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Number of terrestrial leeches with human-bait method and environmental variables in peninsular Malaysia


Hosaka, Tetsuro; Jambari, Asrulsani; Nakabayashi, Miyabi; Numata, Shinya (2022), Number of terrestrial leeches with human-bait method and environmental variables in peninsular Malaysia, Dryad, Dataset,


This is the data on the number of terrestrial leeches (brown Haemadipsa sp.) collected and environmental variables at the sampling site in Endau Rompin National Park in Malaysia. We investigated relative abundance of terrestrial leeches repeatedly at 99 sampling points in the tourism area of ERNP from February 2019 to September 2020. We counted the number of leeches at each sampling point based on the human-bait method, wherein the researcher is considered as the bait (Schnell et al. 2015, Fahmy et al. 2019). To activate the leeches before sampling, the leaf litter was blown upon and stirred with a twig in a 1.5-m radius from the sampling point. A researcher then stood at the sampling point and collected all the leeches attracted and attached to the body in a five-minute sampling session (Kendall, 2012). We collected the leeches in a vial and measured the diameter of the sucking cup of each individual as a proxy for leech size: <1.5 mm = small, 1.5–3.0 mm = medium, and >3.0 mm = large. At the start of the sampling session, any leeches on the researcher’s body were removed and discarded. The leeches collected in the vial were all released at the sampling point after being counted and measured. Since leeches could detect host up to 2.0 m in our pilot survey, the relative abundance here can be regarded as a rough estimate of density of active leeches per 12.6 m2 (an area within 2-m radius).

The microenvironmental variables at each sampling point and time were measured to examine their effects on active leech relative abundance. These variables included air humidity, air temperature (°C), light intensity (lux), altitude (m), distance to the nearest river (m), distance to the nearest rain flow path (i.e., small valley through which water would flow in heavy rain) (m), percentage of bare ground (%), percentage of canopy openness (%), mean litter layer depth (mm), and topsoil moisture (%). Air humidity, air temperature, and light intensity were measured using a handheld Illuminance UV recorder (TR-74Ui). The distances to the nearest stream and river were calculated using ArcMap 10.2.2. (ESRI Inc.). The altitude was measured using a handheld Garmin geographic positioning system (GPS). The percentage of canopy openness and bare ground were measured using hemispherical photography via smartphone cameras equipped with “fisheye” lenses (Bianchi et al. 2017) in the near-vertical skyward and downward directions, respectively. The litter layer depths were visually measured using a ruler. Soil moisture was measured directly at every sampling point using a soil moisture meter (Takemura Soil Moisture Meter DM 18). The percentages of bare ground, canopy openness, litter layer depth, and topsoil moisture, were recorded three times at randomly selected points around the sampling point, and the average value was considered for the analyses.

To measure human and wildlife relative abundance, we installed passive infrared camera traps (O'Connell et al. 2011) (Ltl Acorn Ltl-6210MC, Cams Co., Ltd., Tokyo, Japan), which were triggered automatically by movement, at 24 on-trail sampling points. The camera was operated from February 2019 to September 2020 (560 days and 8,230 trap nights). The sampling points included trails with a gradient of tourist-use frequency. Each camera was mounted approximately 30–40 cm above the ground (Luo et al. 2019) to capture a wide range of animals of different sizes (Mugerwa et al. 2013).

The cameras were set up in video mode with 15 s in video length and 30 s in the interval between videos, which was sufficient to record the group activity of potential hosts (wildlife or humans). The number of individuals in each video was counted. To prevent double-counting individuals, a one-hour interval sequence was introduced between videos of a specific individual of the same species (Azlan & Sharma 2006). Each animal in the videos was identified at the species level, based on Francis (2008). Due to low image resolution, we could not identify smaller animals, such as bats and rats at the species level.