DOI: 10.5061/dryad.pzgmsbcv8
title: Data from: Soil macrofauna communities vary by land use type and environmental conditions in the Serengeti-Mara ecosystem

description: Soil macrofauna are useful indicators of soil health, given their low
 resistance to environmental stressors. The magnitude of such stressors
 varies by land use type and environmental conditions. Despite their
 ecological importance, soil macrofauna remain understudied in the Eastern
 Afrotropics. The greater Serengeti-Mara ecosystem (GSME) holds high
 conservation value and is experiencing acute environmental strain. Our
 study surveyed soil macrofauna communities across four habitat types:
 bush/forest, grassland, human use, and wetlands, following the Tropical
 Soil Biology & Fertility (TSBF) sampling protocol. We discuss the
 community structure and dynamics of ants (Hymenoptera: Formicidae),
 termites (Insecta: Isoptera), and earthworms (Annelida: Oligochaeta) due
 to their relative abundance and biomass in soil communities and for their
 role as ecosystem service providers. Redundancy analysis (RDA) revealed
 the partitioning of habitat types by relative water resource availability
 as quantified by distance to water (m), litter water content (%), and
 litter mass (g/m2). Water limitation increased between bush/forest,
 grassland, and human use habitats, respectively. The spatial patterning of
 habitat diversity and soil macrofauna communities alike are similarly
 linked to local moisture availability in the study region. Ants were
 observed at higher abundances than termites or earthworms throughout the
 study system and especially within relatively water resource-limited
 grassland and human-use study areas. By contrast, earthworms were observed
 most frequently and at higher relative abundances in bush/forest and
 wetland habitats. Termite abundances were low for nearly every study site.
 These patterns emphasize the degree to which landscape-scale heterogeneity
 plays a role in the spatial patterning of soil macrofauna communities in a
 semi-arid tropical landscape.
description: <strong>Study system</strong> The
 Greater Serengeti-Mara ecosystem (GSME) straddles the Kenya-Tanzania
 border of East Africa (1◦15′-3◦30′S, 34–36◦E) (Fig. 1A) and hosts one of
 the most abundant ungulate populations in the world. The protected areas
 of the GSME form a large swath of unfenced land which facilitates the
 annual migration of 1.6 million ungulates (McNaughton, 1985). The edge
 conservancies of the GSME therefore represent the final stronghold
 buffering wildlife populations from the negative effects of rapid land-use
 intensification on the fringes of the core protected area (Newmark, 2008;
 Veldhuis et al., 2019). The GSME—approximately 25,000 km2 in area—is
 dominated by vast expanses of acacia savannah. Within our study area
 (1◦01′-1◦11′S, 35◦08′-35◦20′E) (Fig. 1B), the landscape is dominated by
 bush/forest and open grassland habitat types. The spatial ecology of the
 GSME is due in large part to its prevailing temperature and precipitation
 patterns. The study region has distinct wet and dry seasons resulting from
 a bimodal annual rainfall pattern (Bartzke et al., 2018; Norton-Griffiths
 et al., 1975). Daily temperatures range as low as 7.3 ◦C and as high as
 28.5 ◦C annually (Mukeka et al., 2019), with daily dry season high
 temperatures ranging from 23.3 ◦C - 26.6 ◦C. Average regional temperatures
 are increasing in response to land-use intensification-driven vegetation
 changes (Nduati et al., 2013; Ogutu et al., 2008).
 While humans and their livestock have been present in the GSME
 for millennia (Asiema and Situma, 1994), the last few decades have brought
 about dramatic shifts in land and livestock management practices resulting
 in marked environmental changes at the landscape level. Traditional Maasai
 pastoralism, mainly through the keeping of cattle, holds significant
 sociocultural and economic importance (Asiema and Situma, 1994). Although
 often considered more compatible with conservation than other land use
 types (Western, 1982), the influence of pastoralism on the ecology of
 protected areas varies greatly by context. While the overstocking of
 livestock (i.e., cattle, sheep, and goats) may harm local plant and animal
 assemblages (Lamprey and Reid, 2004), low to moderate livestock grazing
 intensity may promote grazing succession for herbivorous wildlife species
 (Herrik et al., 2023). Within conservancies, the herds
 of cattle which are ubiquitous on the landscape by day are kept in ‘bomas’
 overnight. Bomas are mobile livestock enclosures that create feces-rich
 patches on the landscape, ultimately altering the physical and chemical
 characteristics of the soil (Reid and Ellis, 1995; Stelfox, 1986). Each
 conservancy maintains and implements its own livestock grazing plan; human
 and livestock use therefore differs between conservancies and across
 seasons in accordance with annual rainfall patterns. Cattle management
 strategies vary spatiotemporally, ranging from the full exclusion of
 cattle in core areas to year-round rotational grazing within structured
 grazing blocks. Our sampling areas primarily consisted
 of four conservancies which make up the northernmost end of the GSME
 (Table 1; Fig. 1B). It is here, between the fenceless conservancies to the
 south and densely populated human settlement areas to the north, that the
 human-livestock-wildlife interface is most vulnerable to the effects of
 land use change. To investigate the extent to which permanent livestock
 and/or human settlement impact soil macrofauna communities, we also
 sampled in the neighboring Emarti village (EMA) and neighboring private
 wildlife estate, Naretoi Wildlife Estate (NAR). Emarti is a village
 densely populated by humans and livestock alike. Naretoi, by contrast, is
 a ‘re-wilded’ human estate development made up of privately-owned
 plots. <strong>Macrofaunal sampling
 design</strong> For the purposes of our study, we
 chose to focus on three soil invertebrate groups: ants (Hymenoptera:
 Formicidae), termites (Insecta: Isoptera), and earthworms (Annelida:
 Oligochaeta) due to their relative abundance in soil communities and for
 their ecological role as ecosystem engineers (Anderson, 1995; Beare et
 al., 1997). However, data were recorded for 17 groups of soil
 macroinvertebrates sampled at our sites (Supplementary Table S1), defined
 as any soil animal which exceeds 2 mm in length (Swift et al., 1979).
 Specimens were identified using a dichotomous key published by the Natural
 History Museum (Potts and Holt, n.d.). Relevant keys for the
 identification of East African soil macroinvertebrates to the species
 level were not available and thus species-level observations are not
 reported. Fortunately, changes in group composition at a higher taxonomic
 scale (i.e., class, order) have been posited to better indicate the
 effects of changes in soil biodiversity on ecosystem function than changes
 at finer taxonomic resolutions (Beare et al., 1997).
 Following the Tropical Soil Biology & Fertility (TSBF)
 sampling protocol (Anderson and Ingram, 1994), we sampled soil macrofauna
 by excavating 25 x 25 x 20 cm (length x width x depth) soil monoliths—
 intact vertical sections of soil carefully extracted from the ground to
 preserve physical characteristics (Taylor, 1960)—at five replicate points
 at each randomly selected site location within each nested habitat and
 study area strata. GPS coordinates (accuracy within five meters) were
 taken for the central point at each site, and replicate points were each
 placed 15 m from the central point perpendicular to one another
 (Supplementary Fig. S1). Surface-dwelling macroinvertebrates were
 collected prior to monolith extraction to minimize sampling bias during
 the collection process. After the extraction at each sample point within a
 given site, soil samples were hand sorted without replacement to remove
 all soil invertebrate animals &gt;2 mm in body length. Furthermore,
 the dry, clay-laden soils observed throughout the study region minimized
 concern of subterranean soil animal escape during the soil monolith
 excavation process. After identification, extracted organisms were fixed
 in test tubes containing 70 % alcohol. We sampled at 18 sites across four
 habitat types. Each site had five nested sample points, resulting in the
 excavation of 90 monoliths across bush/forest (n = 15), grassland (n =
 40), human use (n = 15), and wetland (n = 20) habitat types (Table 2) from
 June–July during the years Parreira et al., 2022 and 2023. This period
 marks the annual transition between the region’s late wet and early dry
 seasons (Norton-Griffiths et al., 1975). The hand-sorting method was
 implemented in accordance with the TSBF sampling protocol (Anderson and
 Ingram, 1994) to minimize any potential undersampling of earthworms in the
 study system which may be in seasonal diapause. With
 respect to earthworms, we employed a visual approach to distinguish
 between aestivated, juvenile, and adult earthworm species (Supplementary
 Fig. S2). Juveniles lack the clearly visible clitellum possessed by adult
 earthworms (Edwards, 2004; Sims and Gerard, 1985). Aestivated individuals,
 possessing tightly-wrapped, shrunken bodies bound by mucus and
 pebble-coated cocoons (Storey and Storey, 2012), were extracted from
 aestivation chambers in the soil (Storey and Storey, 2012). This quiescent
 state may cause external features, including reproductive organs, to
 become less prominent or obscured due to the worm’s contracted posture and
 protective mucous layers (Bayley et al., 2010; Juan et al., 2000). Most
 earthworms collected were juveniles, aestivated, or adults whose growth
 was stunted by drought conditions, thereby failing to meet the assumptions
 of existing species-level dichotomous keys. Such low numbers of
 reproductively mature earthworms in combination with a paucity of keys for
 the identification of East African soil macroinvertebrates therefore
 rendered the identification of earthworms to species level infeasible for
 inclusion in this analysis.
 <strong>Characterization of environmental
 features</strong> Habitat types were selected to
 include those which characterize the local landscape: bush/forest,
 grassland, human use, and wetland areas. A stratified site sampling
 approach was taken to ensure accurate habitatlevel representation of the
 local landscape during data collection. Bush/forest areas are
 characterized by closed canopy cover dominated by woody species (Sites O,
 P, and R). Grasslands are open expanses with plant communities made up of
 grasses and forbs (Sites A, B, E, H, K, L, M, and Q) which dominate the
 local landscape. Human-use areas experience direct day-to-day effects of
 human settlement (Sites D, F, and G). Our human-use study sites included
 the town center which experiences high vehicle and foot traffic in
 addition to high livestock grazing intensity (Site D), a schoolyard
 subject to intermittent cropping and livestock grazing (Site F), and a
 family garden (Site G). Our wetland habitat zones experience frequent soil
 water inundation, often at elevational minima within the local landscape
 (Sites C, I, J, and N). Note that floodplains and wetland areas are not
 intrinsically mutually inclusive. Low-lying wetland areas experience
 predictable soil water inundation while floodplains are subject to
 catastrophic flooding at infrequent intervals (McClain et al.,
 2014). Site-level habitat type was characterized in
 situ and in combination with satellite imagery. Environment and management
 information were taken at each point of soil fauna extraction. The
 following metrics were recorded to characterize environmental variation:
 distance to water (m), distance to boma (m), litter water content (%),
 fresh litter mass (g m-2), vegetation biomass (m3 km-2 year-1) (Food and
 Agriculture Organization, 2002), and soil clay content (%).
 Geospatial data quantifying distances to water and bomas were
 analyzed using QGIS (version 3.28, 2023). Upon placement of the 25 x 25 x
 20 cm metal frame used to designate the location of each replicate sample
 point, all surface organic litter within the frame was collected. Litter
 samples were then weighed twice: once immediately following collection and
 once upon drying completely. The difference in measurements was used to
 calculate the proportion of water content present by mass in each litter
 sample at the time of collection. Soil samples were
 collected by hand at each sample point following litter collection.
 Percent clay content was determined via soil particle size analysis (Gee
 and Bauder, 1986), which assumes that soil particle types fall into
 distinct size classes. Sand particles are largest (2000–50 μm), followed
 by silt particles (50–2.0 μm) and clay particles (&lt; 2.0 μm).
 Following Gee and Bauder’s, 1986 protocol, we created a homogeneous slurry
 of soil and water which was left to sit after homogenization. After soil
 particles had stratified into distinct horizons by particle size, relative
 proportions of soil particle size classes were calculated to determine
 percent soil clay particle content.
 <strong>Statistical analysis</strong>
 Abundance data for each taxon were subject to a log (n + 1)
 transformation prior to statistical analyses to normalize distributions
 while minimizing zero-inflation. Log (n) transformations were carried out
 for those continuous variables with high variances or those that did not
 fit a normal distribution and for which no zeroes were present (i.e.,
 distance to water and distance to boma). Quantiles, means, and standard
 error (SE) values were calculated on transformed abundance data for each
 taxon across habitat types. Analysis of variance (ANOVA) and Tukey tests
 were carried out to assess the magnitude of the true difference in mean
 taxon abundances and to calculate pairwise confidence intervals between
 taxa. All p-values reported in the text are derived from these Tukey
 tests. Our point-presence proportion metric captured
 the proportion of replicate points within a sample site at which a
 particular taxon was present. These point-presence proportion quantile
 values, means, and SE values were calculated for all taxa across habitat
 types. Again, ANOVA and Tukey tests were employed to assess the true
 difference in mean point-presence proportions between taxa.
 We Hellinger-transformed our raw taxon abundance prior to
 carrying out a redundancy analysis (RDA). Upon ordination, Euclidean
 values for axes 1 and 2 (RD1 and RD2, respectively) were extracted and
 plotted separately from the initial RDA biplot. Convex hulls were
 constructed around Euclidean values for each habitat type using the
 stat_chull() function from R’s ‘ggpubr’ package (Kassambara, 2022) to
 model the proportion of variance in taxon abundances explained by the
 environmental predictors included in the model. Mean Euclidean values were
 calculated for each habitat type and included in the plot to characterize
 differences in environmental characteristics within and between habitat
 types. We constructed logistic and negative binomial
 regression models to perform the explanatory modeling of patterns of soil
 invertebrate presence and abundance (Supplementary Table S2). Coefficient
 estimates and 95 % confidence intervals (CIs) were extracted from these
 models to construct forest plots. Effect sizes for all predictors included
 in the model were scaled and centered for improved model convergence and
 interpretability. Shannon diversity (H) was calculated
 by applying the diversity (index = “shannon”) function and argument in the
 ‘vegan’ R package (Dixon, 2003) to the raw abundance data collected for
 all 17 taxa for which data were collected at each sampling point.
 Taxonomic density at a sampling point was calculated as the total number
 of taxonomic groups for which one or more individual was sampled.
 Point-level evenness was calculated by dividing Shannon diversity by the
 natural log (ln) of the taxonomic density at each sampling point. Pairwise
 differences between mean values of Shannon diversity, taxonomic density,
 and evenness indices across habitat type were calculated using ANOVA and
 Tukey tests. Statistical tests were also carried out to
 test for significant within and between-site differences in community
 composition (i.e., Shannon diversity, taxonomic density, and evenness
 indices). Dunn’s and Kruskal- Wallis multiple comparisons tests using rank
 sums were carried out to test for significant within-site differences.
 Kruskal-Wallis chi-squared and adjusted p-values were calculated by
 applying the dunn.test (method = “bonferroni”) function and argument in
 the ‘dunn.test’ R package (Dinno, 2024). Pairwise differences between mean
 values of Shannon diversity, taxonomic density, and evenness indices
 between sites were calculated using ANOVA and Tukey tests.
description: # Data from: Soil macrofauna communities vary by land use type and
 environmental conditions in the Serengeti-Mara ecosystem Dataset DOI:
 [10.5061/dryad.pzgmsbcv8](https://doi.org/10.5061/dryad.pzgmsbcv8) ##
 Description of the data and file structure Presence and abundance data
 were collected for 14 soil macrofauna invertebrate taxa using the Tropical
 Soil Biology & Fertility (TSBF) hand-sorting method in Kenya's
 Maasai Mara.  **Note**: Including location data will not pose any risk to
 endangered or vulnerable species in the ecosystem.  ### Files and
 variables #### File: ANTS2.csv **Description:**  filtered (ants
 observations only), cleaned dataset in long format with all environmental
 and site variables. Raw and transformed presence/abundance values and
 community (i.e., site-level diversity + richness indices) included. #####
 Variables * Area: Study area name abbreviation (Enonkishu Conservancy
 [ENO], Emarti township [EMA], Ol Choro Conservancy [OLC], Lemek
 Conservancy [LEM], Naretoi Wildlife Estates [NAR], and Mbokishi
 Conservancy [MBO]). * Protected: Study area protection status. Binary
 variable (Y = 1, N  = 0). * SiteID: Unique Site ID (A-R). Each
 comprisesrised of 5 points, at which replicate monoliths were extracted. *
 Point: Unique point ID. Each site has 5 points, numbered arbitrarily
 (1-5).  * PointID: Unique Point ID (e.g., A1, A2, A3, A4, A5)
 corresponding to each of the sampling points within a particular site.  *
 SoilType: Soil type classification based on USDA Soil Texture Triangle
 classifications. Designation based on calculated volumetric proportions of
 clay, sand, and silt for each soil monolith extracted.  * PctClay: % clay
 of soil sample extracted at a sampling point. * Lat: Sample site latitude
 (taken from center of sampling site, +/- 5m accuracy) * Long: Sample site
 longitude (takenthe  from centthe er of sampling site, +/- 5m accuracy) *
 DistWater: Distance to nearest permanent/semi-permanent water source
 (i.e., creek, river, pond, wetland) in metersthe  from centthe er of
 sample site. * Elev: Sample site elevation in meters.  * TreeCov%: % tree
 cover in 1 km2 surrounding sample site using satellite data. Not used in
 final analysis. * Floodplain: Binarythe , is sample site within a
 floodplain area? (Y = 1, N = 0).  * CatGrazing: Categorical,
 semi-qualitative variable classifying cattle grazing intensity (NONE, LOW,
 MODERATE, HIGH) based on researcher field observations, conservancy
 management, plans and discussions with herders and managers.  * Fire: Fire
 history at sample site within last 2 years (Y = 1, N = 0). * hab_type:
 grassland (open, dominated by grasses and/or shrubs), wetland (frequently
 inundated, low-lying areas of landscape), human-use (areas under intensive
 daily use by humans and/or livestock), bush/forest (closed canopy areas
 dominated by woody species). * FreshWgtCor: Fresh litter weight collected
 at sample point in grams (g). * PctWaterCt: % of fresh litter weight as
 water (calculated as % difference between fresh and dry litter weights in
 g) * ShanDiv: Shannon Diversity (H) index calculated (ants, termites,
 earthwormthe s) at sample point. * SpecRich: Species richness calculated
 (includes all 14 taxa for which data were originally collected) at sample
 point. * taxa: Ants * num_ind: Number of individuals observed at a
 sampling point (integer) * logBiomass: log-transformed local biomass value
 (m3km-2y-1) * logRainprev_30: log-transformed precipitation value (in
 cm)the 30 daysperiod prior to soil monolith excavation. Not used in final
 analyses. * logRainprev_7: log-transformed precipitation value (in cm)
 over the 7-day pbeforeior before soil monolith excavation. Not used in
 final analyses. * logRainprev_2: log-transformed precipitation value (in
 cm) for the 2 period prior to soil monolith excavation. Not used in final
 analyses. * logSpecAbun: Log-transformed species abundance at sampling
 point (calculated using observations from all 14 taxa for whichwereta was
 originally collected).  * logNum_ind: Log-transformed number of
 individuals observed at a sampling point by taxa.Presencee: Binary
 indicator of taxon presence at a sampling point within a site (Y = 1, N =
 0).  * proportion: Proportion of ecosystem engineering soil
 macroinvertebrate (ants, termites, earthworms) community at a particular
 sampling point (i.e., if 10 ants, 5 termites, and 5 earthworms are
 observed at a sampling poin,t their proportions will be 0.50, 0.25, 0.25,
 respectively).  #### File: DECOMP2.csv **Description:** Filtered (ants,
 termites, and earthworms), cleaned dataset in long format with all
 environmental and site variables. Raw and transformed presence/abundance
 values and community (i.e., site-level diversity + richness indices)
 included. ##### Variables * Area: Study area name abbreviation (Enonkishu
 Conservancy [ENO], Emarti township [EMA], Ol Choro Conservancy [OLC],
 Lemek Conservancy [LEM], Naretoi Wildlife Estates [NAR], and Mbokishi
 Conservancy [MBO]). * Protected: Study area protection status. Binary
 variable (Y = 1, N  = 0). * SiteID: Unique Site ID (A-R). Each
 scomprisesised of 5 points, at which replicate monoliths were extracted. *
 Point: Unique point ID. Each site has 5 points, numbered arbitrarily
 (1-5).  * PointID: Unique Point ID (e.g., A1, A2, A3, A4, A5)
 corresponding to each of the sampling points within a particular site.  *
 SoilType: Soil type classification based on USDA Soil Texture Triangle
 classifications. Designation based on calculated volumetric proportions of
 clay, sand, and silt for each soil monolith extracted.  * PctClay: % clay
 of soil sample extracted at a sampling point. * Lat: Sample site latitude
 (taken from center of sampling site, +/- 5m accuracy) * Long: Sample site
 longitude (takenthe  from centthe er of sampling site, +/- 5m accuracy) *
 DistWater: Distance to nearest permanent/semi-permanent water source
 (i.e., creek, river, pond, wetland) in meters from centthe er of the
 sample site. * Elev: Sample site elevation in meters.  * TreeCov%: % tree
 cover in 1 km2 surrounding sample site using satellite data. Not used in
 final analysis. * Floodplain: Binarthe y, is sample site within a
 floodplain area? (Y = 1, N = 0).  * CatGrazing: Categorical,
 semi-qualitative variable classifying cattle grazing intensity (NONE, LOW,
 MODERATE, HIGH) based on researcher field observations, conservancy
 management, plans and discussions with herders and managers.  * Fire: Fire
 history at sample site within last 2 years (Y = 1, N = 0). * hab_type:
 grassland (open, dominated by grasses and/or shrubs), wetland (frequently
 inundated, low-lying areas of landscape), human-use (areas under intensive
 daily use by humans and/or livestock), bush/forest (closed canopy areas
 dominated by woody species). * FreshWgtCor: Fresh litter weight collected
 at sample point in grams (g). * PctWaterCt: % of fresh litter weight as
 water (calculated as % difference between fresh and dry litter weights in
 g) * ShanDiv: Shannon Diversity (H) index calculated (ants, termites,
 earthwormthe) at sample point. * SpecRich: Species richness calculated
 (includes all 14 taxa for which data were originally collected) at sample
 point.Taxaa: Taxon for which raw abundance values and transformed values
 are calculated (ants, termites, and earthworms). * num_ind: Number of
 individuals observed at a sampling point (integer) * logBiomass:
 log-transformed local biomass value (m^3^km^-2^y^-1^) * logRainprev_30:
 log-transformed precipitation value (in cm)the 30 daysperiod prior to soil
 monolith excavation. Not used in final analyses. *
 logRainprev_7: log-transformed precipitation value (in cm) over the 7-day
 period to soil monolith excavation. Not used in final analyses. *
 logRainprev_2: log-transformed precipitation value (in cm) for the 2
 period prior to soil monolith excavation. Not used in final analyses. *
 logSpecAbun: Log-transformed species abundance at sampling point
 (calculated using observations from all 14 taxa for whichwereta was
 originally collected).  * logNum_ind: Log-transformed number of
 individuals observed at a sampling point by taxa.Presencee: Binary
 indicator of taxon presence at a sampling point within a site (Y = 1, N =
 0).  * proportion: Proportion of ecosystem engineering soil
 macroinvertebrate (ants, termites, earthworms) community at a particular
 sampling point (i.e., if 10 ants, 5 termites, and 5 earthworms are
 observed at a sampling poin,t their proportions will be 0.50, 0.25, 0.25,
 respectively).  #### File: TERMS2.csv **Description:** Filtered (termites
 observations only), cleaned dataset in long format with all environmental
 and site variables. Raw and transformed presence/abundance values and
 community (i.e., site-level diversity + richness indices) included. #####
 Variables * Area: Study area name abbreviation (Enonkishu Conservancy
 [ENO], Emarti township [EMA], Ol Choro Conservancy [OLC], Lemek
 Conservancy [LEM], Naretoi Wildlife Estates [NAR], and Mbokishi
 Conservancy [MBO]). * Protected: Study area protection status. Binary
 variable (Y = 1, N  = 0). * SiteID: Unique Site ID (A-R).
 Eachcomprisesprised of 5 points, at which replicate monoliths were
 extracted. * Point: Unique point ID. Each site has 5 points, numbered
 arbitrarily (1-5).  * PointID: Unique Point ID (e.g., A1, A2, A3, A4, A5)
 corresponding to each of the sampling points within a particular site.  *
 SoilType: Soil type classification based on USDA Soil Texture Triangle
 classifications. Designation based on calculated volumetric proportions of
 clay, sand, and silt for each soil monolith extracted.  * PctClay: % clay
 of soil sample extracted at a sampling point. * Lat: Sample site latitude
 (taken from center of sampling site, +/- 5m accuracy) * Long: Sample site
 longitude (takenthe  from centthe er of sampling site, +/- 5m accuracy) *
 DistWater: Distance to nearest permanent/semi-permanent water source
 (i.e., creek, river, pond, wetland) in meters fromthe  center ofthe 
 sample site. * Elev: Sample site elevation in meters.  * TreeCov%: % tree
 cover in 1 km2 surrounding sample site using satellite data. Not used in
 final analysis. * Floodplain: Binarthe y, is sample site within a
 floodplain area? (Y = 1, N = 0).  * CatGrazing: Categorical,
 semi-qualitative variable classifying cattle grazing intensity (NONE, LOW,
 MODERATE, HIGH) based on researcher field observations, conservancy
 management plan,s and discussions with herders and managers.  * Fire: Fire
 history at sample site within last 2 years (Y = 1, N = 0). * hab_type:
 grassland (open, dominated by grasses and/or shrubs), wetland (frequently
 inundated, low-lying areas of landscape), human-use (areas under intensive
 daily use by humans and/or livestock), bush/forest (closed canopy areas
 dominated by woody species). * FreshWgtCor: Fresh litter weight collected
 at sample point in grams (g). * PctWaterCt: % of fresh litter weight as
 water (calculated as % difference between fresh and dry litter weights in
 g) * ShanDiv: Shannon Diversity (H) index calculated (ants, termites,
 earthworthe ms) at sample point. * SpecRich: Species richness calculated
 (includes all 14 taxa for which data were originally collected) at sample
 point. * taxa: Termites * num_ind: Number of individuals observed at a
 sampling point (integer) * logBiomass: log-transformed local biomass value
 (m3km-2y-1) * logRainprev_30: log-transformed precipitation value (in
 cm)the 30 daysperiod prior to soil monolith excavation. Not used in final
 analyses. * logRainprev_7: log-transformed precipitation value (in cm)
 over the 7-day period prior to soil monolith excavation. Not used in final
 analyses. * logRainprev_2: log-transformed precipitation value (in cm)the
 2 daysperiod prior to soil monolith excavation. Not used in final
 analyses. * logSpecAbun: Log-transformed species abundance at sampling
 point (calculated using observations from all 14 taxa for which data were
 originally collected).  * logNum_ind: Log-transformed number of
 individuals observed at a sampling point by taxa. * presence: Binary
 indicator of taxon presence at a sampling point within a site (Y = 1, N =
 0).  * proportion: Proportion of ecosystem engineering soil
 macroinvertebrate (ants, termites, earthworms) community at a particular
 sampling point (i.e., if 10 ants, 5 termites, and 5 earthworms are
 observed at a sampling poin,t their proportions will be 0.50, 0.25, 0.25,
 respectively).  #### File: soildat.csv **Description:** Intra-site soil
 composition and litter mass variability (average and standard error
 values) and corresponding habitat types. ##### Variables * SiteID: Unique
 Site ID (A-R). Each comprisesrised of 5 points, at which replicate
 monoliths were extracted. * variable: clay (% clay at sampling point) and
 litter (average litter weight in [g] at sampling point) * avg: average
 value (% clay or litter weight in grams) calculated across 5 replicate
 sampling points within a given site. * sterr: Standard error associated
 with the average value (% clay or litter weight in grams) calculated
 across 5 replicate sampling points within a given site. * hab_type:
 grassland (open, dominated by grasses and/or shrubs), wetland (frequently
 inundated, low-lying areas of landscape), human-use (areas under intensive
 daily use by humans and/or livestock), bush/forest (closed canopy areas
 dominated by woody species).: #### File: WORMS2.csv
 **Description:** Filtered (earthworms observations only), cleaned dataset
 in long format with all environmental and site variables. Raw and
 transformed presence/abundance values and community (i.e., site-level
 diversity + richness indices) included. ##### Variables * Area: Study area
 name abbreviation (Enonkishu Conservancy [ENO], Emarti township [EMA], Ol
 Choro Conservancy [OLC], Lemek Conservancy [LEM], Naretoi Wildlife Estates
 [NAR], and Mbokishi Conservancy [MBO]). * Protected: Study area protection
 status. Binary variable (Y = 1, N  = 0). * SiteID: Unique Site ID (A-R).
 Eachcomprisesprised of 5 points, at which replicate monoliths were
 extracted. * Point: Unique point ID. Each site has 5 points, numbered
 arbitrarily (1-5).  * PointID: Unique Point ID (e.g., A1, A2, A3, A4, A5)
 corresponding to each of the sampling points within a particular site.  *
 SoilType: Soil type classification based on USDA Soil Texture Triangle
 classifications. Designation based on calculated volumetric proportions of
 clay, sand, and silt for each soil monolith extracted.  * PctClay: % clay
 of soil sample extracted at a sampling point. * Lat: Sample site latitude
 (taken from center of sampling site, +/- 5m accuracy) * Long: Sample site
 longitude (taken from center ofthe  sampling site, +/- 5m accuracy) *
 DistWater: Distance to nearest permanent/semi-permanent water source
 (i.e., creek, river, pond, wetland) in meters fromthe  center ofthe 
 sample site. * Elev: Sample site elevation in meters.  * TreeCov%: % tree
 cover in 1 km2 surrounding sample site using satellite data. Not used in
 final analysis. * Floodplain: Binarthe y, is sample site within a
 floodplain area? (Y = 1, N = 0).  * CatGrazing: Categorical,
 semi-qualitative variable classifying cattle grazing intensity (NONE, LOW,
 MODERATE, HIGH) based on researcher field observations, conservancy
 management plan,s and discussions with herders and managers.  * Fire: Fire
 history at sample site within last 2 years (Y = 1, N = 0). * hab_type:
 grassland (open, dominated by grasses and/or shrubs), wetland (frequently
 inundated, low-lying areas of landscape), human-use (areas under intensive
 daily use by humans and/or livestock), bush/forest (closed canopy areas
 dominated by woody species). * FreshWgtCor: Fresh litter weight collected
 at sample point in grams (g). * PctWaterCt: % of fresh litter weight as
 water (calculated as % difference between fresh and dry litter weights in
 g) * ShanDiv: Shannon Diversity (H) index calculated (ants, termites,
 earthworms) atthe  sample point. * SpecRich: Species richness calculated
 (includes all 14 taxa for which data were originally collected) at sample
 point. * taxa: Earthworms * num_ind: Number of individuals observed at a
 sampling point (integer) * logBiomass: log-transformed local biomass value
 (m3km-2y-1) * logRainprev_30: log-transformed precipitation value (in cm)
 overthe 30 daysd prior to soil monolith excavation. Not used in final
 analyses. * logRainprev_7: log-transformed precipitation value (in cm)
 over the 7-day period prior to soil monolith excavation. Not used in final
 analyses. * logRainprev_2: log-transformed precipitation value (in cm)
 over the 2-day period prior to soil monolith excavation. Not used in final
 analyses. * logSpecAbun: Log-transformed species abundance at sampling
 point (calculated using observations from all 14 taxa for which data were
 originally collected).  * logNum_ind: Log-transformed number of
 individuals observed at a sampling point by taxa. * presence: Binary
 indicator of taxon presence at a sampling point within a site (Y = 1, N =
 0).  * proportion: Proportion of ecosystem engineering soil
 macroinvertebrate (ants, termites, earthworms) community at a particular
 sampling point (i.e., if 10 ants, 5 termites, and 5 earthworms are
 observed at a sampling poin,t their proportions will be 0.50, 0.25, 0.25,
 respectively).  #### File: scaled_logcoefs.csv
 **Description:** Environmental variable coefficients scaled using a
 logarithmic distribution ##### Variables * taxa: Taxon for which scaled
 coefficients are calculated (Ants, termites, and earthworms). * variable:
 Environmental site variable for which estimates, standard errors, CI,s and
 p-values are calculated. logDistWater_scaled (site distance to permanent
 water source in meters, log-transformed and scaled), logBiomass_scaled
 (local biomass production m3k-2y-1, log-transformed and scaled),
 logDistBoma_scaled (distance to nearest boma in meters, log-transformed
 and scaled), PctWaterCt_scaled (% litter water content by weight in g,
 scaled), PctClay_scaled (% clay in soil sample, scaled),
 FreshWgtCor_scaled (fresh litter weight in g, scaled). * estimate:
 Site-level coefficient estimate for each given variable.  * se: Standard
 error value * lower_ci: Lower 95% CI value * upper_ci: Upper 95% CI value
 * p: Associated p-value (a = 0.05) #### File: RDA_obs.csv
 **Description:**  ##### Variables * hell_Earthworms: Hellinger-transformed
 earthworm abundance values by sampling
 poHell_Antsl_Ants: Hellinger-transformed ants abundance values by sampling
 poHell_Termitesrmites: Hellinger-transformed termites abundance values by
 sampling point.  #### File: RDA_env.csv **Description:**  ##### Variables
 * PctClay: % clay of soil sample extracted at a sampling point. * Elev:
 Site elevation in meters (m) * hab_type: grassland (open, dominated by
 grasses and/or shrubs), wetland (frequently inundated, low-lying areas of
 landscape), human-use (areas under intensive daily use by humans and/or
 livestock), bush/forest (closed canopy areas dominated by woody species).
 * FreshWgtCor: Fresh litter weight collected at sample point in grams (g).
 * PctWaterCt: % of fresh litter weight as water (calculated as %
 difference between fresh and dry litter weights in g) *
 logDistWater: Log-transformed distance to nearest permanent/semi-permanent
 water source (i.e., creek, river, pond, wetland) in meters from center of
 sample site. * logBiomass: log-transformed local biomass value (m3km-2y-1)
 #### File: scaled_nbcoefs.csv **Description:** Environmental variable
 coefficients scaled using a negative binomial distribution ##### Variables
 * taxa: Taxon for which scaled coefficients are calculated (Ants,
 termites, and earthworms).Variablee: Environmental site variable for which
 estimates, standard errors, CI,s and p-values are calculated.
 logDistWater_scaled (site distance to permanent water source in meters,
 log-transformed and scaled), logBiomass_scaled (local biomass production
 m3k-2y-1, log-transformed and scaled), logDistBoma_scaled (distance to
 nearest boma in meters, log-transformed and scaled), PctWaterCt_scaled (%
 litter water content by weight in g, scaled), PctClay_scaled (% clay in
 soil sample, scaled), FreshWgtCor_scaled (fresh litter weight in g,
 scaled). * estimate: Site-level coefficient estimate for each given
 variable.  * se: Standard error value * lower_ci: Lower 95% CI value *
 upper_ci: Upper 95% CI value * p: Associated p-value (a = 0.05) All
 missing data are represented as NA. ## Code/software R/RStudio. See .RMD
 file (Invert_Report.rmd) for versions, packages, and workflow. It
 aggregates observations by grouping and calculates relative proportions,
 then prepares datasets for RDA analysis and plotting.  This code loads and
 cleans multiple ecological datasets, derives transformed and aggregated
 variables, and then conducts exploratory analyses, statistical tests, and
 multivariate ordinations to examine taxon abundance, habitat differences,
 and environmental relationships. ## Access information Other publicly
 accessible locations of the data: *
 [https://www.globalsoilmacrofauna.com](https://www.globalsoilmacrofauna.com)