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Volume 8(4): 311-328 (2024) (http://www.wildlife-biodiversity.com/)
Home range and factors affecting the appearance of the
fishing cat (Prionailurus viverrinus) in a human-dominated
landscape, Thailand
Chaiwat Klakhaeng1, Supawat Khaewphakdee4, Wiroon Mongkonsin4, Laurel E.K.
Serieys2, Wai-Ming Wong2, Marnoch Yindee3, Rattapan Pattanarangsan4, Warong
Suksavate1, Pongsatorn Promkuntod5, Thaksin Wongson1, Ronglarp Sukmasuang1,*
1Department of Forest Biology, Faculty of Forestry, Kasetsart University. Bangkok 10900, Thailand.
Tel. +66-2579-0176, Fax. +66-2942-8107
2Panthera, 8 West 40th Street, 18th Floor, New York 10018, United States.
3Akkhraratchakumari Veterinary College, Walailak University, 222 Thaiburi, Thasala district,
Nakhonsithammarat 80160, Thailand
4Panthera South and Southeast Asia, 75/9 Prueksakan, Moo 1, Tha Makham, Muang Kanchanaburi,
Kanchanaburi 71000, Thailand.
5Khao Sam Roi Yot National Park, Ban Khao Daeng, Khao Daeng Sub-district, Kui Buri District,
Prachuap Khiri Khan Province 77150, Thailand
*Email: mronglarp@gmail.com
Received: 19 June 2024 / Revised: 14 September 2024 / Accepted: 14 September 2024/ Published online: 25 September 2024.
How to cite: Klakhaeng, C.H. et al. (2024). Home range and factors affecting the appearance of the fishing cat (Prionailurus
viverrinus) in a human-dominated landscape, Thailand, Journal of Wildlife and Biodiversity, 8(4), 311-328. DOI:
https://doi.org/10.5281/zenodo.13835301
Abstract
A study on the size of the home range and factors affecting the appearance of the fishing cats
using satellite collars at Khao Sam Roi Yot Wetland (KSRYWL), Prachuap Khiri Khan
Province, Thailand was conducted from June 2022 to February 2024. This study aimed to
determine the size of the home range and factors affecting the appearance of this species.
Twelve fishing cats, five adult males and seven adult females, were trapped and tagged with
satellite collars. The average home range size based on Autocorrelated Kernel Density
Estimation (AKDE) of male and female were 6.29 km2 (range 3.40–9.69 km2) and 2.83 km2
(range 1.35–5.25 km2), respectively. The study's results found that factors affecting the
appearance of fishing cats were topographic factors, including Elevation Slope, NDVI, and
anthropogenic factors, including distance to villages, distance to aquaculture, and distance to
abandoned aquaculture, which were significantly significant. The results of the comparative
study between male and female leopard fish found that the factor. Distance to aquaculture had
a greater effect on the presence of male leopard fish than female leopard fish, meaning that
male leopard fish are more vulnerable to threats from humans. The findings of this study can
be used for habitat protection and management activities related to species conservation.
Keywords: Autocorrelated Kernel Density Estimation; Khao Sam Roi Yot Wetland; satellite
collars; step-selection functions
Research Article
312 | Journal of Wildlife and Biodiversity 8(4): 311-328 (2024)
Introduction
Various human activities near urban environments increasingly impact mid-sized felids and
other mammalian carnivores (Kowalski et al., 2015; Decœur et al., 2023). Urban expansion and
rising human activity in and around preserved habitats may lead to shifts in the spatial
distributions of these species (Kowalski et al., 2015; Chang et al., 2023; Ren et al., 2023;
Broquet et al., 2024). The sensitivity of mammalian carnivores to urbanization varies by
species, with some disappearing from fragmented landscapes, while others show greater
tolerance to human disturbances (Łopucki et al. 2019).
The fishing cat is classified as a medium-sized wild cat belonging to the family Felidae
(Chakraborty et al., 2020). It is predominantly found in South and Southeast Asia. It is classified
as a vulnerable species (IUCN, 2024), with a critically endangered status in the national
conservation status of Thailand (Office of Natural Resources and Environmental Policy and
Planning, 2023). Fishing cats show a discontinuous population distribution in mangroves,
wetlands, rivers, and swamps in South and Southeast Asia (Mukherjee et al. 2016). The species
are at a high risk of extinction and are thought to be among the most vulnerable of the medium-
sized wild cats in Southeast Asia (Mukherjee et al., 2016), possibly due to the very low overlap
of their occupied habitat with protected areas and other conservation interventions, rather than
an inherently higher susceptibility shown by the other small cats (Duckworth et al., 2014).
Fishing cats are primarily nocturnal but may also be active during the day (Ganguly & Adhya,
2022). Unlike other cat species, the fishing cat is unique because 70% of its diet consists of fish
(Adhya et al., 2024; Wongson et al., 2024). It also feeds on birds and insects (Wongson et al.,
2024). The home range of a male individual fishing cat is larger (16 to 22 km2) than a female
(4 to 8 km2) (Cutter, 2015). Like other wild carnivore species worldwide, fishing cats in the
Khao Sam Roi Yot Wetland (KSRYWL) are significantly impacted by human activities such
as agricultural expansion, housing development, and infrastructure in their habitats (Phosri et
al., 2021; Bombieri et al., 2023). These factors threaten the survival of the fishing cat population
(Chowdhury et al., 2015). Anthropogenic disturbance is introduced to the landscape, and it can
influence the movement of animals and the spatiotemporal distribution (Xiang et al., 2019;
313 | Journal of Wildlife and Biodiversity 8(4): 311-328 (2024)
Cowan et al., 2024). Understanding how the species relate spatially to the environment and
human disturbances is critical to assigning areas for conservation and developing conservation
strategies (Nagy-Reis et al. 2017). Adhya et al. (2022) reported that important factors that
affected the identifying priority areas for the conservation of fishing cats are wetlands (18.36%)
and elevation (17.15%) are the most important variables determining the ecological niche of
the fishing cat. Identifying factors influencing the distribution of and interactions within
carnivore communities is important for understanding how they are affected by human activities
(Carricondo-Sanchez et al., 2019).
Studies on factors related to the species’ apparent presence in the area using satellite collars are
essential knowledge for management that is still lacking. Therefore, understanding the home
range size and habitat selection in areas with prominent human activity is crucial for managing
and conserving the environment and the endangered fishing cat population in the changing
environment. The objective of this study was to examine the home range size of fishing cats in
the most critical conservation areas in Thailand using radio satellite signals and to analyze
factors affecting their appearance. The results can be used to inform habitat management
strategies for the conservation of fishing cats in changing environments.
Martial and methods
Ethics statement
This study was conducted with permission from the Department of National Parks, Wildlife
and Plant Conservation (License No. 0909.204/10153, dated May 20, 2022) as part of a project
on the ecology of fishing cats using radio satellite collars in the area surrounding Khao Sam
Roi Yot National Park (KSRYNP). The research was also approved by the Office of the
National Research Council (Permission Document No. 0401/9980, dated June 7, 2022).
Additionally, the researchers possess certification for completing animal rights training in
experimental work from the Office of the National Research Council, Thailand.
Study Area
Covering approximately 98 km², the KSRY is located on the coast of the Prachuap Khiri Khan
Province. It was the first national marine park in Thailand (Figure 1). Khao Sam Roi Yot means
the Mountain with three hundred peaks and refers to a series of limestone hills along the Gulf
of Thailand, with the highest at 605 meters ASL. The northwest corner of the mountain range
314 | Journal of Wildlife and Biodiversity 8(4): 311-328 (2024)
is called Thung Sam Roi Yot and is mainly a freshwater marsh covering nearly 37% of the
national park, making it the largest wetland area in Thailand, inside and outside the KSRY. The
major agricultural products in the region include coconuts, pineapples, and mixed orchards,
whereas rice is cultivated in smaller areas. Aquaculture and traditional shrimp and fish farms
(Phosri et al., 2021). The climate in the study area can be divided into 3 seasons: winter between
October - February with average temperatures between 18 and 25 degrees Celsius, summer
between March-May with temperatures between 23 - 32 degrees Celsius, and the rainy season
between June - September has temperatures between 20 - 30 degrees Celsius with an average
rainfall of 800-1,200 mm/year. The wetlands of KSRY are registered as wetlands of
international importance, number 2238 on the date. On January 8, 2008.
Figure 1. Map showing the location of the study area at the KSRY and all fishing cat data
points conducted between June 2022 and February 2024.
315 | Journal of Wildlife and Biodiversity 8(4): 311-328 (2024)
Animal Capture and Collar
Animal capture was conducted with permission from the Department of National Parks Wildlife
and Plant Conservation. Were captured using standardized cage-trapping methods (Serieys et
al., 2023). Traps were primarily set in human-impacted areas, specifically around traditional
shrimp and fishponds. Due to the heat, traps were closed during the days. Traps were thus
checked every morning, as well as during the late evening. Once captured, individuals were
immobilized using tiletamine hydrochloride plus zolazepam hydrochloride (4 mg/kg, Zoletil®)
and xylazine hydrochloride (0.3 mg/kg) by a veterinarian and the lead veterinarian further led
the capture. During capture, animals were sexed, and the age class was estimated as adult or
subadult based on tooth wear, animal size, and evidence of reproduction. Morphological
measurements were taken (Patumrattanathan, 2015). This study employed satellite collars (GPS
collars) from Jul 2022 to Nov 2022, specifically, Lotek Wildcell SLG GPS collars (Lotek
Wireless Inc., Newmarket, ON, Canada) equipped with cotton spacers to ensure that the collars
eventually fell off. GPS locations were collected every four hours and data were downloaded
from Lotek’s web interface or directly from recovered collars (n = 2). Due to the poor
performance of the Lotek Iridium collars from April 2023 to February 2024, we used e-obs 1C-
Light GPS and triaxial accelerometers UHF collars (e-obs GmbH, Oberhachinger, Gruenwald,
Germany). Collars were programmed to collect GPS locations every 10 minutes when the cats
were active and every 4 hours when the cats were at rest. The e-obs collars were fitted with a
cotton spacer to ensure a collar drop-off within one year. The collar weighs no more than 3%
of the animal's body weight (Kenward, 2000; Ratnayaka et al., 2022). Detailed information on
the fishing cats can be found in Table 1.
Data Analysis
Home range size
The home ranges of fishing cats were estimated using an autocorrelated kernel density estimator
(AKDE) method, which involved applying the continuous-time movement modeling (ctmm)
1.2.0 package (Fleming & Calabrese, 2017; Fleming et al., 2022) in R version 4.3.2 software
(R. Core Team, 2022). calculated home ranges for individual fishing cats employing (50%
316 | Journal of Wildlife and Biodiversity 8(4): 311-328 (2024)
AKDE) contours as a means of defining the core area, (75% AKDE), utilization area, and (95%
AKDE) contours to specify the designated surrounding home range(Hinton et al., 2021;
Sukmasuang et al., 2020; Prayoon et al., 2024).
Factors affecting the appearance
We used step selection functions (SSF) to investigate factors affecting the appearance. These
were used to assess the factors affecting the appearance of the fishing cats within their home
ranges. This method compares the available habitat features with selected locations and
identifies the key factors influencing appearance and movement patterns using the animal
movement tools (AMT) package (Avgar et al., 2016; 2017; Fieberg et al., 2021; Elie & Eric,
2018), and R version 4.3.2 (R Core Team, 2022). The factors affecting the appearance of the
fishing cats’ model were assessed using nine variables (Table 1) to explain the factors affecting
habitat selection by fishing cats.
Table 1. Environmental variables for assessing the factors affecting the appearance of the fishing cats
Variable
Description
Source
Elevation
Extract and load data from Google Earth
engine
https://earthengine.google.com/
Slope
Extract and load data from Google Earth
engine
NDVI
Extract and load data from Google Earth
engine
Distance to road
Euclidean distance to the major and minor
road
Royal Thai Survey
Department, Thailand
Distance to villages
Euclidean distance to the villages
Land Development
Department, Thailand
Distance to marsh
and swamp
Euclidean distance to the marsh and
swamp
Distance to
aquaculture
Euclidean distance to the shrimp and fish
farm
Distance to
abandoned
aquaculture
Euclidean distance to abandoned
aquaculture
Results
The study captured and tagged with satellite collar 12 adult fishing cats (5 adult males and 7
adult females). This study used two collar companies: Lotek 3 and E-obs 9. The dataset included
15,461 locations for all fishing cats, covering all sexes (6,209 males and 9,252 females). Further
317 | Journal of Wildlife and Biodiversity 8(4): 311-328 (2024)
details on the fishing cat characteristics, capture data, and telemetry locations are provided in
Table 2.
Table 2. Detailed information on the 12 adult fishing cats around the KSRYWL, Thailand.
Fishing
cat ID
Weight
(kg.)
Length(mm)
Collar
Company
Number of
telemetry
location
Telemetry period
Head
and
body
Tail
Neck
Chest
Female
FFC01
8.25
735
220
255
400
Lotek
196
20 Jul 2022–16 Nov 2022
FFC02
8.82
720
240
285
420
Lotek
119
08 Aug 2022–16 Sep 2022
FFC04
8.06
740
240
275
386
E-obs
2486
01 May 2023–21 Sep 2023
FFC05
9.56
792
255
280
410
E-obs
1959
21 Jun 2023–27 Sep 2023
FFC06
8.25
770
260
265
389
E-obs
842
21 Apr 2023–11 Jun 2023
FFC08
10.06
765
282
290
432
E-obs
1807
23 Nov 2023–27 Feb 2024
FFC10
7.62
730
310
260
380
E-obs
1843
18 Nov 2023–27 Feb 2024
Average
8.7
750.3
258.1
272.9
402.4
Total
9252
Male
MFC03
6.23
680
305
225
369
E-obs
1632
23 Aug 2023-3–02 Nov
2023
MFC07
14.92
850
345
315
470
E-obs
1922
22 Jun 2023–10 Nov 2023
MFC09
11.5
823
282
315
446
Lotek
208
26 Jun 2022–11 Aug 2022
MFC11
12.36
865
312
325
464
E-obs
2066
30 May 2023–25 Sep 2023
MFC12
13.65
765
320
360
500
E-obs
381
20 Nov 2023–13 Dec 2023
Average
11.7
796.6
312.8
308.0
449.8
Total
6209
Home range estimation
Home range characteristics obtained for each fishing cat are shown in Figure 2. Each map shows
the (50%AKDE) core area, (75%AKDE) utilization area, and (95%AKDE) home range area of
each fishing cat. Male individuals' average estimated home range area was 6.29 km2 (SD ±2.85;
range: 3.40 – 9.69 km2). The utilization area averaged 2.91 km2 (SD ±1.55), while the core area
averaged 1.29 km2 (SD ±0.64). Female individuals exhibited a smaller average estimated home
range area of 2.83 km2 (SD ±1.22; range: 1.36 – 5.25 km2). The utilization area for females was
1.51 km2 (SD ±0.69) on average, with a core area averaging at 0.73 km2 (SD ±0.33) in Table 2.
The home ranges of fishing cats are shown in Table 3. Variograms, which depict the spatial
autocorrelation of data points (Figure 2), show all 12 fishing cats using the AKDE method
individually.
318 | Journal of Wildlife and Biodiversity 8(4): 311-328 (2024)
Table 3. Estimated 95, 75, and 50 percentile autocorrelated kernel density estimation (AKDE) around
the KSRYWL, Thailand
Fishing cat ID
No. of
locations
95% AKDE (Area km2)
75% AKDE
(Area km2)
50%AKDE
(Area km2)
low
est.
high
Female
FFC01
196
2.36
3.21
4.20
1.65
0.79
FFC02
119
3.85
5.25
6.86
2.87
1.37
FFC04
2486
2.17
2.96
3.87
1.60
0.75
FFC05
1959
0.99
1.36
1.77
0.65
0.30
FFC06
842
1.86
2.53
3.31
1.39
0.67
FFC08
1807
1.69
2.30
3.01
1.17
0.54
FFC10
1843
1.59
2.17
2.83
1.21
0.66
Total
9252
2.07
2.83
3.69
1.51
0.73
Male
MFC03
1632
7.11
9.69
12.66
4.63
1.89
MFC07
1922
4.45
6.06
7.92
2.24
0.89
MFC09
208
2.49
3.40
4.45
1.76
0.92
MFC11
2066
2.69
3.66
4.79
1.39
0.67
MFC12
381
6.35
8.66
11.32
4.53
2.06
Total
6209
4.62
6.29
8.23
2.91
1.29
Table 4. Average estimated 95, 75, and 50 percentile autocorrelated kernel density estimation (AKDE)
in KSRYWL, Thailand.
Fishing
cat
Area (km2)
Home range area
(95%AKDE)
Utilization area
(75%AKDE)
Core area (50%AKDE)
Average area
Female
2.83
1.51
0.73
[min 1.36; max 5.25]
[min 0.65; max 2.87]
[min 0.30; max 1.37]
Male
6.29
2.91
1.29
[min 3.40; max 9.69]
[min 1.39; max 4.63]
[min 0.67; max 1.89]
319 | Journal of Wildlife and Biodiversity 8(4): 311-328 (2024)
Figure 2. Home range of 12 collar fishing cat individuals in the KSRY, Thailand, from 2022 to 2024.
Using an autocorrelated kernel density estimator (AKDE) the figure shows three confidence levels
(95%, 75%, and 50%). The study of seven fishing cat females (FFC01, FFC02, FFC04, FFC05, FFC06,
FFC08, and FFC10) and five fishing cat males (MFC03, MFC07, MFC09, MFC11, and MFC12).
320 | Journal of Wildlife and Biodiversity 8(4): 311-328 (2024)
Figure 3. Variograms of 12 collared fishing cats between semi-variance and time lag. Fishing cats
comprise seven females (FFC01; FFC02; FFC04, FFC05; FFC06; FFC08; FFC10) and five males
(MFC03, MFC07, MFC09, MFC11; MFC12). The shading represents the 95% confidence intervals.
Factors affecting
The factors affecting the appearance of fishing cats were investigated using step-selection
functions (SSF) with topographic variables, including elevation, slope, and normalized
difference vegetation index (NDVI). Human-related variables included distance to roads,
villages, marshes, swamps, aquaculture, and abandoned aquaculture. The results of habitat
selection for female and male individuals (Table 5).
321 | Journal of Wildlife and Biodiversity 8(4): 311-328 (2024)
Discussion
Home range estimation
This study found that the size of the habitat area of males (6.29 km2) was approximately twice
as large as that of females (2.83 km2). The results of this study were in the same direction as
the results of past studies in the area where it was found that the habitat area of males was larger
than that of female fishing cats (Cutter, 2015; Patumrattanathan, 2015). However, what is
different from the results of past studies is that the habitat area size obtained from this study
was larger than that of female fishing cats (Cutter, 2015; Patumrattanathan, 2015).
Patumrattanathan (2015) more than 1 time (2.91 km2 for males and 1.51 km2 for females). It is
also similar to the results of the study conducted by Cutter (2015), who investigated the home
range size and core area results using the Fixed Kernel method at 95% and 50% were 4.01–
13.53 km2 (FK95%) and core area 0.9–3.05 km2 (FK50%). The female home range size is
1.98–6.78 km2, and the core area is 0.51–1.36 km2 (Cutter, 2015). These differences are due to
differences in the equipment and analysis methods used in the study. The results of this study
are larger than those of Ratnayaka et al. (2024), who reported that the mean (±SD) LoCoH
home range size for all three resident fishing cats was 1.17±0.74 km2. The mean (±SD) LoCoH
core area was 0.35±0.09 km2. The mean (±SD) KDE home range was 2.63±1.04 km2, and the
mean (±SD) KDE core area was 0.53±0.21 km2 conducted in Colombo, Sri Lanka, which is
similar to the home range sizes of female fishing cats in this study. The differences in home
range size between the sexes are due to variations in resource utilization reflecting the cost-
benefit trade-offs in behavioral decisions (Cattarino et al., 2016). Considering the home range
distribution, we cannot conclude the relationships between males and females in terms of home
range overlap that reflects kinship, mating, and pup-rearing behavior in fishing cats. This should
be followed up with further studies. However, the difference in size between male and female
home ranges corresponds with their polygamous mating system, where one male breeds with
several females (Hedmark et al., 2007).
322 | Journal of Wildlife and Biodiversity 8(4): 311-328 (2024)
Table 5. Coefficients of the fitted step-selection function of female and male fishing cats and data
combined for factors affecting the appearance around the KSRYWL, Thailand
Factors
Coefficient
Exp (coef)
SE (coef)
Z
P-value
All fishing cat
Topographic variable
Elevation
-0.058852
0.942846
0.012693
-4.637
3.54e-06 ***
Slope
0.030952
1.031436
0.00992
3.12
0.00181 **
NDVI
0.184192
1.202247
0.009003
20.459
< 2e-16 ***
Human related variables
Distance to road
-0.008498
0.991538
0.009418
-0.902
0.3669
Distance to villages
-0.024521
0.975777
0.012503
-1.961
0.04986 *
Distance to marsh and swamp
-0.005857
0.99416
0.008946
-0.655
0.51263
Distance to aquaculture
-0.031134
0.969346
0.011827
-2.632
0.00848 **
Distance to abandoned aquaculture
-0.029997
0.970448
0.009767
-3.071
0.00213 **
Female
Topographic variable
Elevation
0.02711
1.02748
0.01061
2.554
0.01064 *
Slope
-0.04288
0.95803
0.01352
-3.171
0.00152 **
NDVI
0.15984
1.17332
0.0117
13.665
< 2e-16 ***
Human related variables
Distance to road
-0.02769
0.97269
0.0163
-1.699
0.0894
Distance to villages
-0.02002
0.98018
0.02008
-0.997
0.3188
Distance to marsh and swamp
-0.01379
0.9863
0.01773
-0.778
0.4367
Distance to aquaculture
-0.03667
0.964
0.02259
-1.623
0.1045
Distance to abandoned aquaculture
-0.03458
0.96601
0.01564
-2.211
0.0271 *
Male
Topographic variable
Elevation
0.05467
1.05619
0.01226
4.459
8.22e-06 ***
Slope
-0.06972
0.93265
0.01966
-3.546
0.000391 ***
NDVI
0.21733
1.24275
0.01422
15.286
< 2e-16 ***
Human related variables
Distance to road
0.01029
1.01034
0.01651
0.623
0.53307
Distance to villages
-0.03607
0.96458
0.01867
-1.932
0.05341
Distance to marsh and swamp
-0.0124
0.98768
0.01384
-0.896
0.37028
Distance to aquaculture
-0.04117
0.95966
0.01591
-2.587
0.00968 **
Distance to abandoned aquaculture
-0.03312
0.96742
0.01448
-2.287
0.02217 *
The asterisk indicates the significance of the factor (P < 0.1, * P < 0.05, ** P < 0.01, and *** P < 0.001)
Factors affecting the appearance
323 | Journal of Wildlife and Biodiversity 8(4): 311-328 (2024)
Topographic variables
Elevation has a highly significant negative coefficient. This suggests that fishing cats tend to
avoid areas with higher elevations, possibly because of associated factors, such as limited
access to water sources and increased competition. The slope exhibited a significantly positive
coefficient, indicating that fishing cats prefer habitats with steeper slopes. Steeper slopes may
offer better cover, refugee, or hunting opportunities for fishing cats, aligning with their habitat
preferences and ecological requirements. The NDVI showed an extremely significant positive
coefficient. This indicated a strong preference for fishing cats in areas with higher vegetation
density or greener landscapes. Such areas will likely provide suitable habitat conditions
including food resources, shelter, and protection from distractions.
Human-related variables
This study suggests that fishing cats exhibit adaptability and inhabit a diverse range of habitat
types, including those subjected to disturbance. The coefficient of the distance to the road
indicates that this relationship is not statistically significant. This suggests that the presence or
proximity of roads does not significantly influence habitat selection by fishing cats. The
coefficient of distance to villages was negative. Fishing cats tend to select habitats closer to
villages, possibly due to factors such as prey availability near human settlements or adaptation
to anthropogenic landscapes. The negative coefficients for distance to marsh and swamp were
not statistically significant. This indicates that proximity to marshes and swamps did not
influence fishing cat habitat selection in the study area. The coefficient of distance to
aquaculture was negative. Fishing cats use areas close to aquaculture sites. This is because most
of the area is aquaculture, a source of prey for fishing cats. The negative coefficient for the
distance to aquaculture indicates that fishing cats select areas closer to active aquaculture sites.
This implies that active aquaculture affects habitat selection by fishing cats, possibly because
of prey availability linked to aquaculture activities. The negative coefficient for distance from
abandoned aquaculture sites indicates that fishing cats select abandoned ones. This indicates
that abandoned aquaculture affected the selection of habitats for fishing cats. This may be
because of Low human interference factors disturbance and that natural prey can be found in
the area.
324 | Journal of Wildlife and Biodiversity 8(4): 311-328 (2024)
Conclusion
This study offers new knowledge on home ranges and habitat selection around KSRY. Home
range and utilization patterns of male and female individuals emphasize the importance of
considering sex-specific behaviors and ecological requirements in wildlife research. The fishing
cat habitat selection results were influenced by human-related variables, particularly proximity
to villages, active aquaculture sites, and abandoned aquaculture sites. These findings provide
insights into the impact of human activities on fishing cat habitats and highlight the importance
of conservation and management efforts.
Acknowledgments
The Ayers Wild Cat Conservation Trust funded this study. This is part of a study on Fishing
Cats (Prionailurus viverrinus) using radio-collar satellite telemetry in Khao Sam Roi Yot
National Park areas. The authors thank the Department of National Park, Wildlife, and Plant
Conservation, Thailand, for providing us with the necessary permission and support to conduct
this study.
References
Adhya, T., Bagaria, P., Dey, P., Muñoz, V.H., Ratnayaka, A.A.W., Thudugala, A., Aravind,
N.A., & Sanderson, J.G. (2022). On the edge: Identifying priority areas for conservation
of fishing cats, a threatened wetland felid, amidst rapidly altering freshwater landscapes.
bioRxivhttps://doi.org/10.1101/2022.01.16.476498.
www.biorxiv.org/content/10.1101/2022.01.16.476498v1
Adhya, T., Banerjee, S., Dey, P., Nanda, S., Santra, S., & Nesha, I. (2024). Density estimation
of the globally threatened fishing cat Prionailurus viverrinus through a participatory
science approach in the Chilika lagoon, eastern India. Endang Species Res 54: 1–13.
https://doi.org/10.3354/esr01322.
325 | Journal of Wildlife and Biodiversity 8(4): 311-328 (2024)
Avgar, T., Lele, S.R., Keim, J.L., & Boyce, M.S. (2017). Relative Selection Strength:
Quantifying Effect Size in Habitat and Step‐selection Inference. Ecol Evol 7: 5322–5330.
https://doi.org/10.1002/ece3.3122.
Avgar, T., Potts, J.R., Lewis, M.A., & Boyce, M.S. (2016). Integrated step selection analysis:
bridging the gap between resource selection and animal movement. Methods Ecol Evol
7: 619–630. https://doi.org/10.1111/2041-210x.12528
Bombieri, G., Penteriani, V., Almasieh, K., Ambarlı, H., Ashrafzadeh, M.R., Das, C.S.,
Dharaiya, N., Hoogesteijn, R., Hoogesteijn, A., Ikanda, D., Jędrzejewski, W., Kaboli, M.,
Kirilyuk, A., Jangid, A.K., Sharma, R.K., Kushnir, H., Lamichhane, B.R., Mohammadi,
A., Monroy-Vilchis, O., Mukeka, J.M., Nikolaev, I., Ohrens, O., Packer, C., Pedrini, P.,
Ratnayeke, S., Seryodkin, I., Sharp, T., Palei, H.S., Smith, T., Subedi, A., Tortato, F.,
Yamazaki, K., & Delgado, M.D.M. (2023). A worldwide perspective on large carnivore
attacks on humans. PLoS Biol. 21(1):e3001946.
https://doi.org/10.1371/journal.pbio.3001946.
Cattarino, L., McAlpine, C.A., & Rhodes, J.R. (2016). Spatial scale and movement behaviour
traits control the impacts of habitat fragmentation on individual fitness. Journal of Animal
Ecology 85(1): 168–177. https://doi.org/10.1111/1365-2656.12427
Carricondo-Sanchez, D., Odden, M., Kulkarni, A., & Vanak, A.T. (2019). Scale-dependent
strategies for coexistence of mesocarnivores in human-dominated landscapes. Biotropica
51(5):781-791. https://doi.org/10.1111/btp.12705
Chang, S., Su, K., Jiang, X., You, Y., Li, C., & Wang, L. (2023). Impacts and predictions of
urban expansion on habitat connectivity networks: A multi-scenario simulation approach.
Forests 14(11):2187. https://doi.org/10.3390/f14112187.
Chowdhury, S.U., Chowdhury, A., Ahmed, S., & Muzaffar, S.B. (2015). Human-fishing cat
conflictsand conservation needs offishing cats in BangladeshEds: Christine & Urs
Breitenmoser. P 4-6. . In Cat News 62. Eds: Christine & Urs Breitenmose. Cat Specialist
GroupKORA, Thunstrasse 31, 3074 Muri,Switzerland.
www.researchgate.net/publication/343471962_Human-
fishing_cat_conflicts_and_conservation_needs_of_fishing_cats_in_Bangladesh
Cowan, M.A., Dunlop, J.A., Gibson, L.A. et al. (2024). Movement ecology of an endangered
mesopredator in a mining landscape. Mov Ecol 12: 5. https://doi.org/10.1186/s40462-
023-00439-5.
Cutter, P. (2015). Fishing Cat Ecology: Food Habits, Home Ranges, Habitat Use and Mortality
in a Human-Dominated Landscape around Khao Sam Roi Yot, Peninsular Thailand.
[Dissertation]. University of Minnesota, USA.
Decœur, H., Amir, Z., Mendes, C.P., Moore, J.H., & Luskin, M.S. (2023). Mid-sized felids
threatened by habitat degradation in Southeast Asia. Biol Conserv 283: 110103.
https://doi.org/10.1016/j.biocon.2023.110103.
326 | Journal of Wildlife and Biodiversity 8(4): 311-328 (2024)
Duckworth, J., Lynam, A., & Breitenmoser-Würsten, C. (2014). Non-Panthera Cat Species in
South-East Asia: Present Knowledge and Recommendations. P62-67. In Cat News 62.
Eds: Christine & Urs Breitenmose. Cat Specialist GroupKORA, Thunstrasse 31, 3074
Muri,Switzerland
Fieberg, J., Signer, J., Smith, B., & Avgar, T. A. (2021). How to Guide for Interpreting
Parameters in Habitat‐selection Analyses. Journal of Animal Ecology90: 1027–1043,
https://doi.org/10.1111/1365-2656.13441.
Ganguly, D., & Adhya, T. (2022). How fishing cats Prionailurus viverrinus Bennett, 1833 fish:
describing a felid'’s strategy to hunt aquatic prey. Mammalia 86: 182-189.
https://doi.org/10.1515/mammalia-2020-0133.
Hedmark, E., Persson, J., Segerström, P., Landa, A., & Ellegren, H. (2007). Paternity and
mating system in wolverines Gulo gulo. Wildlife Biology 13(Suppl 2):13–30.
https://doi.org/ 10.2981/0909-6396(2007)13[13:PAMSIW]2.0.CO;2
Hinton, J.W., Proctor, C., Kelly, M.J., Van Manen, F.T., Vaughan, M.R., &Chamberlain, M.J.
(2016). Space Use and Habitat Selection by Resident and Transient Red Wolves (Canis
Rufus). PLoS ONE 11: e0167603. https://doi.org/10.1371/journal.pone.0167603.
IUCN. (2024). The IUCN Red List of Threatened Species. Version 2024-1. www.iucnredlist.
Kenward, R.E. (2000). A Manual for Wildlife Radio Tagging; Academic press. ISBN 0-08-
057420-3.
Kowalski, B., Watson, F., Garza, C., & Delgado, B. (2015). Effects of landscape covariates on
the distribution and detection probabilities of mammalian carnivores. J Mammal 96(3):
511–521. https://doi.org/10.1093/jmammal/gyv056
Łopucki, R., Klich, D., & Kitowski, I. (2019). Are small carnivores urban avoiders or adapters:
Can they be used as indicators of well-planned green areas? Ecological Indicators 11:
1026-1031. https://doi.org/10.1016/j.ecolind.2019.02.016.
Mukherjee, S., Appel, A., Duckworth, J.W., Sanderson, J., Dahal, S., Willcox, D.H.A., Herranz,
M.V., Malla, G., Ratnayaka, A., Kantimahanti, M., Thudugala, A., Thaung, R., &
Rahman, H. (2016). Prionailurus viverrinus IUCN Red List of Threatened Species.
https://doi.org/10.2305/IUCN.UK.2016-2.RLTS.T18150A50662615.en.
Nagy-Reis, M.B., Nichols, J.D., Chiarello, A.G., Ribeiro, M.C., & Setz, E.Z.F. (2017).
Landscape use and co-occurrence patterns of Neotropical Spotted Cats. PLoS ONE 12(1):
e0168441. https://doi.org/10.1371/journal.pone.0168441.
Office of Natural Resources and Environmental Policy and Planning. (2023). Threatened
species of Thailand. Tipco Tower Building 2, 8th Floor, No. 118/1 Rama 6 Road, Phaya
Thai Subdistrict, Phaya Thai District, Bangkok 10400 www.onep.go.th/open-data-
reddatavertibrates/
Patumrattanathan, S. (2015) Ecology of Fishing Cat (Prionailurus Viverrinus) by Community
Participation on Its Conservation in Kui Buri District, Prachuap Khiri Khan Province,
327 | Journal of Wildlife and Biodiversity 8(4): 311-328 (2024)
Thailand. Doctor of Philosophy (Sustainable Land Use and Natural Resources
Management), Major Field: Sustainable Land Use and Natural Resources Management,
Interdisciplinary Graduate Program. Graduate School, Kasetsart University.
Phosri, K., Tantipisanuh, N., Chutipong, W., Gore, M.L., Giordano, A.J., & Ngoprasert, D.
(2021). Fishing Cats in an Anthropogenic Landscape: A Multi-Method Assessment of
Local Population Status and Threats. Global Ecology and Conservation 27: e01615.
https://doi.org/10.1016/j.gecco.2021.e01615.
Prayoon, U., Suksavate, W., Chaiyes, A., Paansri, P., Siriaroonrat, B., Utara, Y., Tipkantha,
W., Baicharoen, S., Jairak, W., Kaewkhunjob, E., Chaisonkhram, C., Maikaew, U.,
Ieamsaard, G., Sripiboon, S., Winitpornsawan, S., Thunhikorn, S., Wanna, N., Sriracha,
S., Sornsa, T., Chokcharoen, R., & Duengkae, P. (2024). Home Range and Habitat
Utilization of Gaur (Bos Gaurus) in Transition Zone between Protected Forest and
Human-Dominated Landscape, Eastern Thailand. Global Ecology and Conservation 50:
e02811. https://doi.org/10.1016/j.gecco.2024.e02811.
R. Core Team R. (2022). R: A Language and Environment for Statistical Computing. R
Foundation for Statistical Computing, Vienna. www.R-project.org
Ratnayaka, A.A.W., Serieys, L.E.K., Prasad, T., Leighton,,G.R.M., Sanderson, J.G., & Leung,
L.K.P. (2022). Urban Habitat Use and Home Ranges of Fishing Cats in Colombo, Sri
Lanka. Mamm Biol 102: 271–277. https://doi.org/10.1007/s42991-021-00198-z.
Ren, Q., He, C., Huang, Q., Zhang, D., Shi, P., & Lu, W. (2023). Impacts of global urban
expansion on natural habitats undermine the 2050 vision for biodiversity. Resources,
Conservation and Recycling 190: 106834.
https://doi.org/10.1016/j.resconrec.2022.106834.
Serieys, L.E.K., Bishop, J.M., Rogan, M.S., Smith, J.A., Suraci, J.P., O’Riain, M.J., & Wilmers,
C.C. (2023). Anthropogenic activities and age class mediate carnivore habitat selection
in a human-dominated landscape. iScience 26: 107050.
https://doi.org/10.1016/j.isci.2023.107050
Sukmasuang, R., Suksavate, W., Songsasen, N., Khiowsree, N., Charaspet, K., Pla-Ard, M.,
Chanachai, Y., Thomas, W., & Srinopawan, K. (2020). Home Range, Movement and
Habitat Selection of Dholes (Cuon Alpinus) in Khao Yai National Park, Thailand.
Biodiversitas 21(12): 5915-5926. https://doi.org/10.13057/biodiv/d211257.
Wongson, T., Khaewphakdee, S., Mongkonsin, W., Serieys, L.E.K., Wong, W-M., Yindee, M.,
Pattanarangsan, R., Suksavate, W., Promkuntod, P., Klakhaeng, C., & Sukmasuang, R.
(2024). Dietary habits of fishing cats in a human-dominated wetland in Coastal Thailand.
Biodiversitas 25(7): 2788-2797. https://doi.org/10.13057/biodiv/d250650.
Xiang, Y., Meng, J., You, N., Chen, P. & Yang, H. (2019). Spatio-temporal analysis of
anthropogenic disturbances on landscape pattern of tourist destinations: A case study in
328 | Journal of Wildlife and Biodiversity 8(4): 311-328 (2024)
the Li River Basin, China. Sci Rep 9: 19285. https://doi.org/10.1038/s41598-019-55532-
w.
