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Land use effects on termite assemblages in Kenya
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Abstract
Termites perform key ecological functions and they also cause crop damage. Land use change resulting from agricultural intensification can result in changes in termite species diversity and abundance. Termite species occurring in natural vegetation, maize monocrop and maize-beans intercrop macrohabitats were investigated in Embu and Machakos Counties, Kenya. Influence of soil properties and seasons was also evaluated. Across the two Counties, seven termite species were recorded with Machakos County having the highest number. Additive diversity partitioning of species richness and Simpson diversity showed that, α component contributed to 98.3% and 99.1% of the total diversity, respectively. Population densities of three termite species significantly varied between land use types in Machakos County but there were no differences in termite species abundance in Embu County. In addition, there were no significant differences in species richness between macrohabitats within each County. In Embu, season significantly influenced the abundance of Macrotermes subhyalinus, M. herus, and Coptotermes formosanus which occurred in greater numbers during the wet season. There was a significant influence of land use on Trinervitermes gratiosus and C. formosanus in Machakos with both species occurring in higher numbers in natural vegetation. Trinervitermes gratiosus was negatively associated with Mn and positively correlated to pH and sand. Macrotermes subhyalinus and M. herus showed a positive association with P and silt while C. formosanus was positively correlated to Ca and Mg. These findings provide an insight into the effects of land use change from natural vegetation to maize agro-ecosystems on termite diversity. It also provides a baseline for further studies on termite diversity in Kenya and their ecological significance.
Abstract
Diversity partitioning; Ecosystem engineers; Maize; Tropical agro-ecosystems.
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Distance-based tests for homogeneity of multivariate dispersions.
Marti J. Anderson (2006)
The traditional likelihood-based test for differences in multivariate dispersions is known to be sensitive to nonnormality. It is also impossible to use when the number of variables exceeds the number of observations. Many biological and ecological data sets have many variables, are highly skewed, and are zero-inflated. The traditional test and even some more robust alternatives are also unreasonable in many contexts where measures of dispersion based on a non-Euclidean dissimilarity would be more appropriate. Distance-based tests of homogeneity of multivariate dispersions, which can be based on any dissimilarity measure of choice, are proposed here. They rely on the rotational invariance of either the multivariate centroid or the spatial median to obtain measures of spread using principal coordinate axes. The tests are straightforward multivariate extensions of Levene's test, with P-values obtained either using the traditional F-distribution or using permutation of either least-squares or LAD residuals. Examples illustrate the utility of the approach, including the analysis of stabilizing selection in sparrows, biodiversity of New Zealand fish assemblages, and the response of Indonesian reef corals to an El Niño. Monte Carlo simulations from the real data sets show that the distance-based tests are robust and powerful for relevant alternative hypotheses of real differences in spread.
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Influence of termites on ecosystem functioning. Ecosystem services provided by termites
Pascal Jouquet, Saran Traoré, Chutinan Choosai … (2011)
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Spatial Pattern Enhances Ecosystem Functioning in an African Savanna
Robert M Pringle, Daniel Doak, Alison Brody … (2010)
Introduction A succession of spatially explicit ecological models in the early 1990s indicated that large-scale regular spatial patterns could arise within homogeneous landscapes from local biotic interactions alone [1]–[3], with potentially profound implications for the maintenance of biodiversity and ecological stability [4],[5]. At first, large-scale ordered patterns were harder to find in natural systems than in systems of equations: the title of a 1997 review questioned whether ecological self-organization was “robust reality” or merely a theoretical set of “pretty patterns” [6]. Over the past decade, however, multiple studies have shown that regular patterns are both common and persistent across a range of ecosystems [7]–[10]. But the crucial questions of whether and how these patterns influence ecosystem functioning remain unanswered [11]. Here, we show that the even spacing of subterranean termite mounds in an apparently homogeneous African savanna provides a template for parallel spatial patterning in tree-dwelling animal communities. We further show that the uniformity of this pattern at small spatial scales elevates the productivity of the entire landscape, providing support for models linking spatial pattern with ecosystem functioning [12]–[15]. Our study site in central Kenya (0°20′ N, 36°53′ E) is a wooded grassland on level vertisol soils. The high clay concentration (40%–60%) of these soils reduces water infiltration and causes shrink-swell dynamics that can shear plant roots [16]. In this habitat, which is widespread in East Africa, a single Acacia species (A. drepanolobium, an “ant plant”) constitutes >97% of the canopy over a continuous understory dominated by five perennial bunchgrasses. Thus, the area appears strikingly homogeneous for a tropical terrestrial ecosystem (Figure S1). In addition to symbiotic ants (3 Crematogaster spp., 1 Tetraponera sp.), A. drepanolobium canopies are inhabited by non-predatory insects, predatory insects and spiders, and insect-eating dwarf geckos (Lygodactylus keniensis). Lygodactylus keniensis is diurnal and exclusively arboreal, and males are territorial; along with the arthropod arboreal predators in the system, it preys almost exclusively on tree-feeding insects [17], excepting workers of the Acacia-ant species [18]. In this ecosystem, fungus-cultivating termites (Macrotermitinae: Odontotermes) nest within low, subterranean mounds (10–20 m diameter, 99.9th percentile of the means obtained from the 1,000 randomly generated landscapes (Figure 4). Because the mean values of all variables were strongly negatively correlated with mean nearest-mound distance (Figure S4), the uniform spacing of mounds in the real landscape—which minimizes the mean distance from any arbitrarily chosen point in the landscape to the nearest mound—maximizes mean values of the response variables. 10.1371/journal.pbio.1000377.g004 Figure 4 Frequency distributions of mean landscape values in 1,000 simulated landscapes of randomly placed mounds for (A) total-arthropod biomass, (B) predatory-arthropod biomass, (C) geckos, and (D) spider fecundity. Vertical bars show the mean landscape value for each variable obtained using the evenly spaced distribution of termite mounds in the mapped 0.36 km2 area of the landscape (Figure 1A). The best-fitting models used in these analyses are presented in Tables S1–S3. Results for total- and predatory-arthropod abundance are not shown but parallel those for biomass. This analysis assumes that multiple mounds would not have additive effects on the density or productivity of trees and tree fauna, which might lead to greater production under clumped scenarios than our models (which were based only on nearest-mound distance) predict. We tested this assumption. When we collected data on gecko abundances in 2009 to test the predictive power of our best-fitting ordinal regression model, we recorded the locations of the two mounds closest to each tree (hereafter, nearest and second-nearest). Adding second-nearest mound distance as a predictor to our best model did not improve the model whatsoever (−2×log-likelihood = 666.265641 for both models). Because nearest and second-nearest mound distances were very weakly correlated (r = −0.08), this result is not biased by collinearity of the predictors. We therefore conclude that the “mound effect” on production is adequately characterized by distance to the single nearest mound. Our simulation results also assume that trees are equally likely to occur anywhere in the landscape, so gradients in tree density might complicate our conclusions. In fact, the response of tree density to mound proximity is weak and inconsistent, and a separate set of landscape simulations in which we accounted for these effects (see Materials and Methods) produced qualitatively identical results (Figure S5). Collectively, our data show (a) that a regularly patterned array of termite mounds induces parallel patterning in the abundance and reproductive output of tree-dwelling fauna, (b) that these patterns arise via both consumptive (i.e., trophic) and non-consumptive (i.e., engineering) indirect interactions, and (c) that the uniformity of the pattern increases the total biomass of prey and predators in the landscape. This emergent effect of spatial pattern upon a fundamental ecosystem function (productivity across trophic levels) confirms theory predicting linkages between patterning and production. Our results further imply that the landscape-level effects of any set of features that induce local gradients in ecological processes are likely to hinge on the spatial patterning of these features, with highly uniform spacing often producing the strongest net outcomes. Future work should address how the landscape-level effects of different spatial patterns vary with the shape and slope of biotic distance-response functions, as well as with possible interactive effects among patterned features. Our study highlights the importance of conserving pattern-inducing taxa and processes—in this case, termites and their mound-building activities. In Africa's fields and pastures, termites are sometimes eradicated to protect crops and forage, and mounds are sometimes destroyed to redistribute the nutrients within them [20], yet these actions may actually diminish overall landscape productivity. More generally, recent research shows that the influence of remnant trees in forest regeneration attenuates with distance [30], which means that restoration efforts will be most effective if organisms—such as trees and corals intended as nucleating agents for forests and reefs—are added to landscapes in uniform, gridded patterns (as theory suggests: [14]). Conversely, other desired ecological outcomes, such as the persistence of competitively inferior plant species, may be most effective if elements are arranged in aggregated distributions [31]. The uniform spacing of plants in plantations, and the ability to manipulate the spatial configuration of the agroscape, likewise provides opportunities to both study and apply the consequences of spatial patterning for the delivery of ecosystem services such as pest control and pollination [32],[33]. Materials and Methods Site Description We conducted this study between June 2006 and August 2009 at the Mpala Research Centre (0°20′ N, 36°53′ E) in central Kenya. Total rainfall during this period was 1,810 mm. The annual pattern was variable and tri-modal, with peaks in August (70 mm) and November (93 mm) of 2006; April (86 mm), June (152 mm), and September (98 mm) of 2007; and May (99.6 mm), July (58.7 mm), and October (143.8 mm) of 2008, followed by drought. The study area is underlain by flat, heavy-clay vertisol (“black cotton”) soils of recent volcanic origin, which are characterized by impeded drainage, pronounced shrink-swell dynamics [34],[35], and species-poor plant communities [36]. These soils and associated vegetation occur in many parts of East Africa, including Nairobi National Park and the western extension of Serengeti National Park [37]. Each A. drepanolobium tree is inhabited by one of four species of symbiotic ants (Crematogaster and Tetraponera spp. [38]). Trees inhabited by each ant species support robust communities of insects, predatory arthropods (primarily spiders and mantids), and dwarf geckos (Lygodactylus keniensis). Because worker ants do not appear to be frequent prey for any of the arboreal predators we studied, we did not include them in our samples or surveys. Adult male L. keniensis are distinguished by a chevron-shaped row of pre-anal pores and fiercely defend territories consisting of individual trees or adjacent trees with contiguous canopies, while several females and subadults can occur on the same tree [18]. Termite Mounds and Spatial Pattern Nests built by subterranean termites (Macrotermitinae: Odontotermes) occur in this and similar habitats throughout upland East Africa. As described above, various physical, chemical, and hydrological properties of mounds lead to greater productivity of both woody and herbaceous plants, revealed at our sites by both field measurements [23],[26] and remotely sensed imagery (Figure 1A; see also [39]). Similar effects of termite mounds on primary productivity occur in many systems [20]. Like all Macrotermitinae, Odontotermes spp. farm fungus in combs underground. Alates typically emerge with the first heavy rain of the wet season [21], but workers and soldiers are virtually never exposed aboveground (see Results), foraging instead within covered runways on the soil surface. Macrotermitinae mounds have long been known to occur with apparently even spacing in upland Kenya and other semi-arid regions throughout Africa (Figure 1A; [16],[19],[21]). Such regular spacing (20–120 m between mounds) arises from colonies' exhaustive partitioning of space into non-overlapping foraging areas (Text S1) [20]–[22]. We quantitatively evaluated mound patterning at different spatial scales using Ripley's K function [28]. Using the near-infrared band from an orthorectified Quickbird satellite image (June 20, 2003) with 2.4 m resolution and ∼3 km2 extent, we visually identified circular areas of high productivity, corresponding to termite mounds. To verify accuracy of our visual photo-interpretation, we field-recorded the geographic coordinates of 50 mounds using a CMT March II GPS (1–5 m accuracy), which we overlaid as a shape file upon the satellite image, confirming that these ground-truthed points did indeed appear as mounds on the image. We then applied Ripley's K to the coordinates of these mounds using Programita [40], establishing that the spacing of mounds is significantly uniform at spatial scales 1 m tall) for search. The mounds were several hundred meters apart. From July–August 2007, Pringle and two assistants exhaustively searched all trees for geckos, using ladders to reach high branches and probing within any hollows. For all 180 trees, we recorded the number of geckos, mound proximity (nearest 0.1 m), nearest-neighbor distance (nearest 0.1 m), height (nearest 0.1 m), basal diameter (nearest 0.1 cm), and resident Acacia-ant species. In August 2009, we repeated this process for an additional 477 trees at the same three termite mounds to obtain an independent dataset with which to test the predictive power of our best-fitting model of gecko abundance. Spider Fecundity Female spiders guarding egg masses were selected opportunistically and haphazardly. Upon collection, we preserved female spiders in ethanol, placed the egg masses in ventilated plastic cups in a common laboratory environment, and checked them periodically. When we were confident that all spiderlings had emerged from the egg sacs (∼14 d after first emergence), we froze the spiderlings and counted them using a dissecting microscope. It is extremely unlikely that cannibalism among spiderlings during this interval influenced our results; we are not aware of any reports of cannibalism among newly hatched juveniles in the Araneidae, and a bias would require that cannibalism was much more frequent among offspring of females far from termite mounds, which is improbable. Of 110 egg cases, 106 (96%) hatched in the laboratory. We calculated two measures of reproductive output for each female: total number of spiderlings and mean number of spiderlings per egg sac per female (each female's egg mass consisted of 1–12 individual egg sacs). Jocqué confirmed the genus identification for this as-yet-undescribed species and measured the width of the carapace and the combined length of the tibia and patella of leg I for each adult female. Measurements were made with an ocular graticule in a Leica M10 stereo microscope (measurement unit = 0.0164 mm). We could not obtain reliable carapace-width measurements for four females, giving us a final sample size of 102. Both measures of reproductive output were positively correlated with female carapace width (r = 0.24, F 1,100 = 6.1, p = 0.02 and r = 0.20, F 1,100 = 4.3, p = 0.04, respectively), while neither measure of reproductive output varied with tibia + patella length (both p≥0.5). Female carapace width was not significantly correlated with termite-mound proximity (r = −0.11, F 1,100 = 1.3, p = 0.3). Regression Modeling of Response Variables To determine the mechanisms (especially the role of termite-mound proximity) influencing tree-dwelling-arthropod abundance, gecko occurrence, and spider fecundity, we constructed sets of candidate regression models and ranked them using the AICc. Prior to constructing candidate sets, we visually examined the shape of the relationship between each response variable and mound proximity. In all candidate sets, we included both a raw mound-proximity term and one-or-more nonlinear transformations (loge for gecko abundance; square-root for spider abundance; loge, square, and square-root for arthropod abundance/biomass and Cyclosa fecundity; Tables S1–S3), as well as categorical mound-identity effects and (for all variables except spider fecundity) raw and transformed effects of tree size. Complete model sets and AICc results are available from Pringle on request. To explain variation in the number of geckos on trees, we employed ordinal logistic regression using the “Ordinal” routine in the Statbox 4.2 Toolbox for MATLAB (http://www.statsci.org/matlab/statbox.html). The dependent variable—number of geckos per tree in our dataset of 180 trees at three mounds—took values 0, 1, 2, or≥3. Independent variables included combinations of mound proximity, tree size (i.e., estimated surface area of the main stem, using the equation for the area of the side of a cylinder, which we considered a more accurate representation of gecko habitat size than either height or diameter alone), distance to the nearest tree ≥1 m tall, and mound identity. We constructed 108 candidate models using combinations of these variables, their natural logarithms, and their first-order interactions. We then ranked these models using AICc (Table S1). Our notation and interpretation follow Burnham and Anderson [29]. Of the five most likely models, all contained terms for both tree size and mound proximity (Table S1). Examination of the complete model set revealed that the importance of variables decreased in the order: tree size > mound proximity > mound identity > nearest-tree distance. We evaluated the goodness-of-fit and predictive ability of our best model by comparing mean model predictions with mean observed results for 12 different categories of trees (assigned based on which of three mounds and which of four 10 m distance intervals they belonged to). We performed this test using both the original 180-tree dataset from 2006 (which reveals goodness-of-fit, Figure S3A) and a novel 477-tree dataset from 2009 (which reveals the substantial predictive power of our model: Figure S3B). We conducted multiple-regression analyses of the abundance of adult arboreal spiders (based on our sample of 70 trees that we sprayed with insecticide) that largely paralleled our ordinal-regression analyses of gecko abundance. Independent variables included combinations of mound proximity, estimated tree surface area, square-root transformations of these variables, their first-order interactions, and mound identity. We constructed 26 candidate models and ranked them using AICc (Table S2). Of the eight most likely models, all contained terms for mound proximity and mound identity (which encompassed seasonal variations in abundance); no model lacking a term for mound proximity received any empirical support. Examination of the complete model set revealed that variable importance decreased in the order: mound identity ≈ mound proximity > tree size. We analyzed arthropod abundance and biomass data (log-transformed to meet parametric assumptions) using multiple regression. Response variables included total arthropod abundance and biomass, prey-arthropod abundance and biomass, and predatory-arthropod abundance and biomass. We constructed 24 candidate models for each variable. Unlike for geckos and spiders alone, all models for arthropod abundance/biomass contained a mound-proximity term (either raw or transformed, as described above), but none contained interactions. The other predictors included raw and log-transformed tree size (estimated surface area, as described above) and mound identity. The best models (Table S3) explained between 2% (for prey-arthropod biomass) and 68% (for predatory-arthropod abundance) of the variation in the response variables. For spider fecundity, we constructed 16 candidate models using raw and transformed mound proximity, female carapace width, and mound identity as predictors. The best model (Table S3) explained 23% of the variation in spider fecundity. For each response variable, we used the single best model for all spatial analyses (see below) and all tests of statistical significance for individual predictors. The log-transformed mound-proximity term was a better predictor of gecko abundance than the linear form. Square-transformed mound-proximity terms best approximated the responses of all arthropod variables except predator abundance, which was best approximated by a linear term, and prey biomass, which was best approximated (albeit non-significantly) by a log-transformed term. Spatial Analysis of Patterns in Consumer Abundance We extrapolated to the landscape scale for six response variables (predatory-arthropod abundance/biomass, total arthropod abundance/biomass, gecko occurrence, and spider fecundity) using a 600×600 m section of our study area, which included 62 termite mounds (Figure 1A). We mapped this area using Quickbird satellite imagery and calibrated the map in the field with a laser rangefinder. We subdivided this area with a grid of 5×5 m cells that defined 14,400 sample points and computed the distance of each point to the nearest mound. We then applied the best-fitting regression model to the distance value for each point. This enabled us to produce the spatial probability distribution of gecko abundance in Figure 2B and also to compute the mean value across all points for each response variable. In making these estimates, we used the observed median value of all other predictor variables (which, depending on the model in question, included tree size, spider-carapace width, and categorical mound-identity effects: Tables S1–S3). In other words, although we refer to these estimates as “real-” or “over-dispersed-landscape” values, they actually estimate abundances in hypothetical landscapes in which the distribution of mounds corresponds to reality, but all trees, female spiders, etc. are assumed to be an identical, typical size, and tree density is assumed to be uniform throughout the landscape (we provide support for this last and most-important assumption below). We then compared the estimated mean landscape value of each response variable from the actual, over-dispersed mound landscape with the corresponding distributions of values from simulated random landscapes that lacked the uniform spacing of real mounds (Figure 4). To do this, we generated 1,000 hypothetical landscapes that had the same number of mounds (N = 62) as the real landscape, but a nearly Poisson (independent and random) distribution of the mounds. To generate these landscapes, we randomly picked sets of latitudes and longitudes to define the location of each mound center within the same size area as that actually surveyed. The only restriction on these randomly generated mound positions was that all mound centers be at least 10 m apart, since real mounds have radii of 5–10 m and cannot overlap. For each simulated mound landscape, we repeated the estimation procedure (described above for the actual landscape) to produce 1,000 hypothetical landscape-wide average values for each response variable. Comparing the mean estimated values of all sample points from the 1,000 hypothetical random landscapes with the mean values obtained from the actual, over-dispersed landscape generated the results shown in Figure 4. As mentioned above, the most likely real-world complication that could influence the results of the randomization tests just described is variation in tree densities at different distances from mounds. For one mound in our study area, we mapped the positions of all trees out to ∼35 m in all directions; for five other mounds, we did the same for a ∼35 m radius semicircle. We used these data to determine whether and how the densities of trees >1 m tall vary with distance from mound centers. We used the following procedure to determine densities. First, for each mound, we used a MATLAB routine to construct Voronoi or Thiessen polygons [41] around each tree in the mapped area, which provides an estimate of local, tree-specific density. Next, we constructed a convex-hull line between the trees that defined the outermost boundaries of the mapped area. Because Voronoi polygons cannot be accurately estimated around these boundary trees, we eliminated these trees from further analyses. We then binned the remaining trees into either 5 or 10 m distance bins and divided the number of trees in each bin by their summed polygon areas to arrive at a distance-specific tree-density estimate. Using these estimates, we applied general linear models with distance and mound identity as independent variables and density as the dependent variable. For 5 m bins, mound ID is highly significant (p 1.) Size and weight data were compared using two-way factorial ANOVA. To ascertain whether the effect of mound proximity on gecko occupation arose from variation in prey availability, we repeated this experiment in conjunction with daily food supplementation. Insects, which included mealworms, termite workers found in dried dung, and sweep-net contents (all collected off site), were always added to the cups between 7:30 and 8:30 a.m., immediately prior to the onset of peak gecko activity. We did not attempt to capture any geckos during this phase of the experiment (Text S3). As before, we conducted 12 surveys of all posts. Mean monthly rainfall did not differ between the pre- and post-prey-addition periods (63.2±46.1 mm and 52.4±34.9 mm, respectively; F 1,12 = 0.2, p = 0.7). We analyzed the data from both runs of this experiment using a single repeated-measures MANOVA design (in JMP 8.01). The dependent variables were the mean occupation frequencies of each post during the 12 surveys prior to prey addition and the same mean frequencies for the 12 surveys conducted during daily prey addition to the far posts. The between-subject factors were post size (large versus small), mound proximity (close versus far), their interaction, and mound identity. The within-subject factor was time (pre- versus post-prey addition). In this design, the significant time × mound proximity interaction (Table S3) shows the equalizing effect of experimental food supplementation. Supporting Information Figure S1 Contextual photographs. (A) Aerial view of apparently homogeneous black-cotton ecosystem. (B) Ground view of Odonotermes mound (white arrow pointing to dark-green vegetation patch) and "large-close" experimental post (foreground). (C) Portion of excavated termite mound showing fungus-comb chambers (white arrows). (D) Lygodactylus keniensis gecko occupying a "small" experimental post. (6.06 MB TIF) Click here for additional data file. Figure S2 Results of Ripley's K -function analysis of termite mounds in a ∼3-km2 portion of the landscape that includes our study area. L(d) values (a transformation of Ripley's K for which zero indicates the number of neighbors expected in a random landscape, negative values indicate fewer-than-expected neighbors, and positive values indicate more neighbors than expected) are plotted against distance. Dashed red lines represent 95% confidence limits expected from a random landscape, solid black line represents observed L(d). The significantly lower-than-expected values of L at scales 300 m reflect clustering at the landscape scale. Thus, evenly spaced lattices of mounds are embedded in a landscape in which overall mound density varies (perhaps as a function of resource availability). Note that the minimum value of L (reflecting maximally even spacing) occurs at a spatial scale of approximately 30 m, which corresponds to the mean distance to the nearest mound center in the mapped portion of the landscape (29.22 m). (0.17 MB TIF) Click here for additional data file. Figure S3 Goodness of fit and predictive power of gecko model. We binned all trees into 12 categories based on which of three mounds (M3, M6, and M19) and four distance categories (0-10 m, 10-20 m, 20-30 m, and 30-40 m) they belonged to. For each of these categories, we calculated the observed mean number of geckos per tree and plotted the values against those predicted by the model. A 1:1 line, indicating perfect correspondence between model predictions and results, is plotted for comparison. (A) Goodness-of-fit. Based on the original 2006 data from 180 trees at three mounds, which was used to parameterize the model (correlation coefficient: r = 0.65; r = 0.91 when the major outlier, a category with only 5 trees, is excluded). (B) Predictive power. We applied the same model (with identical parameters) to a novel dataset of 477 trees at the same three mounds, collected in August 2009 (correlation coefficient: r = 0.75). (0.18 MB TIF) Click here for additional data file. Figure S4 Dependence of response variables on mean distance to nearest mound. Dependence of mean values of response variables on mean distance to nearest mound in 1,000 simulated random landscapes for (A) total-arthropod biomass, (B) predatory-arthropod biomass, (C) gecko abundance, and (D) spider fecundity. For each artificial landscape, generated by the random placement of mound locations, the mean distance to the nearest mound (horizontal axis) and the landscape-wide mean of the response variable (vertical axis) are plotted. The distribution of points in (A), (B), and (D) is identical due to the shared form of the best-fitting multiple-regression model for these variables. The scatterplots for total- and predatory-arthropod abundance (not shown) are similar to those for biomass in (A-B). These results show that average measures of community productivity are greatest in simulated landscapes in which mounds were by chance more over-dispersed, and that landscape-scale productivity decreases with increasing aggregation of mounds, because clumping results in greater average distance to the nearest mound center. (0.22 MB TIF) Click here for additional data file. Figure S5 Tree-density corrected simulation results. Frequency distributions of mean landscape values in 1,000 simulated landscapes of randomly placed mounds in which we controlled for variation in tree density with distance from termite mounds (cf. Fig. 4 and Materials & Methods: Spatial analysis of patterns in consumer abundance). (A) Total-arthropod biomass, (B) predatory-arthropod biomass, (C) geckos, (D) spider fecundity, (D) predatory-arthropod abundance, (E) total-arthropod abundance. Vertical bars show the mean landscape values for each variable obtained using the evenly spaced distribution of termite mounds in the mapped 0.36-km2 area of the landscape (Figure 1A). The best-fitting models used in the analyses are presented in Tables S1-S3. (0.79 MB TIF) Click here for additional data file. Table S1 Top five ordinal-regression models of gecko abundance. The five ordinal regression models of gecko occupancy patterns that received "substantial" empirical support (Δi < 2) according to the second order Akaike Information Criterion (AICc), along with a model (for comparison) that contained only a constant. The best-fitting model, which we used for simulations, appears in bold. Δi is the difference between a model's AICc value and that of the model with the lowest AICc; the Akaike weight wi is likelihood of a given model's being the best model in the set [ref. 29, main text]. Examination of the full set of 108 candidate models shows that Tree Size and Mound Proximity (in that order) were by far the most important predictors of gecko abundance. (0.02 MB XLS) Click here for additional data file. Table S2 Top eight ordinal-regression models of spider abundance. The eight ordinal regression models of adult spider occupancy that received "substantial" empirical support (Δi < 2) according to the second order Akaike Information Criterion (AICc), along with a model (for comparison) that contained only a constant. The best-fitting model, which we used for simulations, appears in bold. Symbols correspond to those in Table S1. Examination of the full set of 26 candidate models shows that Mound Proximity and Mound Identity are the most important predictors of spider abundance; the best-fitting model that did not include a term for distance (which contained the predictors Mound Identity and square-root-transformed Tree Size) had Δi = 32.92 and wi = 0.000, indicating essentially zero empirical support [ref. 29, main text]. (0.02 MB XLS) Click here for additional data file. Table S3 Best-fitting multiple-regression models for arthropod response variables. Best-fitting multiple-regression models for arthropod response variables, which we used in simulations for each variable, with P values from effect tests on the term for mound proximity. Best-fitting models were selected from candidate sets of 24 (for abundance/biomass measures) or 16 (for spider fecundity) using AICc, as described in the Materials and Methods. The model for prey-arthropod biomass appears in italics because it explains a trivial amount of the variance, and because the negative relationship with mound proximity was not statistically significant. (0.02 MB XLS) Click here for additional data file. Table S4 Repeated-measures MANOVA table for gecko occupancy in the habitat-selection experiment. The dependent variables were (a) the mean occupation frequencies for each post during the 12 surveys prior to prey addition and (b) the same frequencies for the 12 surveys conducted during daily prey addition to far posts only. The Time*Proximity interaction term is significant because the experimental addition of prey equalized gecko occupancy rates at 10 m and 30 m from termite mounds (Fig. 3A, main text). (0.02 MB XLS) Click here for additional data file. Text S1 Mechanism underlying patterning of termite mounds. (0.05 MB DOC) Click here for additional data file. Text S2 Potential direct effects between termites and predators. (0.03 MB DOC) Click here for additional data file. Text S3 Interpretation of gecko habitat-selection experiment. (0.02 MB DOC) Click here for additional data file.
Author and article information
Contributors
Hannah Karuri
Journal
Journal ID (nlm-ta): Heliyon
Journal ID (iso-abbrev): Heliyon
Title:
Heliyon
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Elsevier
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2405-8440
Publication date PMC-release: 10
December
2021
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December
2021
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December
2021
Volume: 7
Issue: 12
Electronic Location Identifier: e08588
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Publisher Item ID: S2405-8440(21)02691-8 Publisher ID: e08588DOI: 10.1016/j.heliyon.2021.e08588
PMC ID: 8683729
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This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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August
2021
Date
revision received
: 8
October
2021
Date
accepted
: 8
December
2021
Categories
Subject:
Research Article
Data availability: