Wildlife Research Institute of Heilongjiang Province

Harbin, China

Wildlife Research Institute of Heilongjiang Province

Harbin, China
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Zhou S.,Northeast Forestry University | Zhou S.,Wildlife Research Institute of Heilongjiang Province | Zhang M.,Northeast Forestry University | Sun H.,Wildlife Research Institute of Heilongjiang Province | And 6 more authors.
Acta Theriologica Sinica | Year: 2010

We estimated population size and performed a preliminary analysis of habitat selection of wild boar using line transects in Eastern Wanda Mountains, Heilongjiang Province, China from November 18, 2008 to March 20, 2009. We randomly set 40 sampling sites with 200 transect lines to collect field data on wild boars and their habitat at thirteen forestry farms operated by the Dongfenghong Forest Bureau. We estimated population density at 0. 175 individuals/km2 and abundance at 546-680 individuals. They were distributed in an elevation range of 300 to 800 m. The distribution areas of wild boars were mainly concentrated in Hekou, Qiyuan, Qingshan, Wulindong, Dumuhe, Haiyinshan, and Donglin forest farms. The population sizes and densities of wild boars were 0. 3720 individuals/km2 and a total of 1302 individuals in 1989, 0. 3423 individuals/km2 and a total of 1198 individuals in 1989. We found marginal evidence that the population size decreased slowly from 1989 to 2002, then decreased rapidly from 2002 to 2008. On the other hand, the percentage of wild boar tracks lest in snow in various forest stands shown that wild boars choose habitats of middle slope position a sunny aspect, slope angle less than 5°, 25 to 50 percent shelter and canopy density, vegetation coverage more than 30 percentage. Wild boars prefer broadleaf forest and shrub. Four threatening factors, including illegal hunting to wild boar, forest harvesting, nut collection, and predation by Amur tigers contributed to declining population size and habitat degradation.

Zhou S.-C.,Northeast Forestry University | Zhou S.-C.,Wildlife Research Institute of Heilongjiang Province | Zhang M.-H.,Northeast Forestry University | Yin Y.-X.,Wildlife Research Institute of Heilongjiang Province | Ren M.-F.,Wildlife Research Institute of Heilongjiang Province
Beijing Linye Daxue Xuebao/Journal of Beijing Forestry University | Year: 2010

Based on field data collected by transect lines, winter habitat selection by roe deer(Capreolus capreolus) was studied using a Resource Selection Index(RSI) and Resource Selection Functions(RSF) in the eastern Wandashan mountains, Heilongjiang Province during the winters from November to December of 2007 and 2008. We set 110 systematic transect lines with interval distances of 2 km, each 5 km in length. The following location characteristics were collected at active locations of roe deer and control locations: habitat type, food abundance, slope, slope position, aspect, elevation, shelter class, canopy, degree of coverage, tree density, diameter at breast height(DBH), shrub density and distance to human disturbance. The results showed that roe deer preferred mixed coniferous-broadleaf forests(Si=0.37), mixed broad-leaf forests(Si=0.19), moderate shelter class(35%-70%)(Si=0.46), moderate canopy degree(35%-70%)(Si=0.61), moderate coverage degree(10%-20%)(Si=0.27), slope(<25°)(Si=0.18), sunny aspect(Si=0.44), middle slope position(Si=0.14), large numbers of tree(>0.06 trees/m2)(Si=0.59), lower diameter of tree(<15 cm)(Si=0.66) and more distance to human disturbance(>500 m)(Si=0.12, Si=0.30). The roe deer avoided mixed coniferous forests(Si=-0.56) and did not spot croplands(Si=-1.00). A random selection action of roe deer was known for the shrub habitat(Si=-0.05), canopy degree(<35%)(Si=-0.03), coverage degree(<10%)(Si=0.04). The resource selection function is a logistic regression model: logit(P)=-11.17+0.71 × shelter class+3.24 × slope+0.59 × food abundance -0.62 × distance to human disturbance -0.25 × DBH+0.01 × shrub density+0.07 × slope position. We estimate a habitat selection probability: P=elogit(P)/(1+elogit(P)).

Zhou S.,Northeast Forestry University | Zhou S.,Wildlife Research Institute of Heilongjiang Province | Zhang M.,Northeast Forestry University | Sun H.,Northeast Forestry University | Yin Y.,Wildlife Research Institute of Heilongjiang Province
Shengtai Xuebao/ Acta Ecologica Sinica | Year: 2011

Predicting how many individuals of a predatory species can survive within a given area, based on prey biomass, is important in understanding whether or not significant threats are related to prey availability. The study of ungulate populations and, particularly, reliable estimates of ungulate biomass are necessary for effective conservation and management of the wild Amur tiger. Dense forests, mountainous landscape and low detection probabilities preclude the use of direct count techniques in estimating ungulate populations, while direct methods are expensive and time-consuming. Indirect sampling, by counting footprints or snow tracking, is a widely use, reliable and inexpensive way of estimating ungulate populations and calculating biomass. Amur tiger-prey relationships are so close that data on prey (ungulate) availability may be used to reliably predict the tiger population and determine any threats or risks to the endangered Amur tiger. Little is known about the relationship between the Amur tiger population and ungulate biomass in the easte Wanda Mountains of Heilongjaing Province, China. Therefore, we collected data on the population size of three ungulate species (wild boar Susscrofa, red deer Cervus elaphus and roe deer Capreolus capreolus) by establishing 240 line transects within 48 random sampling sites during late winter 2008 to early spring 2009. The results show that prey biomass, represented by the three ungulate species, was 1 85 849. 00-205335. 00 kg, including 74767. 50-87 825. 00 kg from wild boar (502-606 adults and 209-210 sub-adults), 79744. 50-85984.50 kg from red deer (331-357 adults and 67-72 sub-adults) and 31337. 00-31525. 5 0 kg from roe deer (810-815 adults and 202-203 sub-adults). In the study area, the estimated total biomass of all prey species was 209 619. 89-231 598. 24 kg. The prey biomass, represented by three ungulate species, could support 5.22-6.92 Amur tiger individuals and the estimated total biomass of all prey species could support 5.89-7.81 tigers, assuming 8% biomass as the food demand of Amur tigers in the eastern Wanda Mountains. Finally, to evaluate the accuracy and precision of the line transect surveys, we analyzed the relationship between footprint frequency and sampling effort by bootstrap analysis, The differences in the coefficients of variation for footprint frequency were as follows: wild boar: 37. 9 8% for 1-120 line transects and 2. 7 4% for 121-240 line transects; red deer: 17. 41% for 1-150 line transects and 2. 8 6% for 151 -240 line transects; roe deer: 3 9. 7 2% for 1 -115 line transects and 3. 84% for 116-240. Trend analysis indicated that population sizes could reasonably be established from 120 line transects for wild boar (sampling distance: 600 km), 150 line transects for red deer (sampling distance: 750 km) and 115 line transects for roe deer (sampling distance: 675 km). Sampling effort, both in terms of the number of footprints and intensity of sampling, had a marked effect on the accuracy and precision of the survey results. These findings will provide scientific guidelines for the estimation of prey (ungulate) population size and conservation of the Amur tiger.

Meng G.,Northeast Forestry University | Zhang M.,Northeast Forestry University | Zhou S.,Wildlife Research Institute of Heilongjiang Province
Shengtai Xuebao/ Acta Ecologica Sinica | Year: 2013

Growing density of wild boar (Sus scrofa) led to human-wild boar conflict in Fenghuang Mountains National Nature Reserve. Aiming at determining density of wild boar and estimating the nutritional carrying capacity, on one hand we performed a study to analyze forage components of wild boar by line transect backward snow track survey, former records and observe method to identified food habit in field survey during late winter to early spring of 2009 and 2010 in the reserve; and on the other hand, available plants biomass was analyzed. We located 30 line transects,(total length 134km) each width for 100 meters, length for 3-5km. On each line transect, we designed large sample plots (10m*10m) at 200m intervals, and each large sample plot was partitioned into five small sample plots (1m×1m). When tracing backward the snow tracks, we identified as one individual, while, what if we met several tracks left on the same transect between 30m, we followed until they parted away and the number was clear enough for counting. Nutritional carrying capacity of wild boar was determined and calculated by total energy from integrating data of crude protein, fiber and crude fat. Per gram crude protein and fiber euqals to 16. 74kJ and crude fat 37.66kJ respectively. The daily energy requirement of each wild boar was based on the research of the domestic pig. Also the data of fecals, beding sites, feeding sites of wild boar were collected as ancillary factors to get more precise results. Results showed that wild boars fed mainly on Equisetum hiemale, Pinus koraiensis, Juglans mandshurica, Quecusmongolica, Padus racemosa, Corylus heterophlla, Carex spp. and Aralia elata. The nature reserve could provide 7.375×107 MJ energy for wild boar population and the energy requirement of every wild boar was (14 677. 698±409. 92) MJ in winter. According to energy from habitat and that required by wild boar, we figured out that the winter nutritional carrying capacity and optimum population density of wild boar should be 1 006±28 individuals and (3. 79±0. 11) ind/ km2 respectively. However, we found more than 30 iron knots made for trapping wild boar and 2 individuals were poached by local people during the field survey. Therefore, we added 20% risk coefficient to got a more precision result, considering the death of wild boar from human disturbance (such as poaching and trapping). In this condition, the optimum population of wild boar should be at about (603±17) individuals and the optimum population density was (2. 27±0. 06) individuals/ km2. Finally we found out the actual population size of wild boar was 596±155((2. 24±0. 58) ind/ km2), and habitat carrying capacity was around actual population size of wild boar. We suggested that the wild boar couldn't be hunted and managers should take actions to prevent habitat degeneration by wild boar overabundance.

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