DOI: 10.5061/dryad.3j9kd51kx
title: Data from: Anderson lab experiments from synthesizing the effects of spatial network structure on predator prey dynamics

description: Predator-prey persistence is thought to be enhanced by spatial
 heterogeneity. Theory predicts that metacommunity size, spatial
 connectivity, network synchrony, predator identity, and productivity
 influence predator-prey persistence, through a variety of mechanisms such
 as statistical stabilization, colonization-extinction dynamics, and
 trophic interactions. However, comparative tests and synthesis of the
 multiple factors and mechanisms across different spatial networks are
 needed to understand which factors and mechanisms of spatial network
 structure promote predator-prey persistence. To address this gap between
 theory and empirical work, we synthesized data from 22 microcosm
 experiments of protist predator-prey communities differing the
 productivity, connectivity, and size of spatial habitat structure. Prey
 time to extinction was better explained by productivity and spatial
 factors than predator time to extinction. At the local and regional scale,
 metacommunity size and productivity had positive effects on prey
 occupancy, whereas connectivity negatively influenced prey occupancy. For
 predators, metacommunity size and connectivity had positive effects on
 predator occupancy, network synchrony had negative influences, and
 productivity showed a hump-shaped relationship with predator occupancy.
 Further, trophic interactions drove variation in the way species were
 spatially structured, where the strength and direction of predator and
 prey occupancy relationships varied among productivity levels and
 predator-prey combinations. In predator-prey interactions that were
 stronger, prey occupancy showed negative relationship with predator
 occupancy regardless of productivity. However, in predator-prey
 interactions that were weaker, prey occupancy was positively related to
 predator occupancy at low productivity, and this relationship disappeared
 at higher productivity treatments where prey occupancy was high regardless
 of predator occupancy. Predictions from metapopulation theory explained
 predator occupancy, while prey were better explained by trophic dynamics.
 Taken together, these results highlight that spatial network structure has
 a complex, spatially contingent relationship with predator-prey dynamics.
description: We conducted a data synthesis on experimental studies that
 manipulated aspects of spatial network structure (i.e. connectivity and
 metacommunity size) and measured this effect on predator-prey dynamics. We
 focused our data synthesis on protist microcosm experiments that were
 conducted with predator-prey species in spatially connected
 metacommunities that allowed for active dispersal of protozoa (rather than
 passive dispersal via pipetting) and sampled at high temporal frequencies.
 We utilized a suite of previously unpublished experiments through our
 laboratory at UC Riverside (Anderson Lab, unpublished; Green et al. in
 press) and also through publicly available data (Holyoak, 2000; Holyoak
 & Lawler, 1996). A total of 22 experiments representing 16 unique
 spatial network structures were included in this analysis (Figure 1; Table
 1). <strong>Experimental Methods:</strong>
 Full details of methods conducted for the data used in this synthesis can
 be found in the original papers (Holyoak and Lawler 1996, Holyoak 2000,
 Green et al. in press), but we summarize these methods below. We studied
 the predator-prey dynamics of three groups of predators and prey in a
 spatial context: 1) <em>Euplotes eurystomus</em> and
 <em>Tetrahymena pyriformis</em>, 2) <em>Didinium
 nasutum</em> and <em>Colpidium striatum</em>, 3)
 <em>Didinium nasutum</em> and <em>Paramaecium
 aurelia</em>. <em>E. eurystomus</em> is an omnivorous
 ciliated protist that feeds on smaller protozoa species as well as
 bacteria (Naeem & Li, 1998). <em>T. pyriformis</em> is
 a smaller (~50mm) ciliated protist that naturally feeds on bacteria and
 grows approximately logistically in the absence of other ciliates (Doerder
 & Brunk, 2012). <em>Didinium</em> is a voracious
 predator that naturally feeds on both <em>Colpidium</em> and
 <em>Paramaecium</em> in natural systems (Holyoak, 2000;
 Veilleux, 1979). All these protist species naturally co-occur in aquatic
 environments and form trophically structured communities (McGrady-Steed et
 al. 1997). Individual protist microcosm “communities”
 were 175mL or 32mL polypropylene Nalgene bottles that were linked by
 silicon rubber tubing that allows for natural, active movement of
 materials and individuals to bottles they are connected to. Each network
 has its own unique design and connections but were generally attached
 using 11cm tube lengths except when geometric constraints prevented this
 in the ring lattice treatment (Figure 1k). For all experiments, each
 network had four replicates. The protist medium used in experiments varied
 among a higher and lower productivity medium and was used as a factor
 potentially explaining differences in persistence times. Medium was
 composed of 1000mL of water, 0.2g of reptile food, and either 1) 1.28g of
 protozoan pellet (high productivity) or 2) 0.56g of protozoan pellet
 (medium productivity). Twenty-four hours after autoclaving the protist
 medium, three freshwater species of bacteria were inoculated in this
 medium: <em>Bacillus subtillis</em>, <em>Bacillus
 cereus</em>, and <em>Serratia macescens</em>.
 Each Nalgene bottle was filled with either 50mL or 30mL,
 depending on the size of Nalgene bottle used, of protist medium with
 associated bacteria species and one boiled wheat seed, which provides
 ample nutrients to bacteria throughout the experiment. Prey species were
 initially added to the experiment from a stock culture at carrying
 capacity. After two days, predator species were added in low densities
 from a stock culture at carrying capacity to all bottles. Sampling of
 experiments followed standard protist microcosm procedures (Altermatt et
 al., 2015) and consisted of mixing the community, pipetting five to ten μl
 drops (~0.032 mL/drop) of the community, weighing the sample, and counting
 the total number of each species under a microscope. For studies involving
 <em>Didinium</em>, 3mL of the community was sampled for
 predator densities. Sampling of protist microcosms occurred three times
 per week for a period of more than 75 day. Each week, sterile medium
 (without bacteria) was replaced in each microcosm to remove waste buildup,
 replenish carbon supply, and replace sampled medium.
 Due to differences in experimental time among treatments, we cut
 off experiments at day 75 as all experiments ran at a minimum for this
 time length. We controlled for differences in the size of bottles used
 among Holyoak (2000) and Anderson lab (unpublished) experiments, by using
 the number of bottles as a proxy for metacommunity size. Because the total
 number of bottles and total volume were highly correlated, we used total
 number of bottles to represent metacommunity size. For response variables,
 species density accounted for differences in the volume used amongst
 experiments although we did not correct for difference in volumes for
 occupancy as it is a binomial variable.
 <strong>Spatial Metrics: </strong>Connectivity was
 defined as nearest neighbors’ connectivity, where for each focal bottle,
 the average number of connections for directly connected neighbors was
 taken (Gilarranz & Bascompte, 2012; Melián & Bascompte,
 2002). Metacommunity size was determined by the total number of bottles
 per spatial network. Although the volume varied among experimental
 treatments from 50mL to 32mL in local communities, total volume was highly
 correlated with total number of bottles and is more reflective of that
 used in the literature. We also used spectral theory to characterize
 spatial structures, which we call network synchrony. This approach
 utilizes the eigenvalues (λ) and eigenvectors generated from the spatial
 structure matrix as a Laplacian matrix. The eigenratio, or the largest
 eigenvalue to the smallest non-zero eigenvalue , can determine the
 stability of a metacommunity’s synchronized state (Barahona &
 Pecora, 2002; Barrat et al., 2008; Hayes & Anderson, 2018; Yeakel
 et al., 2014).