Twenty infrared cameras were installed in the study area (Figure 1). The
cameras were placed 30–90 cm above the ground. The height can not only
capture the entire saplings but also obtain a full shot of the ungulates
browsing them. Camera parameter settings were as follows: photo (three
photos) + video (15s). After the camera was installed, the weeds in
front of the shooting area were removed to prevent misshooting and
blocking the lens (Zhao et al., 2020). The monitoring period lasted 2.5
years.
FIGURE 1 Location of the Muling National Nature Reserve in northeast
China
2.2.3 Environment variable data
According to research needs, environmental variables are divided into
four categories:
(1) Land-use types: Scrub, evergreen coniferous forest, deciduous
coniferous forest, mixed coniferous forest, deciduous broad-leaved
forest, and farmland, were obtained using Landsat 8 remote sensing image
interpretation.
(2) Anthropogenic disturbance factors: Rural roads and forest trails
were obtained by vectorization in ArcGIS 10.8 software using forest
topographic maps.
(3) Topographic factors: Elevation, aspect, and slope direction were
obtained by mask extraction of a 30m resolution digital elevation model
(DEM) using the spatial analysis module of the ArcGIS 10.8 software.
(4) Water sources: Streams were obtained by vectorization in the ArcGIS
10.8 software using forested topographic maps.
2.2.4 Data analysis
2.2.4.1 Foraging rhythms of the wapiti and Siberian roe deer
Taking the average time of sunrise and sunset of the local year as the
demarcation, the day was divided into day and night, with reference to
the local climate. May to October was classified as the warm season, and
November to April was classified as the cool season. The daily and
annual foraging rhythms were analyzed in this study by using the kernel
density estimation method (Chen et al., 2019) with the overlap package
and activity package of R. Delta4 values were selected for calculations
when both the sample sizes compared were ≥75, and the Delta1 values were
selected when the size was <75. In addition, the degree of
temporal overlap between the activities of the two species was
classified as high overlap when Delta >0.75, medium overlap
when Delta<0.50<Delta<0.75, and low overlap
when Delta<0.50 (Monterroso et al., 2014).
The foraging patterns of the wapiti and Siberian roe deer on Japanese
yew saplings were analyzed by calculating the relative abundance index
for the day and night (D RAI), month
(M RAI), and season (S RAI)
(Liu et al., 2022). The formulas were as follows:D RAI=(D ij/N i)100,M RAI=(M ij/N i)100, andS RAI=(S ij/N i)100.D ij denotes the number of independent foraging
occurrences of species i in the time period j (both day and night),M ij denotes species i in the time period j
(annual), and S ij denotes species i in the time
period j (cold and warm seasons).
2.2.4.2 Foraging habitats suitability prediction
The systematic resampling method was used to analyze the points to avoid
spatial autocorrelation caused by too close a distance between them, to
standardize the data of the environmental variables, and to conduct
Spearman correlation analysis with SPSS 19.0. The selected points and
environmental variables were used to predict suitable foraging habitats;
75% of the points were used for the construction of the model, 25%
were used for validation, and the model was run for 10 cycles. This was
evaluated using the cutting method and its comprehensive contribution.
The evaluation criteria used were as follows: AUC range from 0.5 to 0.6
was considered as failing, from 0.6 to 0.7 was poor, from 0.7 to 0.8 was
fair, from 0.8 to 0.9 was good, and from 0.9 to 1.0 was excellent. All
operations were performed using MaxEnt 3.3.
The suitable habitats for the Japanese yew and ungulates were
reclassified using ArcGIS 10.8. The average value of the maximum
training sensitivity and specificity after 10 modes of operation was
used as the threshold for the distribution of suitable foraging habitats
(Li, 2022). The habitats of Japanese yew were divided into two
levels—0–0.153 were unsuitable habitats, and 0.153–1 were suitable.
The foraging habitats of the ungulates were divided into three
levels—0–0.55 were unsuitable foraging areas, 0.55–0.75 were
generally suitable and 0.75–1 were suitable.
2.2.4.3 Foraging characteristics of the two ungulates and their effects
on saplings
Taking Japanese yew as the center, the study area was extended in eight
directions—east, west, south, north, northeast, southeast, northwest,
and southwest. The sapling points were recorded along with the
information on whether foraging occurred or not within 50m in each
direction. A total of 71 points were collected and plotted using the
fmsb package in R. The characteristics of ungulates foraging on saplings
were summarized using infrared camera data. The t-test and
Kolmogorov-Smirnov test were used to analyze the following: (1) whether
the monthly relative abundance index of the two ungulates foraging for
young trees was different; (2) whether there was a difference in the
distance between the two ungulates foraging on the young tree and the
mother tree; and (3) whether the growth height of the main branches, the
number of new branches, and the growth amount of lateral branches after
foraging by the two ungulates were different. All operations were
performed using R 4.2.1.
RESULTS3.1 Foraging time strategies of the two ungulatesThe diurnal foraging activity index of the wapiti was higher than that
of the Siberian roe deer (D RAI=92.3%), and the
nocturnal feeding activity index of the Siberian roe deer was also
higher. There were two peaks in the diurnal activity rhythms of the
wapiti foraging on saplings, which were from 7:00 to 8:00 and from
14:00 to 15:30, with the overall shape of an ”M,” and the size of the
peaks varied. There were three peaks for the roe deer, from 6:30 to
9:30, 15:30 to 18:30, and 24:00 to 2:00, with the entire peak being
wavy. In terms of the degree of overlap, the highest overlap indices
were observed at 7:00 and 15:00, and temporally the degree of foraging
was moderate (Dhat1=0.67) (Figure 2).