of Sarawak Museum, Tazudin Mohtar, who provided access to historical hornbill data. Lastly,
we are thankful to the MNS Kuching Branch Committee for their support.
Appendix A. Supporting information
Supplementary data associated with this article can be found in the online version at doi:10.1016/j.gecco.2024.e02828.
S.Q.W. Wee et al.
Global Ecology and Conservation 50 (2024) e02828
11
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