DOI: 10.5061/dryad.dz08kps43
title: Pattern of seasonal variation in rates of predation between spider families is temporally stable in a food web with widespread intraguild predation

description: Intraguild predation (IGP) – predation between generalist predators
 (IGPredator and IGPrey) that potentially compete for a shared prey
 resource – is a common interaction module in terrestrial food webs.
 Understanding temporal variation in webs with widespread IGP is relevant
 to testing food web theory. We investigated temporal constancy in the
 structure of such a system: the spider-focused food web of the forest
 floor. Multiplex PCR was used to detect prey DNA in 3,300 adult spiders
 collected from the floor of a deciduous forest during spring, summer, and
 fall over four years. Because only spiders were defined as consumers, the
 web was tripartite, with 11 consumer nodes (spider families) and 22
 resource nodes: 11 non-spider arthropod taxa (order- or family-level) and
 the 11 spider families. Most (99%) spider-spider predation was on spider
 IGPrey, and ~90% of these interactions were restricted to spider families
 within the same broadly defined foraging mode (cursorial or web-spinning
 spiders). Bootstrapped-derived confidence intervals (BCI’s) for two
 indices of web structure, restricted connectance and interaction evenness,
 overlapped broadly across years and seasons. A third index, % IGPrey (%
 IGPrey among all prey of spiders), was similar across years (~50%) but
 varied seasonally, with a summer rate (65%) ~1.8x higher than spring and
 fall. This seasonal pattern was consistent across years. Our results
 suggest that extensive spider predation on spider IGPrey that exhibits
 consistent seasonal variation in frequency, and that occurs primarily
 within two broadly defined spider-spider interaction pathways, must be
 incorporated into models of the dynamics of forest-floor food
 webs. 
description: <em>Study
 system</em> We
 collected spiders and potential non-spider prey from the oak-dominated
 (<em>Quercus alba and Q. rubra</em>) Swallow Cliff Woods (41°
 40.519’ N, 87° 51.437’ W) within the 320-ha Swallow Cliff nature preserve
 in Palos Township, Illinois (USA). The preserve, which is within the
 Chicago metropolitan region, is managed by the Cook County Forest Preserve
 District. Forests in this region are actively managed for several invasive
 plants (23), and the forest floor at Swallow Cliffs contains a thick
 leaf-litter layer with an abundant and diverse arthropod
 community.  <em>Collecting spiders and non-spider
 prey</em> Our goal
 was to search the ground layer and low understory as thoroughly as
 possible, so that we would collect enough spiders from less-abundant
 families to yield the same number of spiders per family analyzed for prey
 DNA. We did not estimate spider densities. All collections were made
 between 1000 and 1600 hours. We collected from a different location each
 day. The size of the area searched each day was not measured and varied
 with the number of searchers. Collecting areas were widely distributed
 throughout Swallow Cliff Woods, but we did not subdivide the Woods into
 sampling regions. Most terrain was upland forest, but some collections
 were taken from a few scattered wet/marshy areas. The number of collecting
 days in each season was spring (31), summer (33), and fall (29) over the
 years 2009, 2010, 2011 and 2012; the number of days per year was 33, 12,
 34 and 14, respectively. On
 each collecting day, we used both litter sifting and simple searching to
 capture spiders from several microhabitats. For litter sifting, we placed
 litter collected by hand into a flat tray (58 cm x 17 cm x 15 cm) with a
 screen bottom. This tray was shaken over a second tray of the same size
 with a solid bottom, allowing arthropods to fall through the screen to be
 collected by hand or aspirator. Sifted litter was returned to its original
 location. Spiders were also collected by hand from the litter surface,
 open areas in the litter, logs, low vegetation up to ~1m, and tree trunks
 up to ~2m. Individual spiders were placed in separate labelled
 vials. Of the spiders that
 were eventually analyzed for prey DNA (see below), 81% were captured from
 either leaf litter (70%) or adjacent bare ground/logs (11%). Thus, most
 spiders were collected from the litter layer broadly defined. The litter
 layer is a fairly distinct subsystem with respect to rates of migration of
 arthropod predators and prey (24). Nevertheless, we did not limit our
 definition of the “forest floor” to the litter layer because many spiders
 spin webs in vegetation close to the ground. Also, some cursorial species
 move back and forth between the ground and lower understory vegetation and
 tree trunks (for example, 84% of the Corinnidae, a guild of “foliage
 runners” (25), were collected from leaf litter). Therefore, we also
 analyzed spiders that had been collected from low vegetation (10%) and
 tree trunks (9%). All
 specimens were placed on ice within one hour of capture. On the same day,
 spiders collected for detection of consumed prey using PCR were taken to
 the laboratory where they were weighed and stored at -20◦C in a 1.5-mL
 microcentrifuge tube containing 95% ethanol (EtOH). Spiders and non-spider
 prey (see below) intended for primer development or assay optimization
 (see below for details) were kept alive, weighed, placed individually into
 60-mL glass vials, and provided with water ad libitum at room temperature.
 Spiders were identified to family and genus using identification guides
 (26-29). Voucher specimens (one adult male and female) for each genus
 (when available) were archived at The Field Museum (Chicago,
 Illinois). Over the four
 years, ~14,000 spiders (juveniles and adults) from 20 families were
 collected. Presence of prey DNA was tested for adult spiders from 11
 abundant families (those with at least 300 adults) that live primarily on
 the forest floor. Spiders from six of these families (Corinnidae,
 Gnaphosidae, Lycosidae, Pisauridae, Salticidae, and Thomisidae) do not
 spin webs to capture prey (“cursorial” spiders). The other five families
 (Agelenidae, Dictynidae, Hahniidae, Linyphiidae, and Theridiidae) are “web
 spinners.” This dichotomy reflects basic differences in foraging behavior
 (16, 17), but the distinction is not absolute. The web spinners in our
 food web include genera of spiders that also forage for prey off their web
 (18). Non-spider arthropod
 prey were also collected for primer development. They were not sampled
 quantitatively, but were simply selected due to their apparent abundance
 in leaf litter and/or activity just above the litter layer, and their
 likely occurrence in the diets of at least one spider family (15-17, 30).
 Non-spider nodes of the food web were broadly defined taxonomically (at
 the Order level except for Gryllidae):  flies
 (<em>Diptera</em>), moths/butterflies
 (<em>Lepidoptera</em>), springtails
 (<em>Collembola</em>), ants/bees/wasps
 (<em>Hymenoptera</em>), jumping bristletails
 (<em>Archaeognatha</em>), crickets
 (<em>Gryllidae</em>), pseudoscorpions
 (<em>Pseudoscorpiones</em>), harvestmen
 (<em>Opiliones</em>), beetles
 (<em>Coleoptera</em>), earwigs
 (<em>Dermaptera</em>), and pillbugs
 (<em>Isopoda</em>). <em>Molecular
 techniques</em> <em>Primer development and
 optimization</em> We
 utilized multiplex PCR to sequence DNA from at least ten spiders from each
 family and at least ten specimens from each non-spider prey taxon. Each
 spider was first starved for at least ten days to eliminate any
 gut-content DNA that may have been present. Specimens were then
 homogenized in 180 μL of phosphate-buffered saline (PBS) (Hoefer, San
 Francisco, CA). DNA was then extracted with a Qiagen DNEasy Tissue Kit
 (Valencia, CA) using the manufacturer’s protocol. Upon completion of DNA
 extraction, the 200μL of eluate was well-mixed, separated into 20μL
 aliquots, and stored at -20°C until analysis. The general arthropod primers LCO-1490 and
 HCO-2198 (31) were used to amplify DNA from the mitochondrial genome’s
 cytochrome oxidase I (COI) region. Eluate from DNA extractions was
 amplified and sequenced by The Field Museum (Chicago, IL) or Research
 Resources Center (RRC) at the University of Illinois, Chicago. Sequences
 were used to conduct BLASTN searches following the protocol developed by
 (32) using the databases GenBank and BOLD (the Barcode of Life Database).
 Following (33), database sequences were used only if they showed ≥97%
 match to submitted sequences. Sequences were aligned using the CLUSTALW or
 AMPLICON programs. Primers were designed with the assistance of the IDT
 (Integrated DNA Technologies, Coralville, IA) program PrimerQuest and
 tested for melting temperature and CG content using Sci-Tools
 OligoAnalyzer (IDT). <em>Spider gut-content
 testing</em> After a
 PCR assay was developed and optimized for a particular prey taxon (spider
 family or non-spider arthropod), frozen field-caught adult spiders were
 tested for the presence of the target-prey DNA. Spiders were thawed to
 room temperature and underwent DNA extraction and PCR amplification as
 described above. The entire spider was homogenized, except for the largest
 individuals, for which legs were removed to increase the prey/predator DNA
 ratio; coxae were left attached to the body when possible because spider
 guts often extend into the coxae (17). The homogenate was then mixed and
 4uL were added to a well (on a 96-well plate) that contained 21 uL of
 Master Mix. Every run also included positive, negative, and blank controls
 to ensure that target DNA was amplified and that no contamination existed
 on the run. Positive controls consisted of DNA specific to the target
 taxon in question, negative controls contained the PCR Master Mix without
 DNA template, and blank controls were created from MBG water. A sample was
 considered positive for target-prey DNA within the spider’s gut if the Ct
 value of the amplification curve was above the background threshold, if
 the shape of the curve was sigmoidal, and if the positive and negative
 controls were acceptable. Samples that did not show amplification were
 re-analyzed using arthropod-general primers (31) before identifying them
 as negative results; questionable samples (low amplification or a
 non-sigmoidal shape) were re-tested. For constructing the food web, adult
 spiders (as close as possible to 1:1 :: male:female)  were selected at
 random from genera within the most abundant families to yield 25, 50, 75,
 100, or 150 per genus (roughly in proportion to relative abundances) for a
 total of 300 / family. Each spider was analyzed for DNA from the entire
 range of potential prey, i.e., the 11 non-spider arthropod taxa and the
 other 10 spider families. Only 17 out of 3300 spiders tested positive for
 two different prey items; these were treated as two separate interactions
 (as if there were two spiders). Only one spider tested positive for more
 than two prey: a thomisid tested positive for 7 different prey taxa, which
 was considered an outlier and was removed from the analysis (refer to S1
 File for a discussion of possible causes of the low number of spiders
 testing positive for more than one prey item).  Because of these minor
 adjustments, the number of spiders tested for prey in our statistical
 analysis ranged from 299-304 per family. <em>Analysis of food web
 structure</em> <em>Interaction
 matrix</em> PCR
 results were translated into an 11 x 22 interaction matrix in which each
 row (i) represented a consumer, i.e. one of the 11 focal spider families;
 and the ith cell in each column (j) contained the number of the ith
 consumer that tested positive for DNA for one of the other spider families
 or one of the 11 non-spider taxa. Intra-family predation, which includes
 cannibalism within species, no doubt occurred in our study system, but
 could not be detected by PCR. Thus, matrix cells (i,i) by necessity = 0,
 and the food web that was analyzed contained not 11 x 22, but  11 x
 (11+10) = 231 potentially detectable predator-prey interactions.
 The entire matrix, with years and
 seasons pooled, constituted the summary web. To examine temporal
 variability, we created interaction matrices for each year (2009, 2010,
 2011, and 2012; seasons pooled) and each season (spring, summer, and fall;
 years pooled). We did not analyze matrices for each year x season
 combination because small sample sizes for many combinations would have
 made such analyses to be of questionable value.  <em>Food web structure:</em> 
 Graphical depictions Interactions in our tripartite food web can
 most easily be depicted as an interaction network using a graphical
 technique that places the consumer categories (the 11 spider families) in
 the middle row, with triangles connecting each spider family with the
 non-spider prey depicted in the lower row, and spider prey in the upper
 row (34). These diagrams reflect the direction and magnitude of trophic
 interactions based solely upon spiders that tested positive for prey DNA.
 The width of each triangle reflects the relative proportion of that prey
 in the consumer’s diet. Proportions of tested spiders that had no
 detectable prey DNA are reported separately (S1 Table). <em>Food web structure: </em>
 Indices Numerous indices
 have been proposed to measure food web structure. The goal often has been
 to determine whether the observed index indicates a degree of organization
 and distribution of links between nodes that differ from a null hypothesis
 of a random arrangement. We addressed a different question:  do measures
 of food web structure vary temporally?  Three indices were calculated that
 are appropriate to evaluating changes in a spider-focused, hybrid
 source-sink web. The first two, restricted connectance and interaction
 evenness, are abstract measures of overall network structure. The third
 measures the frequency of predator-predator interactions within the
 context of widespread IGP. Restricted connectance -- Measures of
 connectance compare the number of realized links to the number of possible
 links in a food web (1, 35). The number of possible links varies according
 to assumptions about the nature and direction of predator-prey
 interactions in the web, yielding a range of specific terms for
 connectance (36). We used a definition, more restrictive than most, that
 (i) matches the hybrid sink-source structure of our spider-focused web,
 and (ii) reflects the limitations of our PCR analyses, i.e. we could not
 test for intra-family predation (analogous to cannibalism in a food web in
 which species are nodes). To make this distinction clear, we use the term
 restricted connectance, defined as: C_(R )=  L/(J(J-1)+JI) where L is the number of realized links, J
 is the number of consumers (spider families), and I is the number of
 non-spider prey. Restricted connectance, which can vary from 0 to 1, is
 calculated from a binary (i.e. presence/absence ) interaction matrix. We
 re-sampled, with replacement, from subsets of the full data set to
 estimate margins of error (95% BCI) for yearly and seasonal estimates of
 restricted connectance. From 5,000 simulated values we extracted the
 median, and the 2.5 and 97.5 percentiles for the bootstrapped confidence
 interval (95% BCI). Analyses were performed in R version 3.2.1. For the
 yearly webs, three families (Corinnidae, Pisauridae and Gnaphosidae) were
 removed from the full data set due to low numbers collected in 2009; 25
 spiders were then sampled, with replacement, from each family for each
 year. Three families (Corinnidae, Pisauridae and Agelenidae) were removed
 before bootstrapping seasonal data due to low numbers collected in one
 season; 30 spiders were then sampled, with replacement, from each
 remaining family for each season. <em>Interaction evenness:</em>
 This metric reflects the relative intensity of interactions across a
 quantitative food web by incorporating the actual values in each cell of
 the interaction matrix. The metric was calculated using the
 “network-level’ function in the ‘bipartite’ R package (37). The metric
 scales between 0 and 1, with 1 = maximum evenness. We employed the “sum”
 version of the index, which calculates the index only for realized
 interactions.  Margins of error (95% BCI) for yearly and seasonal values
 of interaction evenness were estimated using the procedure employed for
 restricted connectance.  %
 IGPrey: A spider preyed upon by another other spider was an IGPrey if both
 spiders shared at least one non-spider prey, i.e., if both spiders, along
 with the non-spider prey, formed an IGP module. We defined % IGPrey as the
 percentage of all detected prey that were classified as IGPrey. Margins of
 error (95% CI) for values of % IGPrey by year (seasons pooled) and by
 season (years pooled) were based upon the normal distribution because
 sample sizes and success/failure rates satisfied assumptions. Whether
 there was an interaction between year and season was evaluated by
 selecting the best model for a 4 x 3 x 2 contingency table using a
 log-linear model with a log link function and a Poisson response (38, 39).
 The analysis was carried out using SAS version 9.4.
description: # Pattern of seasonal variation in rates of predation between spider
 families is temporally stable in a food web with widespread intraguild
 predation
 [https://doi.org/10.5061/dryad.dz08kps43](https://doi.org/10.5061/dryad.dz08kps43) Metadata for manuscript entitled “Pattern of seasonal variation in rates of predation between spider families is temporally stable in a food web with widespread intraguild predation”. Authors: David H. Wise Department of Biological Sciences, University of Illinois, Chicago, IL 60607, USA dhwise.uic@gmail.com Robin M. Mores 2474 N. Lake Shore Drive, Apt. 4708, Chicago, IL 60611, USA rmores.uic@gmail.com Jennifer Pajada-De La O Department of Mathematics, Statistics, and Computer Science, University of Illinois, Chicago, IL, 60607, USA jpajda2@uic.edu Matthew A. McCary Department of BioSciences, Rice University, Houston, TX 77005, USA matt.mccary@gmail.com File list (files can be found at https://github.com/mmccar26/spider-focused-food-web): Contingency\_4x3x2\_Analysis\_Description.odt Contingency Analysis.sas Figs. 4-7.R IGP-season-by-year.csv interaction\_evenness.csv intraguild\_predation.csv restricted\_connectance.csv PCR Bootstrapping Analysis.R Season\_Fall\_PCR.txt Season\_Spring\_PCR.txt Season\_Summer\_PCR.txt Spider-food-web.Rproj Summary\_PCR.txt Summary\_PCR\_RawData.csv Year\_2009\_PCR.txt Year\_2010\_PCR.txt Year\_2011\_PCR.txt Year\_2012\_PCR.txt 4x3x2\_IGP\_Data\_Matrix.xlsx File descriptions: Contingency\_4x3x2\_Analysis\_Description.odt: The full description of the 4 x 3 x 2 contingency table analysis using a log-linear model with a log link function and a Poisson distribution to test if there is an interaction between year and season for spider intraguild predation. Contingency Analysis.sas: The code to run the 4 x 3 x 2 contingency table analysis; the analysis was performed using SAS. Figs. 4-7.R: The underlying R code to recreate Figures 4 to 7 presented in the main manuscript, which presents the results on interaction evenness, restricted connectance, and intraguild predation. This code requires importing files “IGP-season-by-year.csv”, “interaction\_evenness.csv”, “intraguild\_predation.csv”, and “restricted\_connectance.csv” to run. IGP-season-by-year.csv: Data on the changes in spider intraguild predation by year and season. This file supports the data shown in Figure 7 of the manuscript. interaction\_evenness.csv: Data for showing interaction evenness pooled by year and season. This file supports the data shown in Figure 5 of the manuscript. intraguild\_predation.csv: Data for showing percent intraguild predation pooled by year and season. This file supports the data shown in Figure 6 of the manuscript. restricted\_connectance.csv: Data for showing percent intraguild predation pooled by year and season. This file supports the data shown in Figure 6 of the manuscript. PCR Bootstrapping Analysis.R: The underlying R code to calculate the PCR-based bootstrapped-derived confidence intervals (BCI’s) for two indices of overall network structure, restricted connectance and interaction evenness, overlapped broadly across both years and seasons. Season\_Fall\_PCR.txt: The spider food web interaction matrix for the Fall season. This data helps support Figure 3 in the main text. Season\_Spring\_PCR.txt: The spider food web interaction matrix for the Spring season. This data helps support Figure 3 in the main text. Season\_Summer\_PCR.txt: The spider food web interaction matrix for the Fall season. This data helps support Figure 3 in the main text. Spider-food-web.Rproj: The R Studio Project that hosts the R environment to run most of the analyses (i.e., the scripts named “PCR Bootstrapping Analysis.R” and “Figs. 4-7.R”). Summary\_PCR.txt: The basic interaction matrix for the full summary spider food web. This data helps support Figure 1 in the main text. Summary\_PCR\_RawData.csv: The raw PCR data for calculating the PCR-based bootstrapped-derived confidence intervals (BCI’s) for two indices of overall network structure, restricted connectance and interaction evenness, overlapped broadly across both years and seasons. Year\_2009\_PCR.txt: The spider food web interaction matrix for the year 2009. This data helps support Figure 2 in the main text. Year\_2010\_PCR.txt: The spider food web interaction matrix for the year 2010. This data helps support Figure 2 in the main text. Year\_2011\_PCR.txt: The spider food web interaction matrix for the year 2011. This data helps support Figure 2 in the main text. Year\_2012\_PCR.txt: The spider food web interaction matrix for the year 2012. This data helps support Figure 2 in the main text. 4x3x2\_IGP\_Data\_Matrix.xlsx: The data used to run the 4 x 3 x 2 contingency table analysis using a log-linear model with a log link function and a Poisson distribution. ## Description of the data and file structure Repository description: R scripts, SAS models, description of analyses, and data used to estimate how interaction evenness, restricted connectance, and intraguild predation changed as a function of year and season in a spider-focused forest-floor food web. ## Sharing/Access information All R files must be imported into your working directory for the analysis to work. The 4x3x2 Contingency Analysis was conducted using SAS; the underlying code is provided in “Contingency Analysis.sas” with the underlying description provided in the file named “Contingency\_4x3x2\_Analysis\_Description.odt”.