I 2 1
Thomas Bourguignon, Maurice Leponce and Yves Roisin
’Evolutionary Biology and Ecology, CP 160/12, Université Libre de Bruxelles, Avenue F.D. Roosevelt 50, 1050 Brussels, Belgium. yroisin@ulb.ac.be
^Section of Biological Evaluation, Royal Belgian Institute of Natural Sciences, Brussels, Belgium
Abstract
Termite nests are sessile and established early in the development of the colonies, allowing one to census them and infer the biological processes driving their spatial distribution. A correct interprétation of the nest distribution patterns requires to take into account their âge. However until now these two factors hâve seldom been considered simultaneously. The nests of soldierless humus-feeding termites {Anoplotermes spp.), are generally inconspicuous because subterranean. However, A. banksi builds arboreal nests that can be easily detected. In French Guiana, we conducted a 3-year survey of the dynamics of colonization of a 1-ha plot by the soldierless soil-feeding termite Anoplotermes banksi. The Ripley K-function, detecting aggregation or dispersion at varions spatial scales, was used to investigate the global distribution pattern of A. banksi nests, as well as the distribution of new and established nests. Only one-fourth of the nests recorded the first year were still alive after 3 years, highlighting a rapid turnover of A. banksi nest population. Age classes were differentially affected by mortality, which was higher in young nests than in large, well established nests. Established nests were overdispersed at short scale, whereas young nest had a random or clumped distribution. Young nests tended to be overdispersed from established ones and were more abundant in areas with recently dead nests. Dead nests predominantly occurred where A.
banksi nests were particularly abundant. Altogether, our results suggest that intraspecific
compétition between neighboring colonies is the major force driving A. banksi nest distribution.
Introduction
According to their nesting habits, termites can be distributed among three lifetypes: (i) the single-piece nesters, living inside the substrate they feed on; (ii) the intermediate nesters, living inside their feeding substrate but also foraging outside the nest; and (iii) the separate- piece nesters, living inside a separate nest and foraging away from it to harvest food (Abe 1987). The last group includes conspicuous species which may build huge mounds exceeding human height (see Grassé 1984). For example, African savannas often harbor Macrotermitinae nests, forming islands of biodiversity for a specialized inquiline fauna (Kistner 1979), and ereating soil heterogeneity whieh favours plant species diversity and influences their spatial structure (e.g. Konaté et al. 1999, Traoré et al. 2008, Moe et al. 2009).
When established, the colonies of some species can live for several décades (Nutting 1969). For instance, Hill (1942) mentioned a nest of Nasutitermes triodiae which stayed alive for more than 60 years. However, besides a few records highlighting long-lasting colonies of fungus-growing termites and some xylophagous termitids, it is likely that many species hâve shorter-lived colonies. In the humivorous termite Cubitermes speciosus, the life expectancy of young settled colonies was estimated at 3.5 years, with a rapid colony turnover (Soki et al.
1996). Unfortunately, informations on other humivorous species are scarce and we do not know whether a quick turnover of colonies is the rule or not.
Most social insects nests are sessile, allowing one to census them and to investigate which factors (biotic or abiotic) déterminé their distribution. In ants, individuals from different colonies of the same species generally interact agonistically, resulting in nest overdispersion (e.g. Levings and Traniello 1981, Ryti and Case 1986). Other mechanisms, such as a limited availability of nesting sites, may also constrain the distribution of colonies and produce such a pattern (Foitzik and Heinze 1998). Although nest overdispersion is widespread in many ant species, random or clumped distributions occasionally occur as well. The former case suggests an absence of interaction between colonies which spread over a homogeneous area (e.g. Kenne and Dejean 1999), whereas the latter is typical ofpolydomous species (Debout et al. 2007), or species specialized in a particular microhabitat (Soares and Schoereder 2001). Like ants, termite colonies often interféré agonistically. In Nasutitermes, colonies defend their foraging area during fights which can involve both intra- or interspecific opponents (Thome 1982, Levings and Adams 1984, Thome and Haverty 1991), either leading to neighbouring colony shunning, or to active élimination of the weaker contestant (Levings and Adams 1984, Leponce et al. 1996, 1997). These mechanisms likely lead to nest overdispersion, a pattern observed in grass- and wood-feeding termites (Wood and Lee 1971,
The Anoplotermes group in French Guiana - Article 4 69
Spain et al. 1986, Buschini 1999). Almost nothing is known, however, about interactions among soil-feeding termites. Different species may be segregated from each other by the sélection of different food sources (Bourguignon et al. 2009), but whether or not intraspecific compétition occurs and may lead to nest overdispersion is not known. Other traits such as polycalism (e.g. Levings and Adams 1984, Holt and Easey 1985) could also influence nest distribution.
Investigating nest spatial distribution can bring powerful evidence of compétition, polycalism, nest site limitation or specialization toward heterogeneously distributed resources. However, one has to recognize hierarchical levels and apply the right model to interpret the spatial structure. For example. Macrotermes nest distribution was altematively reported aggregated, random or overdispersed, using nearest-neighbor analysis (Collins 1981, Lepage 1984, Pomeroy 1989, Schuurman and Dangerfield 1997). These variable results may reflect a true pattern or be artefacts caused by (i) not applying edge correction for mounds close to the limits of the studied area, or (ii) pooling ail nests without distinction of âge classes. Taking into account these two points, Korb and Linsenmair (2001) found that large nests were overdispersed whereas small ones were aggregated, probably reflecting, respectively, compétition and microhabitat sélection.
A plethora of statistical methods hâve been developed to investigate point patterns (see Dale et al. 2002; Perry et al. 2002). Nearest-neighbor analysis is the most commonly used method in the literature, due do its simplicity. However, it is outperformed by several alternative methods, such as Ripley’s L and 0-ring statistics (Wiegand and Moloney 2004). These second-order statistics are based on the distribution of distances of pairs of points, taking into account ail inter-point distances (Ripley 1981). They présent the advantage of providing information at ail spatial scales, as far as the extent of the sampling area allows it. This represents a major advance over the nearest-neighbor method, as different processes can operate at different spatial scales (Levin 1992) resulting in mixed patterns (e.g. local aggregation but regular distribution at larger spatial scale). Additionally, Ripley’s L and O- ring statistics can be worked out with bivariate patterns, investigating the influence of one point pattern on the other (Wiegand and Moloney 2004).
Nest temporal dynamics and spatial distribution are intrinsically linked, but hâve rarely been investigated together, although young and old colonies are likely to be influenced by different factors (see Korb and Linsenmair 2001). Here, we investigated the nest distribution and demography of Anoplotermes banksi, which differs from species whose spatial pattern was previously investigated in being a soil-feeder. Soil-feeding termites form
an abundant but understudied component of tropical rainforests (Brauman et al. 2000) with a generally cryptic lifetype, making them difficult to study via direct observation. In this aspeet,
A. banksi is a particularly suitable model because it is among the few species building
conspicuous aboveground nests on trees, which are easy to map. Here, we monitored a lha plot area for 3 years in order to shed light upon small-scale population dynamics. Our aim was twofold:
(1) Démographie studies hâve seldom been carried out on soil-feeding species (Soki et al. 1996). Here, we describe the évolution of A. banksi nests in a démographie perspective through estimation of nest birth, death and replaeement rates. Additionally, we estimated the nest growth rate and the density of A. banksi in a lha plot.
(2) Termite nest distribution might be influenced by several factors among which intraspecific compétition and environmental heterogeneity are the most commonly cited (e.g. Korb and Linsemair 2001). We investigated the nest distribution of A.
banksi, distinguishing âge classes, to test the influence of both factors on this soil-
feeding termite.
Methods Study site
Fieldwork was carried out in a rainforest-covered valley near Petit Saut dam (N 05°04’, W 052°59’) in French Guiana. The site was relatively fiat with a maximum différence in altitude of about 5m. It was situated downhill and received water from surrounding hills during intensive rains, soaking the soil and flooding the area by a shallow layer of water. Beside these sporadic events, the area was free of water exeepted for several small creeks, running through the area with a flow depending on recent rain intensity (Fig. 1). The végétation consisted in palm trees of varions généra, including some rare Euterpe, as well as many trees with pneumatophores. The Petit Saut area expériences a humid tropieal climate with about 3000mm of rainfall per year, mostly distributed during a rainy season occurring between January and June, and with a comparatively drier season from September to November. The mean annual température is 26°C. Petit Saut was almost free of human disturbance till 1994, when the area was partially flooded by the hydroelectric dam (for fiirther details, see Cosson et al. 1999). One transect settled nearby the studied site revealed a spécifie termite fauna eomprising several Anoplotermes species, including three nest-building species; A. banksi
The Anoplotermes group in French Guiana — Article 4 71
Emerson, A. parvus Snyder and A. nr. distans (see Chapter 1). Here we only studied the first species because the other two were far less common.
Fig. 1. Maps of the studied area, depicting the évolution of the nest population of A. banksi between February 2007 and January 2010. Open squares: dead nests; fitll diamonds; established nests; grey triangles: new nests; black circles: nests collected in 2007, not assignment to any category. The two parallel Unes represent the creek.
Nest sampling
The studied site consisted in a 100m x 100m plot, divided into a grid of 400 squares of 5m x 5m (Fig. 1). The grid was set in February 2007 and carefully inspected for Anoplotermes
banksi nests in February 2007, January 2008, February 2009 and January 2010. Each nest was
labeled, positioned on the grid, measured and checked for live termites. If alive, a voucher sample of a few workers was taken into 80° alcohol after scraping off a small piece of the nest
envelope or galleries departing from the nest. Specimens were examined in the lab under binocular microscope to confirm the species status and avoid any confusion with A. parvus and A. nr. distans, and to verify that it was still inhabited by the original builders. Nest measurements included height (herein H), width (W) and thickness (T). Their volume (V) was then estimated assuming that nests hâve an ellipsoidal shape using the formula:
tiHWT “ 6
Demography
As we sampled the nests four times over consecutive years, we could from the second year on recognize three categories of nests: live nests were labeled “established nests” if they were already présent the year before, and “new nests” if they were not; nests that were alive the year before but died were recorded as “dead nests”. We calculated the following démographie parameters: death rate (y), birth rate (P) and natural increase rate of the population (r). These parameters were estimated using the formula:
J i„No
R- il N
P ^ hig and r = p-y
With t being the time elapsed between two records, No the total number of live nests at time 0, N the total number of live nests at time (0 +1) and S the number of established nests at time (0 + t) (see Soki et al. 1996). To allow direct comparison between years, we chose to express t in months. These parameters were estimated for each period between two consecutive sampling years, as well as between the first and the last one.
To test whether death rate, birth rate and total number of nests differed between years, we computed chi-square tests on the total number of dead and new nests and on the total number of nests recorded each year, respectively, under the null hypothesis of no différence between years. To test whether average nest volume differed between years, we carried out a Kruskal-Wallis test using estimated nest volume as data input. We also tested whether the volume of nests in the years 2007, 2008 and 2009 differed between nests that survived or nests that died during the following year (Mann-Whitney tests). Nest growing rate was visually estimated with box plots, using new nests as starting points. Finally, we estimated the total termite number in our lha plot for each year by the following formula, established by
The Anoplotermes group in French Guiana - Article 4 73
Josens and Soki (2010) for A. banksi on the basis of Martius and Ribeiro's (1996) data: for any given nest, log,o N = 4.73 + 0.67 logjo V, with N being the number of individuals and V the nest volume. To make the results clearer, we reported the resuit to a number of individuals per square meter.
Nest distribution
Maps of nest position were generated for each sampling year. Spatial analyses of nest distribution were carried out following the grid-based approach with the freeware Programita (Wiegand and Moloney 2004), which computes Ripley’s K-function and 0-ring statistics. The first statistics use circles of radius r centered on each point of the investigated pattern, numerical implémentation is then based on the number of points and cells inside the delimited area. The Ripley’s K-fiinction can detect aggregation or dispersion up to a given distance r and is therefore suited to study the négative effect of compétition. By contrast, the O-ring statistics uses a ring rather than a circle, including only points at a distance r and excluding the others at shorter distance. This method therefore detects aggregation or dispersion at a certain distance r (Wiegand and Moloney 2004). Because we were interested in short-scale interactions between nests, we used the K-fimction to investigate the global distribution pattern of A. banksi nests, as well as the distribution of new nests and established nests. The analyses were carried out for each year separately. Distribution patterns were emphasized after comparison of the K-function to 5% confidence envelopes, computed following the CSR (complété spatial randomness) null model. The envelopes were calculated from the 50* lowest and highest estimation taken from 999 Monte Carlo simulations of the CSR model. The K-function curve goes below the 5% lowest confidence envelope when nests are overdispersed up to distance r, whereas the curve goes above the 5% highest envelope when the pattern is aggregated. We also studied the interaction between different components of the main pattern, or between the main pattern and a landscape element, using the bivariate K- function, K12. To test if the creek influenced the nest distribution pattern, we used the K12- function with the creek as fixed pattern (because it represents the antécédent condition to nest establishment) and nest distribution as the pattern to randomize, following the CSR model. We used the same procedure to détermine the relationship between the new nests and the already established nests; and between the new nests and the dead nests. In both cases, we kept the latter pattern fixed, because it represented the antécédent condition, and randomized the former. Finally, we investigated how dead nests are distributed in the population using a random labeling model. The question here was: where did nests die within a population of
established nests ? This does not imply interaction between the two processes, but only how the labels are assigned to the points (Wiegand and Moloney 2004). Random labeling thus keeps the joined pattern (pattern 1 + 2) fixed but randomly assigns each case label to one of two patterns following the chosen null model. Here, we implemented two null models noted g2i(r) - g22(r) and g2i(r) - g2,i+2(r). The first model investigated whether type 2 points (dead nests) were positively correlated with other type 2 points, i.e., whether there were more dead nests around dead nests than around live nests, suggesting the influence of a local condition, e.g., a spreading disease; the second model investigated whether type 2 points (dead nests) were mainly located in area with more intense joined pattern, i.e., whether dead nests occurred in areas more densely populated by A. banksi nests. As in the case of the univariate K-function, we carried out ail the bivariate analyses for each year separately and built confidence envelopes with 999 Monte Carlo simulations.
Results Demography
Fig. 2. Survivorship curves of Anoplotermes banksi nests for consecutive sampling years.
Overall, 151 nests of
Anoplotermes banksi were
recorded between February 2007 and January 2010, with an average number of 63 nests per year (Fig. 2). Birth rate was higher than death rate each year, resulting in a positive natural increase of the population (Table 1). However, we did not find any significant différence in the number of nests présent during each sampling event = 5.17, df = 3, p = 0.159). The same was true for dead nests = 1.01, df = 2, p = 0.602) and new nests = 4.56, df = 2, p = 0.102) whose number did not significantly differ between January 2008 and 2010, despite an apparent increase in birth rate. Comparison of the nest volume with Kruskal-Wallis test indicated a marginally significant variation between years (H3,2si = 7.72, p = 0.052). The same
The Anoplotermes group in French Guiana - Article 4 75
Table 1. Démographie parameters (month"') of A. banksi nests: birth rate ((3), death rate (y) and rate of natural increase (r) of the nest population in the lha plot between February 2007 and January 2010. The 35-month values were calculated from the initial and final surveys, without considération for intermediate ones.
Sampling period Number of months Number of nests P Y r
Feb 07 - Jan 08 11 55-57 0.047 0.044 0.003
Jan 08 - Feb 09 13 57-62 0.057 0.052 0.005
Feb 09-Jan 10 11 62-78 0.065 0.045 0.021
Feb 07-Jan 10 35 55-78 0.039 0.049 0.010
analysis carried out with the médian test suggested différence between years = 10.98, df = 3, P = 0.012). Comparison of nest volumes for the years 2007, 2008 and 2009 pointed out that the nests that died during the following year were on average smaller than the ones that survived (2007: U2i,34= 265, p = 0.11; 2008: U2s,29= 204.5, p = 0.001; 2009: U24,s8= 304, p = 0.04). Nest growth rate is depicted in Fig. 3B. New nests which survived underwent an increase in mean volume from 0.43L to 0.76L after 1 year and to 1.39L after 2 years. By contrast, the médian volume was about half as high, rising from 0.20L to 0.37L after 1 year and 0.62L after 2 years. Finally, the total volume of the nests varied between 62.7L/ha and 68.7L/ha and the population density was estimated between 337 and 369 individualsW.
Fig. 3. (A) Volume of Anoplotermes banksi nests for each sampling year; (B) Volume of new nests (2008 + 2009 + 2010 pooled) and of the same nests after one (2008-09 + 2009-10 pooled) or two additional years (2008-09-10). Solid lines represent the médian and dotted lines the mean nest volume. Box plots delimit the first and the third quartiles and the whisker caps the lO"’ and 90"' percentiles.
Nest distribution
Maps of^. banksi nests for each year are represented in Fig. 1. When considered ail together, nests were initially overdispersed the first 2 years, but this trend almost disappeared in 2009 and was only slightly signifïcant between 5 and 10m, whereas in 2010 the distribution was
Spatial scale r (m) Spatial scale r (m) Spatial scale r (m)
Fig. 4. Temporal change in spatial pattern for (A) ail nests; (B) established nests; and (C) new nests. Open circles represent the Ripley K-function. Full circles represent the 95% confidence envelopes computed under the CSR null model (complété spatial randomness). Results below the lower confidence envelope indicate nest overdispersion at the given spatial scale; results above the higher envelope indicate aggregation.
completely random (Fig. 4A). By retaining only the established nests, we found an overdispersed pattern for ail years (Fig. 4B). By contrast, new nests were randomly distributed in 2008 and 2009 and slightly clumped in 2010 (Fig. 4C). The creek flowing through the area represents the most obvious environmental factor likely to influence the nest distribution of A. banksi. However, we did not find any sign of association between the creek and nest pattern for any year (Fig. 5), which indicates that nests are distributed independently from the creek in this area. Other bivariate patterns investigated involved interactions between distinct nest categories. New nest establishment and nest death did not occur randomly in the area. We found a négative association between established nests and new nests for the year 2008 and 2009, whereas the patterns were independent in 2010 (Fig. 6A). When we carried out the same analysis for dead and new nests, we found no interaction in 2008 but a slight positive association the two subséquent years (Fig. 6B). There was no spatial association between dead nests, excepted in 2009 when dead nests were aggregated from 20m to 35m (Fig. 6C). However, this larger scale aggregation is unlikely to reflect direct interactions between nests. Finally, we found that dead nests occurred predominantly in areas with more