DOI: 10.5061/dryad.429t4
title: Data from: Contrasting complexity of adjacent habitats influences the strength of cascading predatory effects

description: 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.
description: Data from tethering
 experiment to measure in-situ blue crab mortality as a function of
 structureData 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.Tether
 data_Dyrad-Byers-Bonnethead.xlsxSummary 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.Mesocosm
 data-Dryad-Byers.xlsx