Top-down effects of intraspecific predator behavioral variation

doi:10.21203/rs.3.rs-3112635/v1

Download PDF

Research Article

Top-down effects of intraspecific predator behavioral variation

https://doi.org/10.21203/rs.3.rs-3112635/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Among-individual variation in predator traits is ubiquitous in nature. However, the role of intraspecific trait variation in trophic dynamics has been seldom considered in community ecology. This has left unexplored a) to what degree does among-individual variation in predator traits regulate prey populations and b) to what degree do these effects vary spatially. We address these questions by examining how predator among-individual variation in functional traits shapes communities across habitats of varying structural complexity, in field conditions for the first time. We manipulated Chinese mantis (Tenodera sinensis) density and trait variability in experimental patches of old fields with varying habitat complexity and quantified the impacts on lower trophic levels, specifically prey and plant biomass. Our mantis groups thus contrasted in density (six or twelve individuals) and levels of variation in a key behavioral trait, activity level (movement on an open field). Our metric of habitat complexity was the density of plant material. In complex habitats and at high mantis densities, behaviorally variable groups decreased prey biomass by 35.1%, while at low densities, low levels of behavioral trait variability decreased arthropod biomass by 27.1%. Behavioral variability also changed prey community composition. Our results are among the first to demonstrate that among-individual trait variation can shape open species-rich prey communities. Moreover, these effects depend on both predator density and habitat complexity. Incorporating this important facet of ecological diversity revealed normally unnoticed effects of functional traits on the structure and function of food webs.

Temperament

Mantoidea

intraspecific niche variation

species interactions

individual trait variation

habitat complexity

Sorting species into broad functional categories (e.g., hunting modes and size classes) and averaging continuous trait values (e.g., body size) have revealed that predator functional traits can predict facets of ecosystem functioning, such as prey population dynamics or producer growth (Preisser et al. 2007). However, grouping species into functional categories is just one way to summarize trait variation. Any effect of a predator population’s functional traits can be broken down into how each individuals’ traits drive their contributions to their populations’ prey consumption or non-consumptive effects (Bolnick et al. 2003; Van Valen 1965). California sea otters (Enhydra lutris nereis) vary in their dive times and depths, allowing them to capture diverse marine invertebrate species. Yet, each individual otter has its own stereotyped diving behavior, allowing them to specialize on a narrow subset of their population’s prey (Tinker et al. 2007). Studies like this demonstrate that predator populations contain variation across individuals in phenotypes or ecotypes that determine their ability to capture different prey (Costa-Pereira et al. 2018; Harmon et al. 2009; Post et al. 2008; Zhao et al. 2014). This kind of trait variability among individuals can contextually hamper or help coexistence between species on the same trophic level (Bolnick et al. 2011; Stump et al. 2022; Violle et al. 2012), but how this facet of trait variation affects interactions across trophic levels remains little known. To expand our ability to use functional traits to predict ecosystem structure, we investigate how between-individual variation in predator traits, alongside complex environments, affects their prey.

We identify mechanisms for how among-individual trait variation in predator traits affect prey survival from studies of among-species predator trait variation. These mechanisms hinge on whether predators interfere with or complement each other (Sih et al. 1998). Across studies, predator species that are more dissimilar from each other in their functional traits tend to antagonize each other less and kill more prey (Greenop et al. 2018). Whether variable predator species complement or antagonize (cannibalism, interference, etc.) each other depends on the availability of resources, such as different prey species to avoid competition or habitat refuges to avoid intra-guild predation (Finke and Denno 2002; Schmitz 2007). Granted enough area to hunt and refuges to avoid each other, among-individual trait variation should increase the rate at which a predator population eats their prey, to the extent that individuals’ traits determine what they eat and how they interact with members of their own species.

Evidence of individuals’ traits driving their interactions with other species can be found in the profusion of work on among-individual variation in behavioral traits, better known as animal personality (Wolf and Weissing 2012). Predator personality traits like activity level, aggressiveness, and boldness determine which prey individual predators eat (Pintor and Byers 2015; Toscano et al. 2016; Wilson et al. 1993), and the extent to which they antagonize conspecifics (Sih and Watters 2005). For example, aggressive Portia spiders are more likely to catch behaviorally erratic members of a prey spider species (Chang et al. 2017), and highly aggressive female fishing spiders are more likely to eat males of their species (Johnson and Sih 2005). These findings suggest that among-individual variation in predator behavioral traits should affect the survival of their prey. Indeed, sea stars with greater variability among individuals have been found to eat more prey because they compete with each other less (Pruitt et al. 2017). Further, Chinese mantis (Tenodera sinensis) behavioral traits affected the survival of their prey, but only in complex habitats (Lichtenstein et al. 2019). In contrast, the behavioral trait composition of perch did not affect their prey communities (Laskowski et al. 2022). These results confirm that among-individual predator trait variability can decrease prey survival, but these effects are varied, unpredictable, and depend on the complexity of the habitats in which interactions take place.

Complex habitats are defined by dense three-dimensional substrate (Attrill et al. 2000), like macroalgae in ponds and vegetation layers in forests (Crowder and Cooper 1982; Diehl 1992; McElhinny et al. 2005). They can help prey by reducing predation through refuges and avenues of escape (Cook and Streams 1984; Crowder and Cooper 1982; Diehl 1992; Uetz 1991; Vince et al. 1976) or providing them with food and space (Kovalenko et al. 2012). Complex habitats can alternatively benefit predators by providing them with ambush sites and cover for stalking (Smith et al. 2019). How habitat complexity favors animals often depends on their functional traits (Nooten et al. 2019). For instance, shy risk averse pumpkinseed sunfish are more likely than bold pumpkinseeds to use dense vegetation to hunt their prey (Wilson 1998). Structurally complex habitats may therefore give individuals with different functional traits the chance to pursue different strategies in how they kill prey and avoid their own predators (Snyder 2019). By these mechanisms, complex habitats may magnify the effects of predator among-individual trait variability on their prey (Lichtenstein et al. 2019).

Studies that assess the role of individuals’ behavior in predator prey interactions have generally been conducted in enclosed mesocosms with drastically simplified communities (Chang et al. 2017; Griffen et al. 2012; Lichtenstein et al. 2019; Lichtenstein et al. 2017; Toscano and Griffen 2013; Toscano and Griffen 2014). Simplified mesocosm studies can provide vital insight, but may vastly under- or overestimate the impact of their treatments (Skelly 2002; Stachowicz et al. 2008). Therefore, studies in open, field conditions are still needed to confirm whether the cascading effects of intraspecific behavioral variability on lower trophic levels holds in natural ecosystems. We therefore tested whether Chinese mantis’ behavioral trait variability shapes the structure of open intact meadows. The presence of Chinese mantises can decrease insect biomass and increase plant biomass in natural meadows (Hurd and Eisenberg 1984a; Moran and Hurd 1997; Moran et al. 1996). Thus, behavioral trait variability among individuals in this voracious predator species may have effects that propagate through the food web. We evaluated the following hypotheses: (1) Predator groups with high among-individual variability in activity level will (a) decrease prey biomass, (b) change the trophic composition of prey communities, and (c) increase plant biomass relative to behaviorally homogenous groups. (2) Structural complexity in these plots increases the strength of the above effects. Evaluating these hypotheses in wild communities helps to predict the top-down effects of among-population variation in predator traits on the ecosystems they inhabit (Bell 2005; Costa-Pereira et al. 2018; Michelangeli et al. 2018; van Dongen et al. 2010).

Study species and site:

We studied Chinese mantises (Tenodera sinensis), because they are widespread, invasive, marketed as biocontrol agents, and have strong effects on the biomass of many insect and plant species (Hurd and Eisenberg 1990; Moran et al. 1996). We collected mantises and constructed experimental plots at the Donald S. Wood field laboratory of the Pymatuning Laboratory of Ecology, located in northwest Pennsylvania (41°34′09.6′′N, 80°27′51.4′′W). The site contains a mixture of deciduous forest and old fields. These fields are dominated by Solidago canadensis and to a lesser extent Poaceae spp., with Toxicodendron radicans growing beneath the level of the S. canadensis. These fields were also interspersed with Cornus racemosa, Rubus spp., Rosa spp., and other Solidago spp. Among these plant species, we focused on Canada goldenrod (S. canadensis), the most common herb. From these fields, we collected roughly 450 mantises via sweep net from June 26th to July 9th 2018, between their 4th and 6th instars. Until their 8th instar, mantises are flightless. We measured mantises’ head to tip length using digital calipers (Fisher Traceable) and mass using portable scales (Fisher Scientific Education). Mantises had an average head to tip length of 31.23mm ± 6.42SD, ranging from 19-56mm, and an average mass of 100mg ± 61SD, ranging from 15–400 mg. Upon collection, we housed each mantis in 600ml plastic containers containing two sticks for perching and 2 cm of dampened topsoil to maintain humidity. To help standardize hunger level and motivation to forage, each mantis was fed a large size-matched katydid nymph (Orchelimum vulgare) upon collection. The next day we began behavioral testing.

Behavioral tests and habitat complexity estimates:

The day after capture (June 29th to July 10th ), we quantified variation in mantises’ activity level and then, two hours later, their aggressiveness towards prey. To decrease the interference of multiple tests and extended lab stay in our experiment, mantises deployed to plots (n = 405) were assessed only once each. These tests were used to create mantis groups with either high or low activity level variability. We ran a separate pool of mantises (n = 19) through four iterations of activity to evaluate how repeatable this behavior is across time. This second cohort of mantises was given a katydid upon capture and their activity level was evaluated the next day. We then fed them another katydid and repeated this sequence thrice more (four tests in total across eight days).

Activity level refers to animals’ propensity to move around their environment, which determines their hunting mode (Huey and Pianka 1981; Savino and Stein 1989; Schmitz 2008), space use patterns (Wilson and McLaughlin 2007), and vulnerability to predation (Pretorius et al. 2019; Smith and Blumstein 2008). Activity level was estimated by open field test. We gently placed mantises (n = 447) in the bottom of 28cm wide, 16cm long, and 36cm tall plastic arenas with 1cm graph paper taped to the outside of the top, back, left and right sides. After we gently placed mantises in the bottom of these containers, we gave them a 30s acclimation period and counted how many squares the mantises crossed with their heads over the next 300s, using the grid on whichever surface they were climbing. We cleaned arenas with 90% isopropyl alcohol between trials. This focal population of Chinese mantises behaves consistently on a very similar activity level test conducted using differently sized containers (Lichtenstein et al. 2019).

The next day, we categorized mantises into extreme or moderate activity level pools (Fig. 1a). Mantises within the 26th and 75th percentiles in activity level were deemed moderately active, and those between the 1st and 25th percentiles and 76th and 100th percentiles were both deemed relatively extreme mantises. Set numbers of mantises from the extreme (both high and low ends) and moderate pools were assigned to plot groups randomly (Fig. 1b). Low density, low behavioral variability treatment plots received five moderately active mantises and one extreme mantis (Mean activity = 34.30 ± 6.69 SD, Mean CV = 0.73 ± 0.18 SD). High density, low variability plots received ten moderately active mantises and two extreme mantises (Mean activity = 38.52 ± 10.22 SD, Mean CV = 0.77 ± 0.12 SD). Low density, high variability treatment plots received one moderate activity mantis and five extreme mantises (Mean activity = 39.24 ± 7.46 SD, Mean CV = 1.35 ± 0.26 SD). High density, high variability plots received two moderate activity mantises and ten extreme mantises (Mean activity = 39.54 ± 9.08, Mean CV = 1.46 ± 0.19 SD). These densities were chosen to reflect expected densities for the higher and lower ends of naturally occurring mantis densities (Hurd and Eisenberg 1984b). These variability manipulations lead to a 88.4% difference in these plots’ activity level coefficient of variation (Linear model: F_3,44=47.64, R² = 0.77, p < 0.01) without altering average activity (Linear model: F_3,44=0.92, R² = 0.06, p = 0.44). Density manipulations altered neither activity level CV (Linear model: F_1,44=0.39, R² = 0.01, p = 0.53) nor average activity (Linear model: F_1,44=0.68, R² = 0.01, p = 0.41). All treatments received some extreme and moderate mantises so that behavioral variability was effectively manipulated without sharply compressing the range of phenotypes present among treatment groups. These mantis groups do not include 15 individuals that died between testing and deployment.

Before mantis addition, we estimated the structural complexity of these plots by estimating foliage density. This is very similar to approaches that use plant biomass (Kovalenko et al. 2012), but foliage density is a more direct metric of the amount of three-dimensional structure available to these insects. We estimated foliage density by placing meter sticks at five randomly generated coordinates within each plot and measuring the height at which any part of any plant touched the sticks. Like other biomass-related complexity metrics, for herbivores this very likely means more living space and food (Heck Jr and Wetstone 1977). However, for the carnivorous mantises, this is strictly structural complexity.

Plot construction:

After mantis groups were assembled, we placed them into one of 60, 2m by 2m open air plots: twelve of each density and behavioral variability combination (Fig. 1b) and twelve plots with no mantises (controls). Plots were spaced 2m apart. It took twelve days to assemble these 60 plots (June 30th to July 11th ), but each was allowed to run for exactly 40 days. High density (3 mantis/m²) and low density (1.5 mantises/m²) treatments match local densities that arise from egg cases of different sizes (Hurd and Eisenberg 1984a). The plots’ design was based on those deployed by Moran et al. (1996), only smaller. A prior study showed that less than 10% of mantises added to similar plots attempted to leave (Hurd and Eisenberg 1984a), suggesting that juvenile mantises disperse relatively little. Our plots were constructed by mowing 4 m² squares into an old field in early April 2018, before leaves began to emerge. At this time, mantis ootheca were very easy to locate, so we removed them (~ 100 in total) and transported them to another old field over 100 m from the experimental plots. We then lined the perimeter of plots with 30 cm of black plastic sheeting held down with landscaping staples to prevent plant growth along plot borders (represented in Fig. 1).

Two weeks before experimental mantis addition, we confirmed whether no interloping mantises were present. We sampled these plots by sweep net, with eight swipes (two per side) roughly 75cm above the ground per plot. During these collections, we lined plots with 120 cm tall moveable polystyrene barriers to hamper jumping insects’ escape. No mantises were found during these collections, confirming that our ootheca removals were successful, and all other insects were returned immediately to their plots. Concurrently, we estimated initial average goldenrod biomass by measuring the height of each Canada goldenrod that touched the sticks during the foliage density estimation process. To estimate aboveground biomass based on stem height, we created allometric equations for Canada goldenrod (Supplementary Table S1) after Crutsinger et al. (2006). We measured the height of 48 Canada goldenrod, dried them in drying ovens at 35°C for 24 hours and weighed them immediately. We used AIC_c weight model selection to determine the best model for each species (Akaike 1987; Burnham and Anderson 2003). The best model had an R² value of 0.87.

After these initial measurements, we clipped all plants between the plots and 20cm into the plots, to prevent mantises from exiting by leaping from tall plants. We lined the center of the black plastic with a 10cm wide coat of Tanglefoot to capture any immigrants or emigrants. On several occasions, we observed mantises walk up to the Tanglefoot, touch it, and then retreat. We patrolled the plots twice daily to remove overgrown plants and reapply Tanglefoot.

Next, we applied mantis treatments to plots. Each day, from June 26th to July 9th 2018, until 1200 hours we collected as many mantises as possible from the property. Upon completion of activity level tests and aggressiveness estimates, we assigned mantises into the density and variability treatments described above (Fig. 1). Plots were established in groups with one of each treatment, where the ordering of treatments randomly determined within each group of plots. This procedure allowed a relatively homogenous yet randomly distributed set of treatments across space. We added mantises to the plots between 1500 and 1700 hours over the course of twelve days and maintained each plot for 40 days.

Plot deconstruction and measurements:

We maintained the plots for 40 days to ensure that mantises were in the plots long enough until they reached sexual maturity and dispersed away in mid-August. Deconstruction ran from August 9th to August 20th, and we collected all data blind to treatment groups. We sampled insects using a Craftsman (41BS2BVG799) 27cc leaf blower with the included vacuum kit, after Stewart and Wright (1995). To catch insects, we set organza wedding gift bags in the vacuum nozzle kept in place with wire rings. After assembling the 120cm tall polystyrene barriers, we ran the vacuum over the plants for 120s switching sides every 30s. This technique sampled insects from the lower to upper canopy of the plants. Bags were sealed and removed from the vacuum while the vacuum ran to prevent insect escape. Bags were then immediately placed in 90% ethanol. We collected at least 50 insect families (Supplementary Table S2), although we were unable to identify all Thysanopterans, because most specimens were very small and damaged. Upon transport back to the University of California Santa Barbara, insects were identified to family, counted, and their body lengths were measured. Dipterans and parasitoid wasps were excluded from the analysis, because high dispersal ability permits these taxa to move among plots with ease. We used allometric equations to estimate the biomass of insects from each family using the family-specific allometric equations presented in Ganihar (1997). These estimates corroborated the less taxa-specific equation proposed by Rogers et al. (1976). When calculating total biomass, we removed two large orthopterans, because they were more than twice the combined mass of all other arthropods sampled from their plots and their inclusion resulted in two clear outliers. For the purpose of understanding how our treatments affect prey functional categories, these arthropods were sorted into five functional categories: predators, herbivores, fungivores, non-eaters (insects that only eat as larvae), and ants (categorizations shown in Supplementary Table S2). Ants do not cleanly fit into any of these categories, so we put them in a category of their own. Families were assigned to these groups based on available information of their diets. We then calculated the total biomass of each group for each plot.

Within 48h of each plot’s deconstruction, we measured the height of every Canada goldenrod (n = 14,182) stem of all sizes. We used the allometric equations described above (Supplementary Table S1) to estimate the aboveground biomass of each stem. We calculated average stem biomass of each plot rather than total biomass and subtracted the initial average goldenrod biomass, because total biomass is heavily influenced by stem density, which the mantises were unlikely to change in 40 days. They could, however, plausibly influence the growth of stems already sprouted when they arrived. All plot deconstruction transpired over twelve days.

Statistical analyses:

We estimated the repeatability of mantis activity level across four trials using a linear mixed model (LMM) fit with 1000 bootstrap iterations and a Gaussian error distribution in the rptR package (Stoffel et al. 2017) in R 3.4.1. The model had “trial number” as a fixed effect, “mantis ID” as a random effect, and “activity level” for its response variable. Because body length could potentially influence activity level via stride length, we fit a linear model with body length as the predictor variable and activity level as the response variable.

To gauge how accurately our measurements of habitat complexity describe their plots, we estimated the variance in the foliage density explained by plot ID. We used a linear mixed model (LMM) model with 1000 bootstrap iterations and Gaussian error distribution, but with “Plot ID” as a random effect and “foliage density” as the response variable, also fit with rptR. Finally, we tested whether structural complexity differed across the five treatments of mantis density and behavioral variability, using a linear model with “mantis treatment” as the predictor variable and “structural complexity” as the response variable

We then assessed the effects of mantis density, behavioral variability, and structural complexity on community outcomes using generalized linear mixed models (GLMMs). These models had “mantis treatment”, “structural complexity”, and their interaction term as predictor variables, either total “arthropod biomass” or “change in goldenrod biomass” as response variables, and deployment “day” as a random effect. The residuals of these two models appeared normal by q-q plot and Shapiro-Wilk test (arthropod biomass: W = 0.96, p = 0.09; goldenrod biomass: W = 0.97, p = 0.11). For the purpose of these models, and the ones described below, we removed one outlier with excessively high arthropod biomass (plot number 29; Grubb’s test G = 3.28, U = 0.82, p = 0.02).

Finally, we assessed the effects of mantis density, behavioral variability, and habitat complexity on the structure of prey communities by analyzing the relative biomass of five arthropod functional groups: herbivores, predators, fungivores, non-eaters, and ants (Supplementary Table S2). We used the package vegan in R to fit a Permutational Multivariate Analysis of Variance (PERMANOVA; Anderson 2014) based on the Bray-Curtis dissimilarities of the multivariate data (i.e., relative biomass of prey functional groups). We used the biomass of these categories relative to their representation in total biomass to control for large differences in biomass between plots. This model had “mantis treatment” and “structural complexity”, and their interaction term as predictor variables. Additionally, we performed a series of post-hoc linear mixed models on each functional category to dissect out the PERMANOVA results. These models had “mantis treatment”, “structural complexity”, and their interaction term as predictor variables, and the arthropod biomass categories as response variables. The excess of zeros in the ant data made linear models poor fits. We thus used the number of ants and negative binomial models to assess effects on ants. Ant abundance was closely correlated with ant biomass (Linear regression: F_1,57=385, R² = 0.87, p < 0.01).

To further break down the effects of mantises and habitat complexity we subjected the total biomass, herbivore, fungivore, and ant results to pairwise post-hoc comparisons among treatments and high (≥ 15) and low (< 15) levels of habitat complexity with Holm-Bonferroni multiple comparison corrections (Aickin and Gensler 1996).

Mantises behaved consistently across time in our activity level tests (R = 0.40, 95% CI lower = 0.14, 95% CI upper = 0.62), similar to the overall repeatability average for all animals, 0.37 (Bell et al. 2009). Mantis body-length correlated with mantis activity level, but this relationship was weak (Linear regression: β = 1.17, F_1,416=12.160, R² = 0.03, p = 0.001). Our metric of structural complexity, average number of stems, was significantly repeatable across measurements within plots (R = 0.32, 95% CI lower = 0.19, 95% CI upper = 0.44). Further, structural complexity did not differ across mantis behavioral variability and density treatments (ANOVA: F_4,54=0.279, R² = 0.02, p = 0.891).

Total arthropod biomass depended on mantis density, mantis behavioral trait variability, and habitat complexity (Table 1; Fig. 2). In plots with dense and behaviorally variable mantises, arthropod biomass decreased with increasing habitat complexity, whereas habitat complexity biomass appeared to increase arthropod biomass or have no effect in all other treatments (Fig. 2). Consequently, in more complex (complexity ≥ 15) and dense plots, high mantis variability decreased arthropod biomass by 35.1% Fig. 2; Supplementary table S3). Also in high complexity plots, less dense and variable mantis groups decreased arthropod biomass by 27.3% relative to plots without mantises (Fig. 2). Mantis density appeared to increase arthropod biomass in less complex plots (complexity < 15), though this effect was only marginally significant (Fig. 2). Habitat complexity, on its own, appeared to have no effect on arthropod biomass in these models (Table 1). Deployment day explained about 6.3% of variation in biomass, although this estimate did not differ significantly from 0. Mantis behavioral variability, density, and habitat complexity had no significant effect on Canada goldenrod biomass (Table 1).

Table 1

Linear models predicting total arthropod, plant biomass, and arthropod functional category biomass. Significant p-values are shown in bold.
Response variable	Predictor variables	Chi2	p
Total arthropod biomass
	Mantis variability and density treatment	7.524	0.111
	Habitat complexity	0.192	0.661
	Treatment by habitat complexity	12.765	0.013
Change in goldenrod biomass
	Mantis variability and density treatment	1.991	0.737
	Habitat complexity	0.071	0.79
	Treatment by habitat complexity	1.833	0.766
Herbivore biomass
	Mantis variability and density treatment	12.664	0.013
	Habitat complexity	1.183	0.277
	Treatment by habitat complexity	6.75	0.15
Predator biomass
	Mantis variability and density treatment	3.444	0.487
	Habitat complexity	0.003	0.954
	Treatment by habitat complexity	3.617	0.46
Non-eater biomass
	Mantis variability and density treatment	7.816	0.1
	Habitat complexity	0.066	0.797
	Treatment by habitat complexity	0.226	0.994
Fungivore biomass
	Mantis variability and density treatment	2.318	0.678
	Habitat complexity	0.366	0.545
	Treatment by habitat complexity	11.747	0.019
Ant biomass
	Mantis variability and density treatment	1.131	0.287
	Habitat complexity	0.756	0.385
	Treatment by habitat complexity	5.517	0.019

Of all arthropod biomass, herbivores made up 84.5%, predators 6.5%, ants 6.2%, fungivores 2.6%, and non-eaters 1.3%. This functional composition of arthropod plots depended on the interaction between mantis behavioral variability and habitat complexity. With raw biomass values, this interaction term was only marginally significant (PERMANOVA: F₄ = 1.81, R² = 0.11, p = 0.07), but for relative (i.e., corrected by their representation in total biomass) biomass data, it was significant (PERMANOVA: F₄ = 2.51, R² = 0.15, p = 0.03). To see what drove these effects we performed post-hoc multiple linear mixed regressions on the biomass of each category, and herbivores, fungivores, and ants appear to drive these results. Herbivores had a 28% higher biomass in plots with low variability high density than all other plots with mantises (Supplementary Figure S1, Supplementary Table S3). Fungivore biomass appeared to increase strongly with habitat complexity in high behavioral variability low density plots, but not other treatments (Supplementary Figure S1). Seemingly artifactual significant differences that do not match data may drive this effect on fungivores (Supplementary Figure S1). In less complex plots, behavioral variability increased ant abundance by 420% compared to low variability plots. Finally, among more complex plots ant abundance was 57.8% lower in less variable mantis plots compared to those without mantises.

Theoretical and mesocosm evidence hint that among-individual variability in consumer traits and habitat complexity may determine the degree to which predators regulate their prey (Coblentz et al. 2021; Lichtenstein et al. 2019; Northfield et al. 2017; Pruitt et al. 2017; Schreiber et al. 2011). This possibility had never been tested in naturally occurring, free living communities, leaving it unclear whether intraspecific trait variability can also shape community structure in natural ecosystems. For the first time, we show that predator behavioral variability and density determined the biomass and composition of wild arthropod communities, although plant growth was unaffected (Table 1; Figs. 2 & 3). Further, these effects depended on the habitat complexity where predator-prey interactions took place. High mantis behavioral variability reduced arthropod biomass at high densities in more complex habitats (Fig. 2). This pattern partially confirms our hypotheses that among-individual trait variation increases the effectiveness of predators, especially in complex habitats. However, low variability mantis deployments substantially reduced arthropod biomass at low densities in complex plots (Fig. 2). Further, habitat complexity, mantis variability and density had varied effects on arthropod functional trophic categories. Combined, these results are the first evidence that among-individual trait variability, not just trait averages, can determine how predator density affects prey communities in wild ecosystems with hundreds of interacting species.

In plots with complex habitats and high densities, behaviorally variable mantises decreased arthropod biomass by 35.1%, confirming mesocosm evidence that intraspecific predator trait variability reduces prey survival (Pruitt et al. 2017). Different levels of activity (Harrison et al. 2017; Toscano and Griffen 2014) and other behavioral traits (Chang et al. 2017; Wilson et al. 1993) correspond to hunting strategies in other predator species, so behaviorally variable predators are thought to kill more by employing complementary hunting strategies and thus competing less (Modlmeier et al. 2015). Our results are consistent with this, at least under high levels of mantis density and habitat complexity (Fig. 2). However, under low levels of mantis density and high levels of habitat complexity, low behavioral variability surprisingly decreased arthropod biomass (Fig. 2). This is in line with mesocosm evidence that mantis trait variability can decrease how much they kill (Lichtenstein et al. 2019). It is quite possible that high behavioral variability increases predation by alleviating dispersal and cannibalism (via decreased competition), whereas low variability increases lethality by a lower sampling effect for potentially less lethal extreme phenotypes. Mantis density tends to increase their dispersal and cannibalism (Hurd and Eisenberg 1984b). High densities might thus augment the reduced competition of high variability, whereas the reduced sampling effect of low variability prevails at low densities. Generally, the potential for intraspecific trait variability to reduce predation is poorly understood, and it suggests a more complex role for predator trait variation in predator-prey interactions that goes beyond reducing interference among predators.

Complex habitats were needed for trait variability to alter arthropod biomass, confirming previous experimental evidence that effects of predator individual trait variation are stronger in complex habitats (Lichtenstein et al. 2019). In less complex habitats, mantis density appeared to increase prey density, conflicting with evidence that the presence of Tenodera sinensis decreases arthropod biomass (Hurd and Eisenberg 1990; Moran et al. 1996). Instead, it aligns with growing evidence that predator density will not be directly proportional to prey mortality in multiple-predator systems (Abrams 1993; Sih et al. 1998). This albeit marginally significant effect may have emerged in less complex habitats, because complex habitats can reduce interference among predators and intraguild predation (Finke and Denno 2002; Grabowski et al. 2008). The interplay of functional traits, habitat complexity, and predation are being increasingly discussed (Higham et al. 2021; Schmitz 2017). Habitat complexity is amorphous and tempting to ignore, but this study contributes to mounting evidence that how traits drive predator-predator and predator-prey interactions hinges on variable habitat structure (Kolar et al. 2019; Lichtenstein et al. 2019; Snyder 2019).

For a better view into these processes, we dissected the effects of mantis behavioral variability, density, and habitat complexity by trophic functional category. We found that mantis behavioral variability significantly altered the functional makeup of these arthropod communities. Herbivore biomass was highest at high mantis density and low behavioral variability. These might be the conditions where they hunt, antagonize, and displace each other most. Although we detected a significant treatment by habitat complexity interaction with fungivores, post-hoc tests indicated that this effect could be artifactual (Supplementary Table S3). At most, habitat complexity increased the biomass of these insects at low densities and high levels of variability. Next, in less complex habitats, behavioral variability appears to increase the abundance of ants relative to less variable plots. A possible underlying driver of this pattern is that mantises increase ant biomass by disrupting predators that hunt ants or hampering ants’ ability to antagonize other ant species or colonies of the same species. In more complex plots, less variable mantises decreased ant biomass relative to plots without mantises, perhaps also by decreased sampling of phenotypes less lethal to ants. Overall, the effects of mantis treatments varied substantially across prey functional trophic categories.

Yet, when these results are combined, predator trait variability removes prey in complex habitats and high predator densities. Functional groups show hints of this overall effect. Herbivores show the high biomass of arthropods in low density low variability plots, but not the habitat dependence. Ants show the negative effect of variable mantises at high levels of complexity. Because Tenodera sinensis is an opportunistic predator that consumes most available arthropod taxa from spiders to hemipterans (Hurd and Eisenberg 1984a; Moran et al. 1996), effects of their trait variability on total arthropod biomass must be composites of how they affect many taxa. Conversely, how among-species predator trait variation affects prey are usually measured with one or a few prey species (Schmitz 2007). The seldom considered effects of among-individual predator trait variation thus reveal how little we know about how predator traits affect prey.

We designed this study to cut inroads into the unknown dynamics of how widespread among-individual variation in functional traits shapes ecosystems. Our results serve as a proof-of-concept that among-individual trait variability, not just trait averages, can affect the structure of open relatively intact communities. This confirms years of mesocosm evidence that individuals’ traits affect species interactions (Lichtenstein et al. 2019; Toscano and Griffen 2014; Toscano et al. 2020). How predator density changes prey biomass is a core concept of population modeling, and the effects of functional trait averages have already been woven into modeling approaches (Emmerson and Raffaelli 2004; Gravel et al. 2016; Tirok and Gaedke 2010). The effects of among-individual trait variability have scarcely been considered in population and community models (Coblentz et al. 2021; Northfield et al. 2017; Schreiber et al. 2011), and our findings suggest that these models could grow more accurate by including among-individual variation. Further, both trait and environmental heterogeneity were required to predict the effects of either one. Because these forms of variation are so widespread (Bolnick et al. 2011; Kovalenko et al. 2012), effects of among-individual trait variability could be context dependent and yet prevalent across environments. Ecology has gained incredible insight from only trait categories and averages, but this has left the usefulness of within-species variation largely untapped. Our study is just one empirical step into a wider perspective.

Declaration of Authorship:

JLLL conceived the ideas and designed methodology; JLLL, BLM, SDP, TL, and ZMW collected the data; JLL and RCP analyzed the data; JLLL led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.

Acknowledgements:

Funding for this research came for the most part from the McKinley Foundation of the University of Pittsburgh, the University of California (Santa Barbara). For additional funding, we thank Jonathan Pruitt for his support through the National Science Foundation grant awards (1352705 and 1455895), and a National Institutes of Health grant (R01GM115509). We further thank the Pymatuning Laboratory of Ecology of the University of Pittsburgh, particularly Dr. Cori Zawacki, Chris Davis, Jessica Barabas, Nick Mihailoff, and Glenn Robinson for hosting our research and assisting with plot creation.

Compliance with Ethical Standards:

All animals were captured, handled, and cared for in compliance with journal and institutional ethical standards. All possible steps were taken to minimize animal harm, and mantises from the behavioral repeatability experiment were released roughly 0.5km from their collection site.

Data Availability:

All data are included with the manuscript as a supplemental file.

Conflict of Interest:

We have no conflict of interest to declare.

Abrams PA (1993) Why predation rate should not be proportional to predator density. Ecology 74:726-733
Aickin M, Gensler H (1996) Adjusting for multiple testing when reporting research results: the Bonferroni vs Holm methods. American journal of public health 86:726-728
Akaike H (1987) Factor analysis and AIC. Psychometrika 52:317-332
Anderson MJ (2014) Permutational multivariate analysis of variance (PERMANOVA). Wiley StatsRef: Statistics Reference Online:1-15
Attrill MJ, Strong JA, Rowden AA (2000) Are macroinvertebrate communities influenced by seagrass structural complexity? Ecography 23:114-121
Bell A (2005) Behavioural differences between individuals and two populations of stickleback (Gasterosteus aculeatus). Journal of Evolutionary Biology 18:464-473
Bell AM, Hankison SJ, Laskowski KL (2009) The repeatability of behaviour: a meta-analysis. Animal Behaviour 77:771-783. doi: 10.1016/j.anbehav.2008.12.022
Bolnick DI et al. (2011) Why intraspecific trait variation matters in community ecology. Trends in ecology & evolution 26:183-192. doi: 10.1016/j.tree.2011.01.009
Bolnick DI et al. (2003) The ecology of individuals: incidence and implications of individual specialization. The American Naturalist 161:1-28. doi: doi:10.1086/343878
Burnham KP, Anderson DR (2003) Model selection and multimodel inference: a practical information-theoretic approach. Springer Science & Business Media
Chang C-c, Teo HY, Norma-Rashid Y, Li D (2017) Predator personality and prey behavioural predictability jointly determine foraging performance. Scientific Reports 7. doi: 10.1038/srep40734
Coblentz KE, Merhoff S, Novak M (2021) Quantifying the effects of intraspecific variation on predator feeding rates through nonlinear averaging. Functional Ecology 35:1560-1571. doi: https://doi.org/10.1111/1365-2435.13802
Cook W, Streams F (1984) Fish predation on Notonecta (Hemiptera): relationship between prey risk and habitat utilization. Oecologia 64:177-183
Costa‐Pereira R, Rudolf VH, Souza FL, Araújo MS (2018) Drivers of individual niche variation in coexisting species. Journal of Animal Ecology 87:1452-1464
Crowder LB, Cooper WE (1982) Habitat structural complexity and the interaction between bluegills and their prey. Ecology 63:1802-1813
Crutsinger GM, Collins MD, Fordyce JA, Gompert Z, Nice CC, Sanders NJ (2006) Plant genotypic diversity predicts community structure and governs an ecosystem process. Science 313:966-968. doi: 10.1126/science.1128326
Diehl S (1992) Fish predation and benthic community structure: the role of omnivory and habitat complexity. Ecology 73:1646-1661
Emmerson MC, Raffaelli D (2004) Predator–prey body size, interaction strength and the stability of a real food web. Journal of Animal Ecology 73:399-409
Finke DL, Denno RF (2002) Intraguild predation diminished in complex‐structured vegetation: implications for prey suppression. Ecology 83:643-652
Ganihar S (1997) Biomass estimates of terrestrial arthropods based on body length. Journal of Biosciences 22:219-224
Grabowski JH, Hughes AR, Kimbro DL (2008) Habitat complexity influences cascading effects of multiple predators. Ecology 89:3413-3422. doi: https://doi.org/10.1890/07-1057.1
Gravel D, Albouy C, Thuiller W (2016) The meaning of functional trait composition of food webs for ecosystem functioning. Philosophical Transactions of the Royal Society B: Biological Sciences 371:20150268
Greenop A, Woodcock BA, Wilby A, Cook SM, Pywell RF (2018) Functional diversity positively affects prey suppression by invertebrate predators: a meta‐analysis. Ecology 99:1771-1782
Griffen BD, Toscano BJ, Gatto J (2012) The role of individual behavior type in mediating indirect interactions. Ecology 93:1935-1943
Harmon LJ, Matthews B, Des Roches S, Chase JM, Shurin JB, Schluter D (2009) Evolutionary diversification in stickleback affects ecosystem functioning. Nature 458:1167
Harrison P et al. (2017) Individual isotopic specializations predict subsequent inter‐individual variation in movement in a freshwater fish. Ecology 98:608-615
Heck Jr KL, Wetstone GS (1977) Habitat complexity and invertebrate species richness and abundance in tropical seagrass meadows. Journal of Biogeography:135-142
Higham TE et al. (2021) Linking ecomechanical models and functional traits to understand phenotypic diversity. Trends in Ecology & Evolution 36:860-873. doi: https://doi.org/10.1016/j.tree.2021.05.009
Huey RB, Pianka ER (1981) Ecological consequences of foraging mode. Ecology 62:991-999. doi: 10.2307/1936998
Hurd L, Eisenberg R (1984a) Experimental density manipulations of the predator Tenodera sinensis (Orthoptera: Mantidae) in an old-field community. II. The influence of mantids on arthropod community structure. The Journal of Animal Ecology 53:955-967
Hurd L, Eisenberg RM (1984b) Experimental density manipulations of the predator Tenodera sinensis (Orthoptera: Mantidae) in an old-field community. I. Mortality, development and dispersal of juvenile mantids. The Journal of Animal Ecology 53:269-281. doi: https://doi.org/10.2307/4356
Hurd L, Eisenberg RM (1990) Arthropod community responses to manipulation of a bitrophic predator guild. Ecology 71:2107-2114
Johnson JC, Sih A (2005) Precopulatory sexual cannibalism in fishing spiders (Dolomedes triton): a role for behavioral syndromes. Behavioral Ecology and Sociobiology 58:390-396. doi: http://www.jstor.org/stable/25063630
Kolar V, Boukal DS, Sentis A (2019) Predation risk and habitat complexity modify intermediate predator feeding rates and energetic efficiencies in a tri‐trophic system. Freshwater Biology 64:1480-1491
Kovalenko KE, Thomaz SM, Warfe DM (2012) Habitat complexity: approaches and future directions. Hydrobiologia 685:1-17. doi: 10.1007/s10750-011-0974-z
Laskowski KL, Alirangues Nuñez MM, Hilt S, Gessner MO, Mehner T (2022) Predator group composition indirectly influences food web dynamics through predator growth rates. The American Naturalist 199:000-000
Lichtenstein JL et al. (2019) Habitat structure changes the relationships between predator behavior, prey behavior, and prey survival rates. Oecologia:1-12
Lichtenstein JL, Wright CM, McEwen B, Pinter-Wollman N, Pruitt JN (2017) The multidimensional behavioural hypervolumes of two interacting species predict their space use and survival. Animal Behaviour 132:129-136. doi: 10.1016/j.anbehav.2017.08.01
McElhinny C, Gibbons P, Brack C, Bauhus J (2005) Forest and woodland stand structural complexity: its definition and measurement. Forest Ecology and Management 218:1-24
Michelangeli M, Chapple DG, Goulet CT, Bertram MG, Wong B (2018) Behavioral syndromes vary among geographically distinct populations in a reptile. Behavioral Ecology
Modlmeier AP, Keiser CN, Wright CM, Lichtenstein JL, Pruitt JN (2015) Integrating animal personality into insect population and community ecology. Current Opinion in Insect Science 9:77-85
Moran MD, Hurd L (1997) A trophic cascade in a diverse arthropod community caused by a generalist arthropod predator. Oecologia 113:126-132
Moran MD, Rooney TP, Hurd L (1996) Top‐down cascade from a bitrophic predator in an old‐field community. Ecology 77:2219-2227
Nooten S, Schultheiss P, Rowe RC, Facey SL, Cook JM (2019) Habitat complexity affects functional traits and diversity of ant assemblages in urban green spaces (Hymenoptera: Formicidae). Myrmecological News 29
Northfield TD, Barton BT, Schmitz OJ (2017) A spatial theory for emergent multiple predator–prey interactions in food webs. Ecology and Evolution 7:6935-6948
Pintor LM, Byers JE (2015) Individual variation in predator behavior and demographics affects consumption of non-native prey. Behavioral Ecology 26:797-804. doi: 10.1093/beheco/arv013
Post DM, Palkovacs EP, Schielke EG, Dodson SI (2008) Intraspecific variation in a predator affects community structure and cascading trophic interactions. Ecology 89:2019-2032. doi: 10.1890/07-1216.1
Preisser EL, Orrock JL, Schmitz OJ (2007) Predator hunting mode and habitat domain alter nonconsumptive effects in predator–prey interactions. Ecology 88:2744-2751
Pretorius JD, Lichtenstein JLL, Eliason EJ, Stier AC, Pruitt JN (2019) Predator-induced selection on urchin activity level depends on urchin body size. Ethology 125:716-723. doi: 10.1111/eth.12924
Pruitt JN et al. (2017) Behavioral Hypervolumes of Predator Groups and Predator-Predator Interactions Shape Prey Survival Rates and Selection on Prey Behavior. The American Naturalist 189:254-266
Rogers LE, Hinds W, Buschbom RL (1976) A general weight vs. length relationship for insects. Annals of the Entomological Society of America 69:387-389
Savino JF, Stein RA (1989) Behavior of fish predators and their prey: habitat choice between open water and dense vegetation. Environmental Biology of Fishes 24:287-293
Schmitz O (2017) Predator and prey functional traits: understanding the adaptive machinery driving predator–prey interactions. F1000Research 6
Schmitz OJ (2007) Predator diversity and trophic interactions. Ecology 88:2415-2426
Schmitz OJ (2008) Effects of predator hunting mode on grassland ecosystem function. Science 319:952-954
Schreiber SJ, Bürger R, Bolnick DI (2011) The community effects of phenotypic and genetic variation within a predator population. Ecology 92:1582-1593
Sih A, Englund G, Wooster D (1998) Emergent impacts of multiple predators on prey. Trends in Ecology & Evolution 13:350-355. doi: https://doi.org/10.1016/S0169-5347(98)01437-2
Sih A, Watters JV (2005) The mix matters: behavioural types and group dynamics in water striders. Behaviour 142:1417-1431. doi: https://doi.org/10.1163/156853905774539454
Skelly DK (2002) Experimental venue and estimation of interaction strength. Ecology 83:2097-2101
Smith BR, Blumstein DT (2008) Fitness consequences of personality: a meta-analysis. Behavioral Ecology 19:448-455
Smith JA, Donadio E, Pauli JN, Sheriff MJ, Bidder OR, Middleton AD (2019) Habitat complexity mediates the predator–prey space race. Ecology 100:e02724. doi: https://doi.org/10.1002/ecy.2724
Snyder WE (2019) Give predators a complement: Conserving natural enemy biodiversity to improve biocontrol. Biological Control 135:73-82. doi: https://doi.org/10.1016/j.biocontrol.2019.04.017
Stachowicz JJ, Best RJ, Bracken ME, Graham MH (2008) Complementarity in marine biodiversity manipulations: reconciling divergent evidence from field and mesocosm experiments. Proceedings of the national Academy of Sciences 105:18842-18847
Stewart AJ, Wright AF (1995) A new inexpensive suction apparatus for sampling arthropods in grassland. Ecological Entomology 20:98-102
Stoffel MA, Nakagawa S, Schielzeth H (2017) rptR: repeatability estimation and variance decomposition by generalized linear mixed‐effects models. Methods in Ecology and Evolution 8:1639-1644. doi: 10.1111/2041-210X.12797
Stump SM, Song C, Saavedra S, Levine JM, Vasseur DA (2022) Synthesizing the effects of individual‐level variation on coexistence. Ecological Monographs 92:e01493
Tinker M, Costa D, Estes J, Wieringa N (2007) Individual dietary specialization and dive behaviour in the California sea otter: Using archival time–depth data to detect alternative foraging strategies. Deep Sea Research Part II: Topical Studies in Oceanography 54:330-342
Tirok K, Gaedke U (2010) Internally driven alternation of functional traits in a multispecies predator–prey system. Ecology 91:1748-1762
Toscano BJ, Gownaris NJ, Heerhartz SM, Monaco CJ (2016) Personality, foraging behavior and specialization: integrating behavioral and food web ecology at the individual level. Oecologia 182:55-69
Toscano BJ, Griffen BD (2013) Predator size interacts with habitat structure to determine the allometric scaling of the functional response. Oikos 122:454-462
Toscano BJ, Griffen BD (2014) Trait‐mediated functional responses: predator behavioural type mediates prey consumption. Journal of Animal Ecology 83:1469-1477
Toscano BJ, Lichtenstein JLL, Costa-Pereira R (2020) Intraspecific Behavioral Variation Mediates Insect Prey Survival via Direct and Indirect Effects. Diversity 12:152. doi: https://doi.org/10.3390/d12040152
Uetz G (1991) Habitat structure and spider foraging. Habitat structure. Springer, pp 325-348
van Dongen WF, Maldonado K, Sabat P, Vásquez RA (2010) Geographic variation in the repeatability of a personality trait. Behavioral Ecology:arq145
Van Valen L (1965) Morphological variation and width of ecological niche. The American Naturalist 99:377-390
Vince S, Valiela I, Backus N, Teal J (1976) Predation by the salt marsh killifish Fundulus heteroclitus (L.) in relation to prey size and habitat structure: consequences for prey distribution and abundance. Journal of Experimental Marine Biology and Ecology 23:255-266
Violle C et al. (2012) The return of the variance: intraspecific variability in community ecology. Trends in Ecology & Evolution 27:244-252
Wilson AD, McLaughlin RL (2007) Behavioural syndromes in brook charr, Salvelinus fontinalis: prey-search in the field corresponds with space use in novel laboratory situations. Animal Behaviour 74:689-698
Wilson DS (1998) Adaptive individual differences within single populations. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 353:199-205
Wilson DS, Coleman K, Clark AB, Biederman L (1993) Shy-bold continuum in pumpkinseed sunfish (Lepomis gibbosus): An ecological study of a psychological trait. Journal of Comparative Psychology 107:250. doi: 10.1037/0735-7036.107.3.250
Wolf M, Weissing FJ (2012) Animal personalities: consequences for ecology and evolution. Trends in Ecology & Evolution 27:452-461. doi: 10.1016/j.tree.2012.05.001
Zhao C et al. (2014) Effects of covering behavior and exposure to a predatory crab Charybdis japonica on survival and HSP70 expression of juvenile sea urchins Strongylocentrotus intermedius. PLoS One 9:e97840. doi: 10.1371/journal.pone.0097840

Download PDF

Reviewers agreed at journal
06 Aug, 2023
Reviewers invited by journal
06 Aug, 2023
Editor assigned by journal
08 Jul, 2023
First submitted to journal
07 Jul, 2023

You are reading this latest preprint version

Top-down effects of intraspecific predator behavioral variation

Status:

Version 1

Abstract

Figures

Introduction

Materials & Methods

Study species and site:

Behavioral tests and habitat complexity estimates:

Plot construction:

Plot deconstruction and measurements:

Statistical analyses:

Results

Discussion

Declarations

References

Supplementary Files

Status:

Version 1