Mammal Study 31: 1–8 (2006) © the Mammalogical Society of Japan

Habitat variables of the Japanese identified by regression tree model

Noriko Tamura1,*, Norio Takahashi2 and Nobuhiko Satou3 1 Tama Forest Science Garden, FFPRI, Todori 1833, Hachioji, Tokyo 193-0843, Japan 2 National Research Institute of Far Seas Fisheries, 5-7-1 Shimizu Orido, Shizuoka 424-8633, Japan 3 Ecosystem Conservation Society-Japan, Otowa Bldg. 2-30-20 Nishi-ikebukuro, Toshima-ku, Tokyo 171-0021, Japan

Abstract. Habitat variables of the Japanese squirrel, lis Temminck, were analyzed by the regression tree method based on radio-tracking data. The results suggest that the tended to use the sites with more walnut trees from July to December, and more evergreen trees in middle layer from January to June for feeding. For nest sites, they tend to use sites with more evergreen trees in the upper layer, preferably having DBH (diameter at breast height) of at least 30 cm. These results are consistent with observations in previous field studies.

Key words: habitat model, habitat requirement, Japanese squirrel, tree regression.

Habitat destruction is one of the most serious factors (USFWS 1980). Habitat suitability index (HSI) models causing population decrease of various kinds of wildlife. (USFWS 1981), developed to quantify the habitat quality For wise landscape management to conserve species, in the HEP, have been widely used by several agencies habitat requirements should be identified through quan- for environmental impact assessments in the United titative measurement of environmental variables. States (Morrison et al. 1998). Japanese squirrels, Sciurus lis TEMMINCK, are en- Recently, discussion has started in Japan about the demic species occurring only on the islands of Honshu, importance of habitat evaluation and application of the Shikoku, and Kyushu in Japan (Wilson and Reeder HEP (Tanaka 2001; Ecosystem Conservation Society- 1993). Recently, however, the populations in Kyushu Japan 2004). Habitat units for the species in the HEP are and western Honshu have become extinct (Kawamichi determined as the product of “habitat quality” (calculated 1997). One potential cause of local of the spe- by the HSI model) and “habitat quantity” (area) (USFWS cies is rapid habitat changes in forest environments, such 1981). As the first step to make an HSI model of the as replacement of natural forest with artificial plantations Japanese squirrel, we attempted to select habitat vari- and fragmentation of natural forests by roads (Yatake ables for their life requisites. and Tamura 2001). The regression tree method is a modern statistical The tree squirrels depend on forest resources for their technique, which allows flexible data analysis without food and nest sites, and thus changes in forest environ- making any assumption about statistical distribution. ments may greatly affect their occurrence. Specifically, This feature makes this method appropriate for analyzing the Japanese squirrels selectively use natural or second- data for wildlife-habitat relationship inherently involv- ary forests but seldom use artificial plantations (Tamura ing non-linearity and high-order interactions (De’ath and 1998). Therefore, conservation of this species is a key to Fabricius 2000). Applications of regression tree meth- protecting the forest environment. ods specific to ecological data and analyses of wildlife- Habitat Evaluation Procedure (HEP) is one approach habitat relationships, are found in De’ath and Fabricius to evaluate habitat value, that was developed in the (2000), Andersen et al. (2000), and Rejwan et al. (1999). 1970’s by the United States Fish and Wildlife Service De’ath and Fabricius’s paper also provides a good over-

*To whom correspondence should be addressed. E-mail: [email protected] 2 Study 31 (2006) all review of tree models. 50 m × 50 m, including the quadrat in its center. The purpose of this study is to identify the habitat Field observation showed that the squirrels use the requirements of the Japanese squirrel using a regression large evergreen trees as nest sites (Yatake and Tamura tree model based on quantitative field data. 2001), so the number of large evergreen trees (DBH > 30 cm) and the mean DBH of evergreen trees in the upper Study area layer were added to the variables. A summary of the 10 vegetation variables considered in our habitat analyses is Field studies were conducted at the experimental for- shown in Table 1. est of the Forestry and Forest Products Research Insti- One of the 50 quadrats contained planted Japanese tute’s Tama Forest Science Garden (TFSG, elevation white pines (Pinus parviflora), a favorite food source of 170–265 m, 57 ha) located in Hachioji, western Tokyo, the squirrels. Because this pine is not native to the low- Japan. The experimental forest comprises six types of elevation mountains of the Kanto district, the analysis vegetation; (1) natural forest dominated by Abies firma was conducted using data from 49 quadrats excluding and Quercus glauca, (2) secondary forest dominated by this Japanese white pine plantation. evergreen trees, such as Q. glauca, mixed with decidu- ous Q. serrata, (3) conifer plantations of Cryptomeria Radio-tracking japonica and Chamaecyparis obtusa, (4) deciduous for- Trapping was conducted 2–4 months intervals from est dominated by Q. serrata and Prunus jamasakura, (5) September 1991 to April 2001. To capture squirrels, 15 an arboretum, and (6) shrub/grassland (Tamura 1998). to 20 live-traps were set on tree branches (1–3 m in Tree species, the number of trees planted, and the year of height) and placed at 50–100 m intervals throughout the planting in each lot were recorded in the Afforestation entire trap area. Walnuts with peanut butter were used as Plan of TFSG. bait. Each trapped squirrel was weighed and the sex, maturity and reproductive status were recorded by the Methods following methods. The length of the scrotum was measured to discriminate between mature (≥ 25 mm) and Vegetation survey immature (< 25 mm) males. For females, the size and Fifty quadrats (10 m × 10 m) for the vegetation sur- shape of teats were used as criteria to estimate whether vey, were set up at random points throughout the study they had experienced reproduction. We defined an indi- area (Fig. 1). Each lot had at least one quadrat; two or vidual that was captured more than two times in different more quadrats were set in large lots containing different months as a resident individual. types of vegetation. We identified the species and mea- A collar with a radio-transmitter (50 MHz band, ZTS- sured the DBH (diameter at breast height) and the height 7D, Nakane Studio, Kamakura, Japan) was placed on 10 for all of the woody plants existing in a quadrat from males and 9 females. All radio-collared squirrels were April to June in 1996. We divided vegetation into three mature residents. Location was obtained by triangula- layers: Upper layer (DBH ≥ 23 cm, or height ≥ 10 m), tion methods using a receiver (FT-690 mk2, Yaesu) and Middle layer (3 cm ≤ DBH < 23 cm, or 3 m ≤ height < a dipole receiving antenna (Nakane Studio). The dis- 10 m), and Lower layer (DBH < 3 cm, or height < 3 m). tance between a squirrel and a receiver ranged from 20 The number of woody plants and evergreen plants in to 50 m. The location of each individual was traced from each layer were recorded. The number of species in the the time it left its nest in the morning to the time it middle and upper layers was counted because the species returned to the nest in the evening. richness may be important for squirrels as an indicator of We traced each squirrel for a total of 5 days within a food availability. In addition, the number of walnut trees period of 50 days. Data for females whose parturition was counted, because walnuts are the main food of squir- had occurred within the previous 2 weeks were omitted rels in this study site (Tamura 2004). Japanese squirrels from analysis, because they seldom left their nests. often transport walnuts to hoard and eat later and the The Japanese squirrels move from the nest sites to the mean transport distance was found to be ca. 20 m from feeding sites soon after sunrise and stay there for an hour the source tree (Tamura and Shibasaki 1996). Therefore, or more in the early morning (Nishigaki and Kawamichi the counting of walnut trees was expanded to the area 20 1996). Thus, we defined a site where the squirrel stayed m beyond each side of a quadrat; the total area was thus for eating for at least 1 hr in the early morning as a feed- Tamura et al., Habitat of Japanese squirrel 3

Fig. 1. Habitat utilization of the Japanese squirrels estimated by radio-tracking. Squares are the quadrates of vegetation study established in each lot. White squares indicate the quadrates in lots where squirrels did not use, while black squares do ones where squirrels used as feeding sites in Period 1 (a), Period 2 (b), and nest sites (c), respectively. ing site. After the early morning, squirrels occasionally sional feeding sites in the analyses, because it does not foraged in a short time during resting, grooming, nest- always follow that squirrels selected the sites for feeding. making, and other activities. We omitted such occa- In cases where after feeding at one site, a squirrel moved 4 Mammal Study 31 (2006)

Table 1. The mean value of 10 vegetation variables for 49 quadrats (10 m × 10 m) at TFSG, Hachioji, Western Tokyo. Upper layer (DBH ≥ 23 cm, or height ≥ 10 m), middle layer (3 cm ≤ DBH < 23 cm, or 3 m ≤ height < 10 m), lower layer (DBH < 3 cm, or height < 3 m).

Variables Mean SE Min Max NU: No. trees in upper layer 7.61 0.61 2 22 NM: No. trees in middle layer 8.65 1.56 0 53 NL: No. trees in lower layer 43.63 5.33 0 150 EU: No. evergreen trees in upper layer 4.67 0.66 0 22 EM: No. evergreen trees in middle layer 4.74 0.95 0 35 EL: No. evergreen trees in lower layer 33.94 4.19 0 113 SP: No. tree species in middle and upper layer 5.08 0.43 1 11 DBH: The mean DBH (cm) of evergreen trees in upper layer 21.84 2.35 0 59.1 ELU: No. evergreen trees more than 30 cm DBH 1.37 0.28 0 8 WAL: No. walnuts trees 1.2 0.65 0 28 and began feeding again at another site in the morning, independent and dependent variables. Details of this we considered all such feeding sites in a single day in the method can be found in Breiman et al. (1983). analyses. As predictor variables of regression model, we chose The main food of the squirrels in the present study site the 10 vegetation measurements described in the Vege- is the Japanese walnut, Juglans ailanthifolia, throughout tation survey section above (Table 1). Habitat variables the year (Tamura et al. 1999). However, the frequency for both feeding and nesting sites were separately ana- of feeding on walnuts accounted for 60.0–84.6% in all lyzed based on these predictor variables. The response feeding bouts from July to December, while it did only variables chosen were frequencies with which squirrels 14.3–42.9% from January to June. In the latter period, used feeding and nesting sites, which were determined squirrels depend on various species of plants, insects, from radio-tracking. Details on the methods for obtain- and mushrooms (Tamura 2004). Therefore, our data ing the frequency data were given in the Radio-tracking analyses were divided into the following two periods section above. Analyses of feeding habitat preference based on diet: Period 1 (from July to December), and were conducted dividing the study period into two time Period 2 (from January to June). Eight females and horizons: Period 1 (July–December) and Period 2 (Janu- seven males were radio-tracked in Period 1 and 6 ary–June) (see the Radio-tracking section for details). females and 4 males in Period 2. The frequency of Data analyses were performed with the statistical each squirrel’s selection of feeding sites in the 49 lots package SPSS AnswerTree (SPSS Japan Inc. 2002). was analyzed in relation to the 10 vegetation variables There are several computational algorithms for the described above (Table 1). tree structure method. We used the CART algorithm We also located the nest site for each individual during (Breiman et al. 1983) for our habitat preference analyses. the 5 days of radio-tracking. Nests themselves were not To obtain the best tree structure, an excessively large tree always found by sight, so we defined the location where is first “grown” (that is, a data set is divided into as many squirrels stop moving until sunset as a nest site. Japa- subsets as split criteria permit), and then the size of the nese squirrels have 2–7 nests at a time (Nishigaki and “tree” is decreased (“pruning”) by evaluating the total Kawamichi 1996). Frequency data of each squirrel’s residual (Breiman et al. 1983). selection of nest sites were also analyzed in the same To compare the results from tree regression models way as the feeding site analyses. with ones from more familiar statistical methods, we also analyzed the same data using forward stepwise multiple Regression tree model regression methods (Statview 5.0). The regression tree method divides a data set into a hierarchical sequence of subsets based on certain criteria. Results The results of analysis are graphically represented in the form of a tree diagram which explicitly depicts high- Site selection by squirrels ordered interactions and nonlinear relationships between The number of feeding sites used by each female in 5 Tamura et al., Habitat of Japanese squirrel 5 days ranged from 3 to 5 with a mean of 4.4 in Period 1, 7 males had 2 to 5 (mean 3.3) nest sites in the 5 days in and 1 to 4 with a mean of 2.8 in Period 2. The number of Period 1, and 1 to 4 (mean 2.3) nest sites in Period 2. In feeding sites used by each male in 5 days was 1 to 7 with total, the 9 females selected 16 lots and the 7 males a mean of 4.3 in Period 1, and 2–5 with a mean of 4.3 in selected 13 lots as nest sites during both periods (Fig. Period 2. 1c). Nine of these lots were used as nest sites by both For all females, 51 feeding sites (18 lots) were located females and males. in Period 1, and 25 feeding sites (15 lots) in Period 2 (Figs. 1a, 1b). Eight of these sites were used as feeding Regression tree results sites in both periods. For males, 38 feeding sites (14 The resultant regression trees for female and male lots) were located in Period 1, and 21 feeding sites (14 feeding sites show different structures in selected vari- lots) in Period 2 (Figs. 1a, 1b). Nine of these sites were ables between Periods 1 and 2 (Fig. 2a and 2b for used as feeding sites in both periods. In both Periods 1 females, 2d and 2e for males). For female feeding sites and 2, nine feeding sites were used by both females and in Period 1, the regression tree has 5 terminal nodes and males. a coefficient of determination (calculated as 1 – [error Each female used 1 to 4 (mean 2.5) nest sites in 5 days variance]/[total variance]) of 0.67. The two highest in Period 1, and 1 to 3 (mean 1.8) in Period 2. Three predicted frequencies of feeding are found in sites where males used nests outside of the study area, and the other the number of walnut trees (WAL) > 1.5, and in sites

Fig. 2. Results of regression tree analyses. (a) female feeding sites in Period 1 (b) female feeding sites in Period 2 (c) female nest sites (d) male feeding sites in Period 1 (e) male feeding sites in Period 2, and (f) male nest sites. Alphabetical abbreviations in each split indicate environmental variables described in Table 1. The numerals on the split are the values that determine the split. The numerals in the terminal node indicate the predicted value. 6 Mammal Study 31 (2006) where WAL ≤ 1.5, the number of evergreen trees in the Variables WAL and EM were also identified as impor- middle layer (EM) > 4, and the number of trees in the tant by tree regression analyses for Periods 1 and 2, upper layer (NU) > 13.5 (Fig. 2a). respectively (Fig. 2a and 2b). The other variables, SP In Period 2, the regression tree has the same number of and NU, did not appear in the regression trees. terminal nodes as Period 1 and a coefficient of determi- For male feeding, WAL and EM were only factors nation of 0.57. The highest feeding frequency occurs in selected in Period 1 (P < 0.001, R2 = 0.71), and Period 2 sites where the number of evergreen trees in middle layer (P < 0.001, R2 = 0.24), respectively. As with the female (EM) > 4.0, the number of evergreen trees in the upper feeding, these two variables were selected by regression layer (EU) ≤ 6.5, and the number of trees in the lower trees for the Periods 1 and 2 (Fig. 2d and 2e). Similar to layer (NL) ≤ 55.0 (Fig. 2b). In EM ≤ 4.0, the number of the multiple regression results, male occurrence in feed- evergreen trees more than 30 cm DBH (ELU) > 3.5 were ing sites is modeled by fewer variables and simpler struc- relatively selected. tures in tree regressions (Fig. 2d and 2e). For male feeding in Period 1, the two highest predic- The nest sites of females were explained by the num- tions are found in sites where the number of walnuts ber of large trees in the upper layer (NU) and SP (P < trees (WAL) > 20.5, and in sites where 1.5 < WAL ≤ 0.01, R2 = 0.17), while those of males were explained 20.5 and the number of trees in lower layer (NL) > 29.5 by the number of evergreen trees in the upper layer (EU) (Fig. 2d). There are 5 terminal nodes and the coefficient (P < 0.05, R2 = 0.06). In regression tree analyses, other of determination is 0.80 in this regression tree. The variables were further selected, but NU and EU were regression tree for male feeding in Period 2 has the sim- identified as the most important (Fig. 2c and 2f). plest structure of 2 terminal nodes and the lowest coeffi- Measures for model explanation power were greater cient of determination of 0.37 (Fig. 2e). In Period 2, for regression trees than for multiple regression in both male feeding mostly occurred in sites where the number Periods 1 and 2 (comparing coefficients of determination of evergreen trees in middle layer (EM) > 13.5. for regression trees with R2 for multiple regression). Structures of the resultant regression trees are different Correlations among the 10 variables are summarized in selected habitat variables and the number of terminal in Table 2. The number of evergreen trees in the middle nodes for female and male nest sites (Fig. 2c for female layer (EM) was correlated with the number of tree spe- and 2f for male). The two highest predicted frequencies cies (SP), and the number of trees in the middle layer. are found in sites where the number of trees in upper The number of trees in the upper layer (NU) was corre- layer (NU) > 15.5, and in sites where NU ≤ 15.5, the lated with the number of evergreen trees in the upper number of trees in the middle layer (NM) > 18.5, and the layer (EU). NU was correlated with the mean DBH number of large evergreen trees (ELU) > 1.5 for female (DBH). The number of trees in the lower layer (NL) was nesting (Fig. 2c). This tree has 4 terminal nodes and a correlated with the number of evergreen trees in the coefficient of determination of 0.61. lower layer (EL). In contrast, the two highest frequencies occur in sites where the number of evergreen trees in upper layer (EU) > 3.5 and the number of trees in lower layer (NL) > 80, ≤ Table 2. Correlation among 10 variables. Abbreviations are as and in sites where EU > 3.5, NL 80 and the number of shown in Table 1. trees in middle layer (NM) > 17.5 for male nesting (Fig. 2f). This regression tree consists of 6 terminal nodes NU NM NL EU EM EL SP DBH ELU and its coefficient of determination is 0.72. NU NM –0.10 Multiple regression results NL –0.04 –0.07 Normal multiple regression analysis was used to EU 0.75 –0.35 –0.11 select, the number of walnuts (WAL) and the number of EM 0.02 0.84 –0.11 –0.27 tree species (SP) to explain variance in frequency of EL 0.01 –0.09 0.97 –0.10 –0.05 feeding site use in Period 1 for females (P < 0.001, R2 = SP –0.17 0.72 –0.17 –0.38 0.61 –0.17 0.23). The number of trees in the upper layer (NU) and DBH –0.51 –0.03 –0.26 –0.19 –0.18 –0.32 0.16 the evergreen trees in the middle layer (EM) were ELU –0.11 –0.23 0.03 0.16 –0.34 –0.02 –0.20 0.62 selected in Period 2 for females (P < 0.001, R2 = 0.21). WAL –0.08 –0.06 –0.26 –0.11 –0.02 –0.24 0.07 –0.09 –0.16 Tamura et al., Habitat of Japanese squirrel 7

Discussion ture (Yatake et al. 1999). In Tateshina, Nagano Prefec- ture, nests were built on evergreen trees from 4 to 18 m, Our regression tree analyses suggest that, for feeding with a mean of 10 m in height (Nishigaki and Kawamichi of both female and male Japanese squirrels, the number 1996). These observations support our results from the of walnuts (WAL) and the number of evergreen trees in regression tree method; that was they selected the site the middle layer (EM) were the most important habitat with large evergreen trees as nest sites. Among the iden- variables for Period 1 (July–December) and Period 2 tified habitat variables for male nesting, there is still (January–June), respectively. These results from the insufficient ecological interpretation of the number of regression tree analyses are consistent with field obser- trees in the middle layer (NM) and the number of species vations of diet items; Japanese squirrels depend almost (SP). exclusively on walnuts in Period 1, and eat hoarded Significant similarities in identified important habitat walnuts and other foods in Period 2 (Tamura 2004). variables for Japanese squirrels were found between the Japanese squirrels transport walnuts from deciduous multiple regression and regression tree analyses. How- walnut forests and store them in the forest floor covered ever, there are also noticeable differences. In prelimi- by evergreen trees in the middle layer (Tamura and narily studies, the multiple regression analysis may be Shibasaki 1996). The dense middle layer may serve as useful to obtain rough pictures of important habitat a shelter from predators when they retrieve the hoarded variables, but it cannot provide much detail about the food. In addition to WAL and EM, the number of trees relationships between habitat variables and squirrel in the upper layers (NU) and lower layers (NL) are occurrence, and high-order interactions among the vari- identified as important habitat variables, especially for ables. Multiple regression approaches often pose diffi- female feeding. culties even when analyzing effects of second-order Empirical studies also indicate that Japanese squirrels interactions between habitat variables because values often use middle and upper layers for feeding, suggesting needed for analyses are often missing. the importance of tree density and connection between In contrast, the regression tree analyses are robust middle and upper layers to safe movement around against missing values and allow us to detect nonlinear feeding sites (Yatake et al. 1999). Further, because the relationships and high-order interactions. Furthermore, squirrels often forage on the ground to collect fallen regression tree approaches can provide a framework for seeds and hoarded food, a forest floor moderately (not flexible modeling by assuming no particular statistical densely) covered by shrubs may be preferable for for- distributions. The results of our regression tree analyses aging (Yatake and Tamura 2001). suggest a nonlinear relationship between the occurrence Although there is not a large difference between the of Japanese squirrels and habitat variables (Fig. 2). Our sexes in habitat variables identified important for forag- results also imply that habitat selection of Japanese ing, males show a simpler relationship and interaction squirrels for foraging and nesting may be hierarchical with habitat variables than females, whose habitat selec- processes of behavioral response involving multiple hab- tion is affected by other variables than the amount of itat variables and high order interactions of the variables available food, especially in Period 2. An ecological (Fig. 2). interpretation of this may be that females are more cau- Although the regression tree method has several tious when feeding because they sometimes accompany advantages over multiple regression, it should be noted their young in Period 2, the reproductive season. The that, as with other statistical approaches, this method dense branches may help facilitate their locomotion and never identifies underlying mechanisms affecting an provide for their young to hide from predators. ’s habitat selection. It only detects correlation When making nests, female Japanese squirrels tend to between the animal’s preference and habitat variables. prefer sites with a larger number of trees in the upper Thus, conclusions on wildlife-habitat relationships should layer or sites with a larger number of big evergreen be derived only after careful examination of results from trees, while males tend to use sites with a large number both regression tree analyses and field studies. of evergreen trees in the upper layer. Nests of Japanese Rosenfeld (2003) maintains that the fitness conse- squirrels were found in evergreen trees such as Pinus quence is the only certain way to determine habitat densiflora, P. thunbergii, Cryptomeria japonica and requirements. In case of Japanese squirrels, however, Castanopsis cuspidata in Shimizu Park, Chiba Prefec- there are not enough data on reproductive success to 8 Mammal Study 31 (2006) compare the fitness consequence, although additional Ecology 81: 3178–3192. data may become available. Thus, we attempted to pro- Ecosystem Conservation Society-Japan. 2004. HEP Makes Environ- mental Impact Assessment More Scientific and Pursative: Its visionally determine habitat variables important for the Concepts and Some Examples. Gyosei Corporation, Tokyo, 206 foraging and nesting of Japanese squirrels as surrogates pp. (in Japanese). of habitat requirements based on field data of habitat Jones, P. F., Hudson, R. J. and Farr, D. R. 2002. Evaluation of a win- ter habitat suitability index model for elk in west-central Alberta. utilization. Further investigation will be needed to deter- Forest Science 48: 417–425. mine true habitat requirements of Japanese squirrels by Kawamichi, T. 1997. Red List of Japanese . Bun-ichi, Tokyo, 279 pp. (in Japanese). comparing reproductive success among different habi- Loukmas, J. J. and Halblook, R. S. 2001. A test of the mink habitat tats. suitability index model for riverine systems. Wildlife Society The present study site for the Japanese squirrel is Bulletin 29: 821–826. Mitchell, M. S., Zimmerman, J. W. and Powell, R. A. 2002. Test of a characterized by a mosaic forest landscape consisting of habitat suitability index for black bears in the southern Appala- various types of vegetation. This is a typical pattern chians. Wildlife Society Bulletin 30: 794–808. observed in low-elevation mountain in the Kanto District Morrison, M. L. Marcot, B. G. and Mannan, R. W. 1998. Wildlife- habitat Relationships: Concepts and Applications. The Univer- of Japan, where the forest environment has been greatly sity of Wisconsin Press, Madison, 343 pp. altered by intensive human disturbance over a long Nishigaki, M. and Kawamichi, T. 1996. Japanese squirrel. 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