Data from: Contrasting complexity of adjacent habitats influences the strength of cascading predatory effects
Byers, James E., University of Georgia
Holmes, Zachary C., University of Georgia
Malek, Jennafer C., University of Georgia
Published Aug 11, 2018 on Dryad.
Cite this dataset
Byers, James E.; Holmes, Zachary C.; Malek, Jennafer C. (2018). Data from: Contrasting complexity of adjacent habitats influences the strength of cascading predatory effects [Dataset]. Dryad. https://doi.org/10.5061/dryad.429t4
Although cascading effects of top predators can help structure communities, their influence may vary across habitats that differentially protect prey. Therefore, to understand how and to what degree habitat complexity can affect trophic interactions in adjacent habitats, we used a combination of a broad regional-scale survey, manipulative field trials, and an outdoor mesocosm experiment to quantify predator–prey interaction strengths across four trophic levels. Within estuaries of the southeastern USA, bonnethead sharks (Sphyrna tiburo) hunt blue crabs on mudflats and adjacent oyster reefs, two habitats with vastly different aboveground structure. Using 12-h tethering trials of blue crabs we quantified habitat-dependent loss rates of 37% on reefs and 78% on mudflats. We hypothesized that the sharks’ predatory effects on blue crabs would cascade down to release a lower-level mud crab predator, which subsequently would increase juvenile oyster mortality, but that the cascade strength would be habitat-dependent. We experimentally manipulated predator combinations in split-plot mesocosms containing reef and mudflat habitats, and quantified oyster mortality. Bonnetheads exerted strong consumptive and non-consumptive effects on blue crabs, which ceased eating oysters in the sharks’ presence. However, mud crabs, regardless of shark and blue crab presence, continued to consume oysters, especially within the structural refuge of the reef where they kept oyster mortality high. Thus, bonnetheads indirectly boosted oyster survival, but only on the mudflat where mud crabs were less active. Our work demonstrates how structural differences in adjacent habitats can moderate trophic cascades, particularly when mesopredators exhibit differential use of structure and different sensitivities to top predators.
Data from tethering experiment to measure in-situ blue crab mortality as a function of structure
Data collected from field experiment on blue crab tethers in Wassaw Sound, Georgia. Abbreviations of variables are as follows, Site (representing general deployment area): TC= Tybee Creek; SK= Skidaway River; RM = Romerly Marsh); Position: on = on reef; off= off reef; Date: deployment date in field (treated as blocking factor in analysis); CW: Carapace width; CL: Carapace length; Crab Status: A=alive, D=dead; Shark Involved?: P=predation by shark; N=no predation by shark (based on forensics evidence and best guess. All alive crabs were automatically coded as N). To examine the mortality of blue crabs as a function of position (or habitat) , we used a generalized linear mixed effects model to analyze individual crab survival with a binary distribution and a logit link function as influenced by the fixed effect of position (i.e., on reef or off reef). Because the response of crabs within a given deployment batch may not be fully independent, deployment replicate (i.e., date) was used as a random effect.
Summary data on oyster spat survival from split-plot mesocosm experiment to examine predation strengths as function of structure.
Abbreviations of variables are as follows, Treatment: Oyster=Oyster only control; MC = mud crab; BC = blue crab; BH = bonnethead shark; Habitat: on= on oyster reef; off= off reef on sandflat; # Alive: number of oyster spat alive in a habitat at the end of experimental trial; Starting #: number of oyster spat in each habitat at start of experiment. Each tank was divided in half between oyster reef and sandflat habitat, reflecting the study’s split plot design. Hence every tank has two entries in the dataset, one for on reef habitat and one for off-reef (sandflat). Because these data were analyzed at the level of binary responses of individual oysters (live or dead), the data were expanded out to the level of individual oysters (i.e., DO loop in SAS) before analyses with generalized linear mixed effects models to analyze individual oyster spat survival with a binary distribution and logit link function.