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THE JOURNAL OF TROPICAL BIOLOGY AND CONSERVATION BIOTROPICA 41(3): 328–337 2009 10.1111/j.1744-7429.2008.00477.x Seasonality of a Diverse Beetle Assemblage Inhabiting Lowland Tropical Rain Forest in Australia Peter S. Grimbacher1,2,4 and Nigel E. Stork3 1 Department of Resource Management and Geography, Melbourne School of Land and Environment, University of Melbourne, Melbourne, Australia 2 School of Marine and Tropical Biology, James Cook University, PO Box 6811, Cairns 4870, Queensland, Australia 3 Burnley Campus, 500 Yarra Boulevard, Richmond, VIC 3121, Melbourne, Australia ABSTRACT One of the least understood aspects of insect diversity in tropical rain forests is the temporal structuring, or seasonality, of communities. We collected 29,986 beetles of 1473 species over a 4-yr period (45 monthly samples), with the aim to document the temporal dynamics of a trophically diverse beetle assemblage from lowland tropical rain forest at Cape Tribulation, Australia. Malaise and flight interception traps were used to sample adult beetles at five locations at both ground and canopy levels. Beetles were caught throughout the year, but individual species were patchy in their temporal distribution, with the 124 more abundant species on average being present only 56 percent of the time. Climatic variables (precipitation, temperature, and solar radiation) were poorly correlated with adult beetle abundance, possibly because: (1) seasonality of total beetle abundance was slight; (2) the peak activity period (September–November) did not correspond to any climatic maxima or minima; or (3) responses were nonlinear owing to the existence of thresholds or developmental time-lags. Our results do not concur with the majority of tropical insect seasonality studies suggesting a wet season peak of insect activity, perhaps because there is no uniform pattern of insect seasonally for the humid tropics. Herbivores showed low seasonality and individual species’ peaks were less temporally aggregated compared to nonherbivores. Canopy-caught and larger beetles (> 5 mm) showed greater seasonality and peaked later in the year compared to smaller or ground-caught beetles. Thus seasonality of adult beetles varied according to the traits of feeding ecology, body size, and habitat strata. Key words: Coleoptera; feeding guild; phenology; spatiotemporal dynamics; temporal partitioning; time-series; vertical stratification; Wet Tropics. WHY TROPICAL FORESTS ARE SO RICH IN SPECIES, particularly insects, has puzzled many for decades (Godfray et al. 1999). Tropical insect assemblages are renowned for their spatial and temporal complexity and thus remain poorly understood both taxonomically and ecologically (May 1997, Basset et al. 2003; but see Novotný et al. 2002a). In recent years considerable progress has been made to understand ‘the pieces of the diversity jigsaw puzzle’ (Kitching 2006, Stork 2007a) through key studies of tropical insect host-specificity, and beta-diversity (Novotný et al. 2002b, 2007; Dyer et al. 2007). One of the less well-studied areas is the temporal dynamics of insects in tropical forests (Wolda 1988). Such studies documenting intra- and interannual population dynamics of multi-species insect assemblages are important because they provide a basic knowledge baseline of the ecological processes operating within tropical rain forests. They can also inform whether species are genuinely rare or rare because they are only able to be sampled at particular times of the year. This information is particularly important for those monitoring faunal responses to environmental change, such as climate change. Furthermore, understanding the temporal dynamics of insects is important because of the roles insects play in mediating many ecological processes (Miller 1993, Godfray et al. 1999, Wall & Moore 1999). If there is low host specificity (Novotný et al. 2002b, but see Dyer et al. 2007) and spatial turnover of tropical rain forest insects (Novotný et al. 2007), other possible reasons to explain the Received 6 March 2008; revision accepted 25 Augugst 2008. author; e-mail: [email protected] 4 Corresponding 328 concentration of insect biodiversity in the tropics might need to be considered. One such reason is the partitioning of resources through time. Insects with similar ecological traits may coexist by being abundant or active at different times of the day or year, thereby avoiding competition (Wolda & Fisk 1981, Tylianakis et al. 2005), although this idea has been little explored to date. Problems in establishing and maintaining long-term (multiyear) sampling in the tropics and difficulties in sorting large numbers of often taxonomically intractable taxa have meant that there have been few long-term studies into the temporal dynamics (often referred to as seasonality) of multi-species assemblages of tropical insects, with the Central American studies of Wolda (1978, 1988, 1989) being the exception. While short-term studies on a few, concurrently studied species are useful, extrapolating conclusions to other species with diverse life histories is problematic and thus many assumptions about tropical insect seasonality exist (Wolda 1988). In practice, there are few generalizations that can be made about insect seasonality in the tropics and tropical insects can show a wide array of seasonal patterns, even at the same site (Wolda 1988). Species can be aseasonal, occurring throughout the year, can show one or more seasonal peaks, and those peaks can be short and sharp or spaced over a broad period (Wolda 1988). In the highly seasonal temperate forests there are a range of life-history strategies to deal with the large differences in temperature and rainfall and consequent availability of resources during the year. Some adult insects enter a state of reproductive dormancy in the winter months and sometimes during hot dry periods of the summer. Cues that C 2008 The Author(s) C 2008 by The Association for Tropical Biology and Conservation Journal compilation Seasonality of Rain Forest Beetles control the switching from one state to another or from one life stage to the next are thought to include photoperiod, temperature, and moisture (Wolda 1988, Tauber et al. 1998 but see Didham & Springate 2003). Despite the usually low intraannual variation in temperature and photoperiod in the tropics, the climate of most tropical rain forests is distinctly seasonal particularly with respect to precipitation (Leigh 2004). Consequently tropical insect assemblages have shown strong seasonal patterns, more often associated with changes in precipitation rather than temperature (Denlinger 1980, Wolda 1988, Frith & Frith 1990, Hill 1993). A majority of tropical studies covering a wide range of insect groups support the classic view of tropical entomologists that insect activity and diversity peak during the wettest part of the year (Owen & Chanter 1970, Wolda 1978, Denlinger 1980, Smythe 1982, Frith & Frith 1985, Hammond 1990, Hill 1993, Novotný & Basset 1998, DeVries & Walla 2001) rather then the driest (Boinski & Fowler 1989, Basu 1997, Dibog et al. 1998). However, the cues for changes in activity and reproductive behavior can vary even among closely related species (Jones & Rienks 1987), and can be relatively subtle. For example, in the virtually aseasonal African equatorial rain forests of the Democratic Republic of Congo, a change in soil temperature of 0.9◦ C is believed to trigger the onset of reproductive dormancy in the majority of ground beetle species (Carabidae) and hence the driver of seasonal abundance patterns for these beetles (Paarmann 1976). The paucity of long-term, multi-species assemblage studies means that we have been unable to answer such basic questions as: (1) For how long do insect species remain active? (2) When are they most active? (3) Is the seasonality of insects correlated with climatic seasonality? (4) Does the seasonality of insects vary according to ecological traits such as feeding ecology, body size, and habitat strata? Answering such questions is essential if we are to better understand spatiotemporal dynamics of tropical insect communities and predict impacts of climate change. For this reason we sampled adult beetle species assemblages at monthly intervals over a 4-yr period from a lowland rain forest in northeastern Australia, with a view to answering the four questions above. METHODS STUDY SITE.—We conducted our study in tropical lowland rain forest at the Australian Canopy Crane Research Facility (16◦ 17 S, 145◦ 29 E; 40 m asl), 3–4 km south-west of Cape Tribulation Queensland, Australia (Stork 2007b). This site lies within the Daintree subregion of the Wet Tropics World Heritage Area, an area recognized as being particularly species rich (Nix 1991). The rain forest at the site is classified as complex mesophyll vine forest type 1a (Webb & Tracey 1981). There are 657 individual trees (> 10 cm dbh) comprising 70 species in 32 families in the almost 1-ha area below the canopy crane (Laidlaw et al. 2007). The site is flanked to the west by coastal ranges rising to > 1300 m and by the Coral Sea to the east. Extensive Quaternary alluvia underlie the area. 329 We used three different sources to present climate data for the study site. Precipitation data were recorded from the Cape Tribulation weather station situated < 1 km from the site, for the duration of the study (March 2000–February 2004). Temperature was recorded from equipment at the crane site. Solar radiation data were sourced from satellite imagery (cloud reflectance) processed by the Bureau of Meteorology from the Geostationary Meteorological Satellite GMS-5. For these data there were some missing data points spanning all of 2002 and some months in 2001 and 2003. We dealt with this missing data by modeling the day-to-day relationship of solar radiation between Cairns and Cape Tribulation for the time period 1990–2006 where data existed (y = 0.817x + 3.592; R2 = 0.71, P < 0.0001, N = 3295). The missing values for Cape Tribulation were then predicted using the Cairns data for the time period in question and the derived model. The region is also subject to tropical storms and cyclones, the most recent to directly affect the area being Cyclone Rona in 1999, a Category 3 Cyclone with winds up to 190 kph that affected the study site prior to the commencement of this study (Stork 2007b). Damage to the structure of the forest included the felling of trees and branches and a reduction in canopy height and cover. However, recovery was very rapid and canopy closure was re-attained after only a few years. BEETLE SAMPLING PROTOCOL.—Beetles were chosen as suitable insect group to study because they are the most species-rich insect order (Nielsen & Mound 2000) and because species from different families encompass a wide range of feeding, body size, and lifehistory strategies (Lawrence & Britton 1991). Adult beetle flight activity was measured using a combined Malaise and Flight Intercept Trap (FIT; see Stork & Grimbacher 2006). This style of trap is very efficient at capturing winged beetles but is poor at sampling flightless ones that are unlikely to crawl into collecting containers. Malaise–flight interception traps measure the flight activity of adult beetles. Therefore data obtained from such samples are unable to inform us of the seasonality of nonactive adults or immature stages. By quantifying flight activity, we are measuring adult beetles engaged in foraging for food and microhabitat resources, searching for mates, avoiding predation and potentially dispersing or migrating (Johnson 1966). Even though we cannot determine the exact reason why beetles are flying, we believe that this type of data validly describes the seasonal dynamics beetle assemblages because most adult beetles are fairly long-lived. Unlike many fragile insects, adult beetles are heavily sclerotized with robust mouthparts and well-developed digestive systems that predispose them for extended longevity (Carey 2001). This longevity can range from several weeks through to many years (Carey 2001) and means that adult beetles must actively forage (including flying) to find food and resources fairly regularly throughout their adult life span. Thus, we believe that sampling flying adult beetles measures more than just mate searching or migratory behavior. We also acknowledge that measuring fluctuations in activity may not always reflect real variations in abundance. For example, this may occur when insects try to compensate for adverse environmental conditions through extra foraging activity. 330 Grimbacher and Stork Five locations, each 40–60 m apart were selected for sampling, within or near the circumference of the Australian Canopy Crane. At each location one combined Malaise–FIT trap was located on the ground and another was suspended in the canopy. Canopy traps were hoisted 15–20 m up into the treecrowns on ropes and pulleys. Equal sampling effort went into the canopy and the ground because each stratum contains distinct species assemblages (Stork & Grimbacher 2006). Traps were run for 2 wk/mo from March 2000 to February 2004 as near as could be arranged given the remoteness of the site and problems with inclement weather. Samples were missed from July and October 2001, March 2002 and May 2003, while two samples were obtained early and late in August 2000. This sampling strategy generated five replicate Canopy FIT, Canopy Malaise, Ground FIT, and Ground Malaise samples, during all 45 sampling periods. To simplify analyses and maximize statistical power, samples from different trapping methods (Malaise and FIT), and replicate traps at the five sites were pooled. All beetles were mounted, labeled, and sorted first to family or subfamily and then to morphospecies. Both authors were involved in sorting all specimens to morphospecies to provide two taxonomic opinions. We used morphospecies as surrogates for species because there were so many species, many of which are undescribed (Stork et al. 2008). STATISTICAL ANALYSIS.—We tested if the temporal distribution of beetles was clumped or random over the 4-yr sampling period with the Runs test. This nonparametric test determines whether the sequence of values above and below the median is random (no seasonality exists) or is aggregated (seasonality exists; SPSS v. 13.0, 2004, Chicago, Illinois). For this analysis, the four missing values in the 4-yr time series were substituted with the means of adjacent values. Beetle abundance was used because it was thought to best represent beetle activity. Note that total beetle abundance and total species richness were strongly correlated over the 45 sampling periods (R2 = 0.74, P < 0.0001). The Runs test was performed on total beetle abundance and the abundance of beetles grouped by feeding ecology, body size, or whether they were caught from the canopy or the ground. Feeding guilds were: predators, herbivores, xylophages, fungivores and saprophages. Body size categories were < 2 mm, 2–5 mm, and > 5 mm. Feeding guild and body size data were sourced from Grimbacher and Stork (2007). The existence of temporal autocorrelation in the number of beetle species and individuals caught was analyzed with the autocorrelation function described by the following equation: n−k rk = (x i − x̄ )(x i+k − x̄ ) i=1 n , (x i − x̄ ) 2 i=1 where x̄ is the average of the n observations. This procedure tests the strength of the correlation of the data against itself for in- creasing time-lags. The four missing values in the 4-yr time series were substituted with the means of adjacent values. We explored the relationship between climate and beetle abundance with linear regression. We tested total beetle abundance and the abundance of beetles grouped by feeding ecology, body size, or whether they were caught from the canopy or the ground against three climatic variables; total monthly precipitation, mean daily maximum temperature, and mean daily solar radiation. Mean daily maximum temperature and mean daily minimum temperature were strongly correlated (R2 = 0.67, P < 0.0001, N = 48) so we used the former. Because these time-series data were both positively and negatively temporally autocorrelated, depending on the monthly time lag (see Results), before conducting regressions, the data were seasonally adjusted by removing periodicity operating at a 12-mo cycle. In this procedure the four missing values in the 4-yr time series were substituted with the means of adjacent values and then the ‘seasonal decomposition’ function in SPSS was used to model periodicity. The residuals from this analysis (excluding substituted missing values) were used in subsequent regression analyses. Descriptive statistics were performed on the more abundant species with ≥ 45 individuals. This cutoff was chosen as there were 45 sampling periods. Circular statistics (Batschelet 1981) were used to quantify the period of greatest activity for individual species. For the more abundant species, the peak within-year abundance period was expressed as an algebraic vector: φ, whereby φ = arctan (y/x ) if x > 0 or φ = 180◦ + arctan (y/x ) if x < 0; and x= n̄ i cos φ i , y = n̄ i sin φi , n̄ i is the mean monthly abundance (across the 4 yr) for month i and φ is the midpoint of month i expressed as an angle (0–360◦ ). For these calculations, monthly values were based on monthly means from the 4-yr sampling period to account for the slightly unequal monthly sampling across years (see above). Data were presented for all species analyzed and grouped according to feeding ecology and body size. To examine possible seasonal variation among species sampled from the canopy or the ground, additional analyses were conducted for species sampled separately from each stratum. In this last analysis, the same species could appear in both groups provided there were ≥ 45 individuals encountered separately from each stratum. We also calculated the mean period of activity (mean vector and 95% CIs of total beetle abundance) and compared beetles grouped by the ecological traits listed above. To quantify how long species were active within the study period, we tallied the number of times species were detected among the 45 temporal samples (for species with ≥ 45 individuals). Counts were performed for the total number of samples, and for the number of consecutive samples where a species was present. Seasonality of Rain Forest Beetles 331 FIGURE 1. Total monthly precipitation (bars), mean daily minimum and maximum temperature (solid lines) and mean daily solar radiation (dashed line) for Cape Tribulation for the study period March 2000–February 2004. RESULTS The climate of the site is distinctly seasonal (Fig. 1). Precipitation data for the duration of the study (March 2000–February 2004), showed that there is usually some rain throughout the year (Fig. 1). However, most rain occurs in the ‘wet season’ of November–April, although the onset, duration, and cessation of the wet season can vary by several months. The 50-yr average (to 1995) annual precipitation at Cape Tribulation is 3926 mm (Hopkins et al. 1996). Mean daily temperature records show distinct seasonality, with the mean daily temperature fluctuating by as much as 5◦ C from the warmest to the coldest months (Fig. 1). Mean daily solar radiation data for Cape Tribulation also show considerable within-year variation in solar radiation (Fig. 1). All three climatic variables (solar radiation, temperature, and precipitation) were temporally intercorrelated, with FIGURE 2. the yearly peak in precipitation preceded by a peak in temperature, which was itself preceded by an earlier peak in solar radiation (Fig. 1). Linear regressions testing the intercorrelation of these three variables at time lags of up to 5 mo showed that the strongest correlations between mean daily solar radiation and mean daily maximum temperature was at a 1-mo time lag (R2 = 0.65, P < 0.0001, N = 48) and for mean daily solar radiation and total monthly precipitation it was 3 mo (R2 = 0.36, P < 0.0001, N = 48). The strongest correlation between mean daily maximum temperature and total monthly precipitation was at a 1-mo time lag (R2 = 0.28, P < 0.0001, N = 48). A total of 29,986 beetles sorted to 1473 morphospecies and 77 families were collected over the 4-yr period. Beetles were caught throughout this time and although they did show peaks and troughs in activity each year (Fig. 2), the runs test on total beetle abundance Total number of individuals (bars) and species (stars/ line) caught across the 45 sampling periods (March 2000–February 2004) using 10 Malaise/FIT traps at Cape Tribulation, Australia. 332 Grimbacher and Stork TABLE 1. Abundance of beetle groups and results of the runs test (P-values), testing whether the abundance of beetles captured from 45 sampling periods during March 2000-February 2004 using10 FIT/ Malaise traps at Cape Tribulation, Australia was temporally aggregated. Pearson’s correlation coefficients, (r-values) are also presented for comparisons among climatic variables and beetle abundance groups over the same time period. For the Runs test, values in bold are significant at P < 0.05. Note that correlations have not been adjusted for multiple comparisons or temporal autocorrelation. Beetle grouping Total abundancea Runs test (P-value) Total monthly precipitation (r-value) Mean daily maximum temperature (r-value) Mean daily solar radiation (r-value) −0.26 −0.31∗ −0.37∗ 0.27 0.09 0.12 0.33∗ 0.40∗∗ 0.45∗∗ −0.19 −0.20 0.06 −0.47∗∗ Saprophage Fungivore Predator 1493 5724 3002 0.0627 0.0003 < 0.0001 Herbivore Xylophage 7445 6099 0.1892 0.0292 < 2 mm 2–5 mm > 5 mm 10007 17045 2924 0.0022 0.0131 < 0.0001 −0.35∗ −0.34∗ −0.09 −0.03 −0.03 0.39∗∗ 0.36∗ 0.36∗ 0.50∗∗∗ Canopy Ground All beetles 14473 15513 29986 0.0003 0.1892 0.1892 −0.27 −0.38∗ −0.37∗ 0.20 −0.19 −0.00 0.55∗∗∗ 0.21 0.42∗∗ 0.27 −0.12 a Note that groups do not necessarily add up because not all beetle families could be allocated into feeding guilds and the body length of some species could not be measured (see Grimbacher & Stork 2007). ∗ P < 0.05, ∗∗ P < 0.01, ∗∗∗ P < 0.001. suggested no significant temporal aggregation (Table 1). However, when the assemblage was divided into groups corresponding to feeding ecology, body size, and strata caught, most groupings showed significant seasonality (Table 1). The exceptions were the herbivores and ground-caught beetles, while the saprophages were marginally significant. The autocorrelation function analysis showed temporal autocorrelation within the data operating at various timescales. Neighboring temporal samples (1–2 mo) were positively autocorrelated, samples 5–7 mo apart were negatively autocorrelated, while samples spaced 11–12 mo apart were positively autocorrelated (Fig. S1). None of the correlations between beetle abundance residuals and total monthly precipitation, mean daily maximum monthly temperature, and mean daily monthly solar radiation were significant (results not presented). We suspected that this was because the process of accounting for temporal autocorrelation may have been overly conservative. Therefore we repeated the tests, correlating beetle abundance directly with the three climatic variables (Table 1) without accounting for temporal autocorrelation. By conducting these subsequent analyses we acknowledge the risk of making type I statistical errors. However, we believe that this risk should not prohibit exploring the existence of potential patterns provided that the results are interpreted with caution (see Roback & Askins 2005). From these subsequent analyses we found that there was a fairly weak negative relationship between total beetle abundance and total monthly precipitation (Table 1; Fig. 3A), no significant relationship with mean daily maximum temperature (Table 1; Fig. 3B), and a weak positive correlation with mean daily solar radiation (Table 1; Fig. 3C). For the most part individual beetle groups followed these same trends however, there were some subtle but interesting differences with the xylophages and larger beetles (> 5 mm) deviating the most from the general trends (Table 1). There were 124 species (with ≥ 45 individuals) on which we could conduct further analyses. These species account for 21,963 individuals (73% of the total abundance and 8% of the species). Analysis of species’ peak abundance period showed that the within-year distribution was heavily skewed, with most species (60%) peaking during September–November (Fig. 4A). By contrast, only 5 percent of species peaked during February–April. Like the correlations with climate, there were some intriguing differences among the different beetle groups. Among feeding guilds, 84 percent of predator species peaked in abundance during October and November, whereas the distribution for herbivore species was more even, with species peaking throughout the year (Fig. 4B). These results corroborate the: (1) tests for seasonality (Table 1), which showed that the abundance of predators was significantly seasonal while that of herbivores was not; and (2) larger confidence intervals for the herbivores, relative to other feeding guilds (Fig. 5). Xylophages peaked several months earlier than beetles with different feeding ecologies (Fig. 5) probably because there was a group of xylophage species that peaked in July. Larger beetle species (> 5 mm) only peaked in abundance during October–January, whereas smaller species peaked throughout the year (Fig. 4C). The mean period of activity for larger beetles (> 5mm) was later than for smaller beetles (Fig. 5). More canopy caught beetle species peaked in abundance during November, whereas the peak in ground-caught beetle species was during October (Fig. 4D). The overall mean period of activity for canopy-caught beetles was significantly later than for the ground-caught beetles (Fig. 5). On average, the most abundant species were present in 25 of the 45 samples (median = 25, SD = 7.7), but only ten consecutive samples (median = 7.5, SD = 7.8; Fig. S2). Seasonality of Rain Forest Beetles FIGURE 3. 333 Plots of the total number of beetle individuals caught against: (A) total monthly precipitation; (B) mean daily maximum temperature; and (C) mean daily solar radiation. DISCUSSION The results from this study concur with those of other rain forest insect studies that observed year-round insect activity (Lowman 1982, Smythe 1982, Wolda 1988, Hammond 1990, Novotný & Basset 1998, DeVries & Walla 2001, Novotný et al. 2002a) although studies conducted in other Queensland rain forests with more seasonal climates (Jones & Rienks 1987, Frith & Frith 1990, Basset 1991, Hill 1993, Seymour & Jones 2000) have shown stronger within-year differences in abun- FIGURE 4. Species’ distribution (for species with ≥ 45 individuals) for the peak monthly activity calculated as an algebraic vector for: (A) all species (N = 124); (B) species grouped by feeding guild (N = 99); (C) species grouped by body size (N = 124); and (D) species caught from the canopy and the ground (N = 82). Data are based on 4-yr monthly means. In (D) circular statistics were performed on species with ≥ 45 individuals separately for each strata. dance and species richness than those observed in this study. Tests for seasonality of total beetle abundance suggested no significant seasonality existed despite strong climatic seasonality. However, repeated peaks in adult beetle activity were evident in each of the 4 yr when this study was conducted, a result that was corroborated by the peak activity analyses and the autocorrelation 334 Grimbacher and Stork FIGURE 5. Period of mean activity (and 95% CIs) for beetles grouped by feeding ecology, body size, and whether caught from the canopy or the ground. Note that comparisons are only valid within these groups (color coded). function analysis. Furthermore, individual adult beetle species were not evenly distributed through time and were present for only 56 percent of the duration of the study, with only 22 percent of that time being consecutive presence. This contrasts with studies conducted in climatically less seasonal rain forests that show that Lepidoptera (Novotný et al. 2002a, Hamer et al. 2005) are active for longer periods of the year. Our result has important implications for those targeting particular species, such as indicator species for environmental monitoring, as it suggests that there is a high chance of missing target species, with one sampling period, even if the target species are relatively common and several traps are in concurrent use. Surprisingly, climate was not strongly correlated with beetle abundance patterns, in spite of clear seasonality among climatic variables and a relaxation of statistical rigor among correlative tests. There are several possible reasons to explain this result, the first of which might be attributed to the low seasonality for the whole beetle assemblage. Another reason may be because more species were most active on, and either side of the month of October, and this period does not correspond to the maxima or minima of any of the climatic variables (see Fig. 1). We did not conduct any correlations among beetle abundance and climatic variables with time lags because interpreting such tests would have been confounded by the intercorrelation of the climatic variables at times lags of 1–3 mo. If the adult beetles are responding directly to climate, our results suggest that it is not in a simple linear way and it may not be due to any one particular variable. Insects are most likely to display nonlinear or threshold-type responses (Tauber & Tauber 1976, Chown & Gaston 1999) and multiple climatic cues may operate in synergies. Both climatic and chemical cues have been suggested as possible factors causing insect seasonality in the tropics (Wolda 1988, 1989; Tauber et al. 1998; Didham & Springate 2003), and this remains a controversial topic. Didham and Springate (2003) suggest that many species may respond to biotic triggers such as leaf flush and chemical cues associated with seasonal changes in plants, which in themselves are a response to more direct climatic changes. Several studies have shown that insect abundance peaks with leaf flush and flowering (Lowman 1982, Basset 1991, Kato et al. 1995, Intachat et al. 2001, Richards & Coley 2007). There is some circumstantial evidence that the peak period of flowering in the northern lowlands of the Wet Tropics (an area that includes our study site; Boulter et al. 2006) corresponds with the peak beetle activity period in our study. The peak period of flowering and fruiting of Normanbya normanbyi, a common palm at our study site, is also at this time of year (Inkrot et al. 2007). Thus the beetle activity peaks in our study may be caused by the seasonality of biological resources although the nature of our data precludes us from drawing any such conclusions. Consideration of the whole life cycle of insects, including the larval stages may be necessary to explain the insect seasonality patterns observed among the adults. Each of the beetle life-history stages of egg, larvae, pupae, and adult need a minimum time to complete development before proceeding to the next stage. Once development proceeds to the next life-history stage it cannot be reversed. For example, if a certain climatic (or chemical) threshold is reached that triggers pupal emergence, the adults have to make do with the environmental conditions even if they subsequently become suboptimal (Tauber & Tauber 1976). This feature of lifehistory behavior may also explain the observed poor correlations with climatic parameters. The environmental pressures on the larval life-history stages could also be very important in explaining the seasonality patterns of the adults. For example resource constraints or the abundance of predators may control larval abundance (Godfray & Hassell 1987, Richards & Coley 2007), which would obviously limit the number of insects emerging as adults. We speculate that the larvae of the beetle species in this study may show quite different seasonal patterns to those observed by the adults (but see Basset 1991). Larvae may display their abundance peak after the adults have mated and the females laid eggs. For most species at Cape Tribulation this could be after the October Seasonality of Rain Forest Beetles peak in adult activity, during the wet season. Testing this hypothesis will prove difficult given that a large proportion of the adult beetle species in this assemblage are undescribed (Stork et al. 2008) and that the larvae are even less well known. Interestingly our results do not concur with the majority of tropical insect seasonality studies suggesting that the wet season is the peak time of activity and diversity of adult insects (Owen & Chanter 1970, Wolda 1978, Denlinger 1980, Smythe 1982, Frith & Frith 1985, Hammond 1990, Hill 1993, Novotný & Basset 1998, DeVries & Walla 2001), although a small number of other studies have observed reduced insect numbers in the wet season (Boinski & Fowler 1989, Basu 1997, Dibog et al. 1998, Hill et al. 2003) or a prewet season peak similar to our study (Buskirk & Buskirk 1976). This may be because the Cape Tribulation site has a very high rainfall compared to other parts of the Australian Wet Tropics and to many other tropical rain forest research sites around the world, which typically receive 2500–3000 mm rainfall a year, although it is comparable to La Selva research station in Costa Rica, el Choco and the Amazon basin of Ecuador (Leigh 2004). Interestingly Janzen (1973) and Richards and Windsor (2007) observed spatiotemporal variation in the abundance of rain forest insects, with the more moist (or shaded) sites having a peak in abundance during the dry season, while the drier sites had peaks during the wet season. Thus it is quite plausible that no uniform pattern of insect seasonally exists for the humid tropics (Wolda 1988) owing to the large spatial variability in precipitation regimes (Sombroek 2001, Leigh 2004). A further argument against the existence of a uniform pattern is that insects with varying ecologies may not show the same seasonal patterns (see below). Our results showed some subtle, yet important, differences in the seasonal patterns of adult beetles grouped according to feeding ecology, body size, and habitat strata that were not apparent when the assemblage was only examined as a whole. This is a good example of how analyses performed at high taxonomic levels can sometimes mask contrasting responses within ecological groups or lower taxonomic levels (see Grimbacher et al. 2008). Interestingly seasonality differences among ecological groups existed in spite of strong temporal correlation within and between all of these groups (results not presented). Unlike the other feeding guilds, herbivores did not show significant seasonality and there was a greater spread of herbivore species activity peaks throughout the year. Although this cannot be interpreted as definitive evidence of temporal partitioning, we suggest that it warrants further investigation. Our results contrast with those of Basset (1991) who found that herbivorous canopy arthropods from Australian subtropical rain forest were much more seasonal than nonherbivores. In contrast, the temporal distribution of nonherbivorous species in the present study was much more aggregated, which are not supportive of the existence of temporal partitioning (at least at the temporal scale investigated). Thus with the exception of the herbivores, these results suggest that fairly strong environmental pressures (biotic or abiotic) are structuring the seasonality of this beetle assemblage. Among body size groups, larger beetles were more seasonal and their activity peaks were later than those of smaller beetles. These differences may be caused by different adult emergence triggers. For 335 example, larger beetles such as scarabs remain as pupae until the first significant rainfall event at the start of the wet season softens the pupal case and surrounding soil, allowing the adult to emerge (Lawrence & Britton 1991). Our results corroborate with Smythe (1982), who found that larger insects showed more pronounced seasonality than smaller insects. In our study, beetles caught from the canopy were more seasonal than ground-caught beetles, a result that concurs with light trap caught insects from the Neotropics (Smythe 1982, Wolda & Broadhead 1985). Canopy-caught beetles also peaked later in the year than ground-caught beetles. By contrast ground-caught beetles did not show significant seasonality. We speculate that this might be because the microclimate near the ground is buffered by the canopy and hence is less seasonal (Walsh 1996). The similarity in seasonal patterns between larger and canopy-caught beetles is likely to be due to the significant relationship between these two traits as canopy-caught beetles also tend to be larger (Grimbacher & Stork 2007). In summary, we are unable to definitively attribute our beetle seasonal patterns to any particular cause given that our data simply represent the activity of adult beetles. Our results suggest that environmental manipulations to test the influence of abiotic factors such as precipitation, solar radiation, and day length are unlikely to be of use in future in situ experiments because of the likely existence of threshold responses to climate and life-history time-lags. In contrast, better progress may result from bottom-up approaches with single species or guilds of species looking at complete life cycles rather than just the adult life stages. Our results show that important differences in seasonality exist among adult beetles depending on their feeding ecology, body size, and whether they live in the canopy or near the ground. ACKNOWLEDGMENTS We thank the Australian Canopy Crane for use of the crane to access the canopy and M. Cermak, B. Howlett, R. Rader, J. Hill, K. Goodall, and Green Corps volunteers for help in servicing traps and sorting and mounting of the beetles. Comments from W. Edwards, R. Didham, T. Schowalter, M. Lowman, and several anonymous referees improved earlier versions of this manuscript. This project was supported by funding from the Rainforest Cooperative Research Centre and from the Marine and Tropical Science Research Facility. We also acknowledge the support of the John T Reid Charitable Trusts, the Vincent Fairfax Family Foundation, and an anonymous donor for generous funding for the operation of the Australian Canopy Crane. SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article: FIGURE S1. Plots of the autocorrelation function for the number of beetle species and individuals captured during March 2000– February 2004. 336 Grimbacher and Stork FIGURE S2. Counts of the number of samples when the 124 most abundant beetle species were present. Please note: Wiley-Blackwell is not responsible for the content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article. LITERATURE CITED BASSET, Y. 1991. The seasonality of arboreal arthropods foraging within an Australian rainforest tree. Ecol. Entomol. 16: 265–278. BASSET, Y., V. NOVOTNY, S. MILLER, AND R. L. KITCHING. 2003. Arthropods of tropical forests: Spatio-temporal dynamics and resource use in the canopy. Cambridge University Press, Cambridge, UK. BASU, P. 1997. 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