J For Res (2015) 20:125–134 DOI 10.1007/s10310-014-0444-3
ORIGINAL ARTICLE
Chronosequential changes in species richness of forest-edgedwelling butterflies during forest restoration after swidden cultivation in a humid tropical rainforest region in Borneo Takao Itioka • Kohei Takenaka Takano • Keiko Kishimoto-Yamada • Taizo Tzuchiya • Yasuhiro Ohshima • Rai-ichiro Katsuyama • Masaya Yago Osamu Yata • Michiko Nakagawa • Tohru Nakashizuka
•
Received: 11 January 2013 / Accepted: 26 March 2014 / Published online: 3 May 2014 Ó The Japanese Forest Society and Springer Japan 2014
Abstract In contrast to the large number of studies addressing the effects of deforestation on insect diversity, few studies have focused on the recovery of diversity during forest restoration. In this study, we investigated the recovery, or chronosequential change, of butterfly species richness during forest restoration after cessation of swidden cultivation in a humid tropical rainforest region in Borneo. Through conducting censuses on butterflies at 21 study plots, placed at open habitats adjoining edges of forest stands that differed in elapsed years after cessation (\3, 5–13, and 20–60 year-old fallows, and isolated and largearea primary forests), we obtained presence or absence data
Electronic supplementary material The online version of this article (doi:10.1007/s10310-014-0444-3) contains supplementary material, which is available to authorized users. T. Itioka (&) K. Kishimoto-Yamada T. Tzuchiya Graduate School of Human and Environmental Studies, Kyoto University, Yoshida-Nihonmatsucho, Sakyo-ku, Kyoto 606-8501, Japan e-mail:
[email protected] K. T. Takano K. Kishimoto-Yamada M. Nakagawa T. Nakashizuka Research Institute for Humanity and Nature, Kita-ku, Kyoto 603-8047, Japan K. T. Takano T. Nakashizuka Graduate School of Life Sciences, Tohoku University, Aoba-ku, Sendai 980-8578, Japan K. Kishimoto-Yamada Graduate School of Arts and Sciences, The University of Tokyo, Komaba, Meguro-ku, Tokyo 153-8902, Japan
on 132 butterfly species. The cumulative number of observed species was significantly higher at two types of primary forests than at three types of fallows; significantly higher at two older fallows than the youngest fallows; and not significantly different between the two older fallows. The species number in the older fallows was less than half that of either type of primary forest. The numbers of species at the oldest fallows and isolated primary forests significantly decreased with distance from the large-area primary forests, and the majority of butterflies observed in the fallows were also observed in the primary forests. These results suggest that although the species richness of forest-edge-dwelling butterflies recovers in the initial 20 years of forest restoration, the recovery pace decreases thereafter and depends on the presence of large-area
Y. Ohshima Mie Prefectural Museum, 3060 Issinden-kozubeta, Tsu, Mie 514-0006, Japan R. Katsuyama The Research Institute of Evolutionary Biology, 2-4-28 Kamiyoga, Setagaya-ku, Tokyo 158-0098, Japan R. Katsuyama M. Yago The University Museum, The University of Tokyo, Hongo, Bunkyo-ku, Tokyo 113-0033, Japan O. Yata The Kyushu University Museum, 6-10-1 Hakozaki, Fukuoka 812-8581, Japan M. Nakagawa Graduate School of Bioagricultural Sciences, Nagoya University, Chikusa-ku, Nagoya 464-8601, Japan
Y. Ohshima R. Katsuyama O. Yata Graduate School of Social and Cultural Studies, Kyushu University, Motooka, Fukuoka 819-0395, Japan
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primary forest in the vicinity. It is suggested that forest fragmentation also decreases butterfly diversity. Keywords Assessment of insect species richness Butterfly assemblage Deforestation Southeast Asian tropics Swidden cultivation
Introduction Tropical rainforests are known to foster extremely speciesrich arthropod assemblages (Erwin 1982; Wilson 1992). Because globally most tropical rainforests are shrinking due to deforestation and anthropogenic degradation (Achard et al. 2002; Peres et al. 2006; Asner et al. 2009; Corlett and Primack 2011), the arthropod assemblages housed within them are also facing reduction in species richness and diversity (Dunn 2004; Sodhi et al. 2004, 2009; Gibson et al. 2011; Corlett and Primack 2011; Edwards et al. 2012). To devise conservation strategies that mitigate these risks, it is necessary to accurately assess what effects deforestation and forest degradation have on the species richness and composition of arthropods in tropical rainforests (Barlow et al. 2007; Asner et al. 2009). Indeed, many studies have estimated the effects of forest fragmentation, degradation due to selective logging, and deforestation on arthropod diversity in the tropics (e.g., Willott 1999; Vasconcelos et al. 2000; Willott et al. 2000; Davis et al. 2001; Shahabuddin et al. 2005; Barlow et al. 2007; Chazdon et al. 2009; Hawes et al. 2009; Hayes et al. 2009; Maeto et al. 2009; Berry et al. 2010; Woodcock et al. 2011). However, to date there is still a paucity of information on how arthropod diversity changes during the recovery process of forests—from the deforested status, to secondary, to primary—particularly in Southeast Asia compared with other tropical regions (e.g., Stork et al. 2003; Florens et al. 2010; Safian et al. 2011; but see Beck et al. 2002). In this study, we attempted to estimate the chronosequential change of butterfly assemblage biodiversity over the restoration process of forest vegetation—from slashand-burned areas, through secondary forests or fallows, to matured forests that are close to primary forests—by comparing diversity among the various types of forest stands that differed in years elapsed since cessation of swidden cultivation. Slash-and-burn or swidden cultivation is a common traditional style of agriculture in large parts of the Southeast Asian tropical rainforest regions. Butterflies have been frequently used as a taxonomic group indicating biodiversity of a target habitat or area (Cleary 2004; Schulze et al. 2004; Bobo et al. 2006; Fleishman and Murphy 2009; Bonebrake et al. 2010) because they are taxonomically well known, and because the
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species richness and diversity of butterflies are thought to depend on the physical condition and diversity of plants in the habitats (Kremen 1992). Effects of deforestation, logging, or other types of forest degradation on the diversity of butterflies in Southeast Asian tropical forests have also been addressed by many researchers (e.g., Hill et al. 1995; Willott et al. 2000; Lewis 2001; Ghazoul 2002; Cleary et al. 2005, 2009; Cleary and Mooers 2006; Hirowatari et al. 2007; reviewed in Koh 2007). Although such studies assessed the reduction in butterfly diversity due to selective logging or forest fragmentation, few so far have fully investigated changes in butterfly diversity over the recovery process of vegetation from swidden cultivation to forest maturation (but see Veddeler et al. 2005). To assess chronosequential changes in butterfly species richness over long-term recovery of forest vegetation, we chose a site in a Southeast Asian tropical rainforest and conducted censuses on the absence or presence of butterfly species at open spaces neighboring edges of various forest stands—large-area reserved primary forest, isolated primary forests, and fallows—that differed in years elapsed after cessation of swidden cultivation. Based on the data (assuming that the number of observed butterfly species represents the species density of butterflies), we analyzed the differences in cumulative number of observed butterfly species at each study plot and the similarity of species composition among forest types in relation to differential levels of forest maturation or vegetation recovery. To test for the effect of forest fragmentation, we also analyzed the effects of distance from the large-area primary forests on the cumulative number of butterfly species at some forest stands.
Materials and methods Study site Study sites were located in and around Lambir Hills National Park (LHNP) (4°20 –4°110 N, 113°500 –114°30 E; 20–150 m above sea level) in the northwest part of Borneo Island, Sarawak, Malaysia. The national park was mostly covered by primary lowland dipterocarp forest (Hazebroek and bin Abang Morshidi 2000; Yumoto and Nakashizuka 2005) and surrounded by secondary forests, dry fields and paddy fields, orchards, small-scale extensive rubber plantations, an oil palm plantation, and isolated primary forests stands (Ichikawa 2002, 2003; Nakagawa et al. 2006; Tanaka et al. 2007; personal observation, Fig. 1). The surrounding secondary forests mainly comprised fallows of slush-and-burn cultivation (Ichikawa 2002, 2003). In the isolated primary forest stands, almost no logging and swidden cultivation had been done during the last hundred
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or more years. The climate is humid-tropical, with a weak seasonal change in precipitation (Kumagai et al. 2005, 2009). The fauna of butterflies has been intensively investigated since 1994, and consequently 356 butterfly species have been recorded from LHNP (Itioka et al. 2009). Through the investigation, the first author became skilled in identifying species of many butterflies, particularly most of the target species in this study, when butterflies were in flight under field conditions. Study plots In August 2003, we selected 17 forest stands in and around LHNP. The physical conditions of all stands have been described previously by Nakagawa et al. (2006). Of the 17 forest stands, two were located in the primary forest inside LHNP (PR stands), six in isolated primary forest stands outside LHNP (IP stands), three in old fallows where 20–60 years had elapsed since cessation of swidden cultivation (OF stands), three in young fallows where 5–13 years had elapsed since cessation (YF stands), and three in new fallows where 1 year had elapsed since cessation (NF stands). We summarized four fundamental characters of the forest stands: number of species of trees of [10 cm in diameter at breast height, basal area of trees, water potential in the soil, and average canopy openness at ground level (Fig. 2, see Nakagawa et al. 2006 for details of methods for obtaining data of these characters). In addition to these 17 forest stands, we selected three forest stands of ‘‘old’’ fallow that had been abandoned for [20 years after cessation of swidden cultivation (Fig. 1). The 20 stands, which varied in area from 2772 to 4917 m2, were surrounded by crop fields, small-scale extensive rubber plantations, orchards, open land along roadsides, abandoned fields covered by grasses, ferns, lianas and/or shrubs, or other types of forest stands. We then established a study plot along the edge of each forest stand, except for PR stands (Fig. 1). A study plot consisted of two approximately 100 m2 areas just outside the edge of each forest stand. Through a preliminary survey, in which we slowly walked along the outside edge of each forest stand, we selected two sites for study plots where density of flying butterflies was relatively higher than in other places outside the forest edge. In addition, three plots were established along the primary forest in LHNP. Although two were relatively near to the two PR stands, none of the three adjoined the PR study stands (Fig. 1). The sizes of the three plots were similar to the above 18 plots. Because all the study plots were set in open land along the forest edge, there was considered to be little difference in canopy openness or relative illuminance among the plots, except that environmental conditions inside the neighboring forest stands differed among forest types.
Fig. 1 Map of the 21 study plots for butterfly censuses in and around Lambir Hills National Park. Two study forest stands, used only for measurement of forest structure of the large-are primary forests (PR), are also shown on the map. The satellite image is from IKONOS, 17 September 2000 (ÓSIJ2000/Kyoto Univ.). The five forest types are as follows: the large-area primary forest in the national park (PR), isolated primary forest (IP), old (20–60 years old) fallows (OF), young (5–13 years old) fallows (YF), and new (\3 years old) fallows (NF)
Although the three PR plots were far from the PR stands selected for estimation of forest characteristics, the characters of the forest stands that the three PR study plots adjoined were likely to be similar to those for the PR stands, shown in Fig. 2, with the consideration of the continuity of the primary forest. The characters of the neighboring forests of the three additional OF plots were inferred by visual appearance inside forest stands to be more similar to those of the OF stands than those of the other forest types. Field census We censused butterflies in the 21 study plots four times: August 2003, September 2003, January 2005 and June 2006. It took approximately 12 days to complete a census, in which we conducted field surveys twice in each study plot. For a trial of the field survey in each study plot, one researcher (T. Itioka) spent approximately 30 min collecting and observing butterflies, with additional time for handling the collected butterflies. A trial survey started just after the researcher reached the plot. Immediately after finding a butterfly flying in a study plot, the researcher
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started to collect or observe it for species identification. There were few butterflies that escaped from the collection trial and could not be identified by observation (\ 5 % of all found butterflies). Each trial was done during the period from 9:30 to 12:30, only when the weather was fine. Census trials were usually conducted at three to four plots in 1 day, and the order of study plots for trials was randomized. We identified collected butterflies to species level in the laboratory. With observational data, we then recorded data on the presence of species in each plot for each event of the census (consisting of two sampling trials). Finally, we obtained data on the presence or absence of each butterfly species for each plot for each of the four census events. Data analysis To ensure that sampling efforts were sufficient to reliably compare of the cumulative number of observed species among forest types, we drew the sample-based rarefaction curves for each forest type using EstimateS 8.2.0 (Colwell et al. 2004). Each of the repeated censuses per plot was defined as a sample (n = 4 per plot). We then analyzed the difference in the cumulative number of species observed one or more times in each plot among forest types with generalized linear models (GLM). Because we collected data on the absence or presence of each species, we based analyses on the overdispersed Poisson model. Multiple comparisons post hoc tests with Bonferroni correction were performed to test the difference between all pairs of forest types. We then analyzed the effects of distance from the largearea primary forest of LHNP, together with effects of forest
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Water potential (log(-KPa))
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Fig. 2 Four fundamental characters of forest structure examined for the five types of study forest stands: number of species of trees of [10 cm in diameter at breast height (a), water potential [log(-KPa)] in the soil (b), canopy openness (%) at ground level (c), and basal area of the trees (cm2 m-2) (d). See the legend of Fig. 1 for abbreviations of the five forest types
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Number of species
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type, on the cumulative number of observed species. We performed GLM analysis, with the cumulative number of observed species in each plot as a dependent variable, in a log link function model based on overdispersed Poisson distribution, with the forest type as a class explanatory variable and distance as a continuous explanatory variable (see the legend of Fig. 5 for the details of the model). In this analysis, we used the data for only two forest types, IP (isolated primary forest) and OF (old fallow), because the replication of study plots and the variation range in distance were not sufficient for other forest types. GLM analyses were performed using JMPÒ version 10 (SAS Institute Inc. 2012). We analyzed the composition similarity of the observed species in each plot among forest types with the Jaccard coefficient. In addition, to illustrate the similarity among plots, we ordinated the data of butterfly species assemblage by nonmetric multidimensional scaling (NMDS) using R statistical software version 2.15.0 and MASS and VEGAN packages (Oksanen 2010). In the VEGAN package, we used the function ‘‘vegdist’’ for calculating (Bray–Curtis) dissimilarity (Oksanen 2010). Using a matrix of dissimilarities, we performed NMDS with the function ‘‘isoMDS’’ in the MASS package (Oksanen 2010).
Results Species richness In total, 132 butterfly species were recorded from 21 study plots throughout the four censuses per plot (Supplementary Table 1). On average, 9.89 species were recorded per plot
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Effect of distance from the large-area primary forest For both IP and OF plots, the cumulative number of butterfly species significantly decreased with distance from the large-area primary forest of LHNP (Table 1; Fig. 5). Independent of the effect of distance, the difference in forest type (IP vs. OF) had a significant effect on species number (Table 1; Fig. 5), which accords with the result shown in Fig. 3. The interaction effect of distance and forest types was not significant (at P = 0.93, the model that incorporated the interaction effect was omitted from the final analysis), suggesting that there was no significant difference in the effect of distance on the species number between IP and OF plots. Similarity of species composition between forest types The number of cumulative butterfly species that was recorded only in fallows but not in primary forests was remarkably smaller than the number of species recorded both in primary forests and fallows (Fig. 6). The similarity of the composition of observed butterfly species (based on absence/presence data) between any two study plots was notably higher in the comparison within a forest type than in the comparison between any two forest types, except for one YF plot (NMDS, Fig. 7). Moreover, the data plots of study plots of a forest type formed clusters on the graph of NMDS, and the clusters were distributed from the lower right to the upper left on the graph in order
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Fig. 3 The cumulative number of butterfly species observed in each study plot at the open habitat adjoining the edge of various types of forest stands. There was significant difference among forest types (GLM based on overdispersed Poisson distribution model, likelihood ratio v2 = 174.28, P \ 0.0001). Different letters indicate significant differences in a pairwise comparison between any two forest types at P \ 0.05 (Post-hoc test with Bonferroni correction). See the legend of Fig. 1 for abbreviations of the five forest types
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Number of species
per census event. The number of species of butterflies that were unable to be identified was 12 species at most, and most of these were observed at PR or IP. The cumulative number of butterfly species observed one or more times throughout the study period was significantly different among forest types (GLM based on overdispersed Poisson distribution model, likelihood ratio v2 = 174.28, P \ 0.0001), with the number being highest in PR plots, second highest in IP plots, and lowest in NF plots (post hoc test with Bonferroni correction, P \ 0.01, Fig. 3). The rarefaction curves for cumulative species on sampling efforts also showed a similar pattern of difference among forest types, especially for the order of richness (Fig. 4). The average cumulative number of species of PR plots was approximately three times that of OF plots, and approximately twice that of IP plots (Fig. 3). There was no significant difference between OF and YF plots in species number, while the species number of NF was almost half those of both fallows (OF and YF; Fig. 3). The average of cumulative species number at OF was 75.0 % of that at IP, which was significantly different.
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Fig. 4 Sample-based rarefaction curves of the expected number of butterfly species in each of the five forest types. Shaded regions indicate 95 % confidence intervals
of degree of progress in recovery from fragmentation or deforestation (NF to PR).
Discussion The rates of change of all five rarefaction curves for cumulative number of butterfly species decreased as the number of samplings increased (Fig. 4), suggesting that our census efforts reached the necessary levels for statistically robust analyses using the absence or presence data. Moreover, the amplitude in the number of butterfly species targeted in this study seems to support the sufficiency of the census efforts for statistical analyses; the total number of butterfly species that were targeted for the analyses and
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Table 1 Poisson regression on the cumulative number of observed species in each plot, with the forest type as a class explanatory variable and with the distance as a continuous explanatory variable, based on overdispersed Poisson model
3.2325
769.2084
\0.0001
Forest type Distance
0.2946
26.8983
\0.0001
-0.000016
5.0078
0.0252
0.054
* Parameters are the components of the following a log link function model on which the analysis was based: ln (N) = a ? bF ? cD, where N, F and D are variables representing the number of species, the forest type, and the distance (m) from the large-area primary forest, respectively, and where a, b and c are the estimated constant parameters, which are shown in the rows for ‘intercept’, ‘forest type’ and ‘distance’, respectively, in the table. If the forest type is ‘IP’, F is 1, while, if it is ‘OF’, F is -1
Number of species
40 Isolated Primary Forest (IP) 30
0.422
0.188
80
Since the interaction effect of forest type and distance was not significant in the model incorporating the interaction effect, with both discrete and continuous covariates (likelihood ratio v2 = 0.00654, P = 0.9355), the interaction effect was removed from this analysis
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Fig. 6 Degree of sharing species between any two forest types. The height of bars indicates the number of species. In each bar, the white indicates the species number of butterflies observed only in the first forest type (shown on the right side of the graph), the black indicates species number observed only in the second forest type (shown on the front side), and the gray indicates species number observed in both forest types. The number written around the top of each bar indicates the Jaccard index, representing the proportion of shared species in comparisons between the pair of forest types. See the legend of Fig. 1 for abbreviations of the five forest types
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the total number of butterfly species observed in all the target primary forest plots (PR and IP) reached 139 and 113, respectively. These numbers are approximately onethird of 356, which is the number of butterfly species that have been recorded in and around the study areas over more than 10 years of intensive inventory work on butterflies (Itioka et al. 2009). This is one of the few studies that has empirically demonstrated changes in species richness of forest-edgedwelling butterflies (those that fly in areas adjoining forest edges) over more than 50 years of chronosequence during the process of restoration from cessation of swidden cultivation to forest maturation. Our results clearly show the longstanding negative impact of deforestation on species richness of forest-edge-dwelling butterflies, and
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Fig. 5 Relationship between distance from the large-area primary forest and cumulative number of species for isolated primary forests (closed circle) and old fallows (open circle). Each line is drawn based on the parameter estimated through Poisson regression (see Table 1 for the details)
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Fig. 7 Ordination diagram of NMDS scores (along the first two axes, stress value = 0.18) for illustrating the similarities of butterfly species composition among all study plots. See the legend of Fig. 1 for abbreviations of the five forest types
illustrate the recovery process of species richness over the forest restoration process. The cumulative observed species number at each plot, which represents species
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density, demonstrated that the species richness of butterflies gradually increased along vegetational succession from newly growing forest just after cessation of swidden cultivation, through development of secondary forests, to matured forests (Fig. 3). Species richness is suggested to rapidly increase, especially during the first several years of cultivation abandonment (from NF to YF). However, the results also suggest that the pace of recovery would decrease in the following several decades (from YF to OF). Although it remains to be examined how many years are required for fallows to mature into primary forests, the results indicate that the species richness of forest-edgedwelling butterflies is unlikely to recover monotonically after the start of forest restoration. Such stagnation or slowdown in recovery of species richness was not found in assemblages of fruit-feeding nymphalids studied by Veddeler et al. (2005). However, the results of the analyses on the similarity of butterfly species composition among forest types (Figs. 6, 7) suggest that a clear chronosequential change during forest restoration was found in species composition from NF to IP, including the period from YF to OF. Although species richness did not significantly increase from YF to OF, the species composition of OF was more similar to that of IP than that of YF. In this study, we did not perform analyses on the relationship between environmental conditions and butterfly species richness because of insufficient sample size (forest stands). However, of the four types of environmental factors, butterfly species richness seemed to correlate most with the species number of trees [ 10 cm diameter at breast height (Fig. 2). Even if tree species diversity affects forest-edge-dwelling butterflies, the increase of tree species from YF to OF (Fig. 2) would not significantly contribute to recovery of butterfly species richness. It remains to be examined what environmental factors affect the recovery of butterfly species richness during forest restoration. Most studies investigating the impacts of deforestation or anthropogenic forest degradation on butterfly diversity in the Southeast Asian tropics have compared several parameters of species diversity between the target disturbed areas and the neighboring undisturbed primary forests (e.g., Hill et al. 1995; Willott et al. 2000; Lewis 2001; Ghazoul 2002; Cleary et al. 2005, 2009; Hirowatari et al. 2007; reviewed in Koh 2007). These studies faced challenges in comparing parameters representing diversity, because differential micro-environmental conditions among compared habitats were not completely controlled, or were separated from the main factor (whether forests were disturbed or not at larger spatial scales) in most cases. Micro-environmental conditions, such as light, humidity, temperature and food abundance, have been considered to
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strongly affect the flying activities or microhabitat preferences of butterflies (Basset et al. 1998, 2001; Dumbrell and Hill 2005; Hamer et al. 2005; Koh 2007). If micro-environmental conditions differ between disturbed and undisturbed forests, it becomes difficult to distinguish whether the differences in the estimated diversity parameters are due to differences in micro-environmental conditions or differences in species diversity itself. To solve these problems, we controlled the micro-environmental conditions of our ‘‘study plots’’, by setting all plots in open areas at the outer edge of target forest stands. Although we controlled the bias of diversity estimation due to differential environmental conditions, we were unable to incorporate floor-dwelling butterflies, those depending on undisturbed forests as their main habitat, into the analyses. These types of butterflies are known to include many species that are endemic to the local region, and are expected to be more prone to local extinction in the face of forest disturbance (Spitzer et al. 1997; Hill et al. 2001; Cleary and Mooers 2006). Therefore, our estimation of the negative impact of deforestation with swidden cultivation on species richness of the whole butterfly assemblage is likely to be underestimated. Indeed, from our preliminary observations on butterflies in the forest floor at each plot, it appeared that the number of floor-dwelling lycaenid species was clearly higher in primary forests than in fallows (personal observation). Estimating the recovery process of such floor-dwelling butterflies along with recovery of forest vegetation is a challenge for future studies. Interestingly, although the physical conditions in the open habitats studied here were considered to be more similar to those inside the secondary forests, such as fallows, than those in primary forests, the species diversity of forest-edge dwelling butterflies was higher in primary forests (PR and IP) than in the fallows (OF, YF and NF). The analyses of species compositions also show that the number of species shared by primary forests and fallows were notably higher than number of species observed only in old fallows (Fig. 6). Furthermore, the number of species at old fallows and isolated primary forests decreased significantly with distance from the large-area primary forest. These results lead to the hypothesis that most forest-edge-dwelling butterflies observed in fallows may potentially prefer primary forests rather than fallows as their habitats at the larval and/or adult stage. Our findings also show that these butterflies disperse from areas in and around primary forests to fallows after adult eclosion, in accordance with the distance effect in the theory of island biogeography (MacArthur and Wilson 1967), which suggests that the target isolated primary forests (IP) and old fallows (OF) were spatially distributed as if they were
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‘‘islands’’ in the ‘‘ocean’’ of the coverage of lower vegetation, such as paddy and burnt fields, orchards, extensive rubber plantations and much younger fallows. This hypothesis is consistent with the general tendency of most forest-edge-dwelling butterflies to have relatively high flight ability (Ohsaki 1999). If this were true, the recovery of butterfly species richness after land use, such as swidden cultivation, would be much slower in areas where there is no large-area primary forest in the vicinity. At present, this hypothesis remains to be tested, and future studies must determine the factors or ecological processes generating such distance effects with special reference to spatial distributions of host plants and adult’s food resources for the target butterfly species. Based on previous studies, spatial distribution is expected to strongly affect the abundance of butterflies (Cleary et al. 2009), even though the distance effect was not detected in fruit-feeding butterflies (Veddeler et al. 2005). Our results also suggest that forest fragmentation greatly decreases species diversity of forest-edge-dwelling butterflies, even if the neighboring forest stands have no significant damage by logging or other human activities. The degree of difference in the species richness in the comparison between PR and IP seems to be notably greater than between IP and either of the other forest types (Fig. 3), even when taking into account the above-mentioned distance effect on butterfly species richness in IP or other fallows. Our results suggest the validity of our target butterfly assemblage, which consists of butterflies that fly in open areas just around forest edges, as an indicator of the level of forest disturbance. This accords with many studies that suggest the use of butterflies as an indicator of forest degradation (e.g., Cleary 2004; Schulze et al. 2004; Bobo et al. 2006; Fleishman and Murphy 2009; Bonebrake et al. 2010). For future studies that wish to use butterfly assemblages as an indicator of forest disturbance, sampling (census) efforts should be appropriately estimated for obtaining robust statistical power and sufficient sensitivity to small differences in the disturbance level to forest vegetations. Acknowledgments Our study was conducted in accordance with the Memorandum of Understanding signed between the Sarawak Forestry Corporation and the Japan Research Consortium for Tropical Forests in Sarawak in November 2005. We thank Ms L. Chong and Mr H. Kaliang for help with the approval procedure to gain permission to conduct the study. We also thank Dr K. Momose, Dr S. Sakai, and Prof N. Yamamura for their extensive support of our study, Dr T. Matsumoto, Dr H. O. Tanaka, Mr D. Matsumoto and Mr T. Okubo for their assistance work, and Dr M. Yamashita for providing the map information of our plots. This study was financially supported by the Research Institute for Humanity and Nature (project number P2-5 and D-04) and JSPS KAKENHI (23570111 to MY).
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