DOI: 10.5061/dryad.ht76hdrhm
title: Biodiversity influences the effects of oil disturbance on coastal ecosystems

description: Biodiversity can enhance the response of ecosystems to disturbance.
 However, whether diversity can reduce the ecological effect of
 human-induced novel and extreme disturbances is unclear. In April 2010,
 the Deepwater Horizon (DwH) platform exploded, allowing an uncontrolled
 release of crude oil into the northern Gulf of Mexico. Initial surveys
 following the spill found that ecological impacts on coastal ecosystems
 varied greatly across habitat-type and trophic group; however, to date,
 few studies have tested the influence of local biodiversity on these
 responses. We used a meta-analytic approach to synthesize the results of 5
 mesocosm studies that included 10 independent oil experiments and 5
 independent oil + dispersant experiments. We tested whether biodiversity
 increased the resistance and/or resilience of coastal ecosystems to oil
 disturbance, and whether a biodiversity effect depended on the type of
 diversity present (taxonomic or genetic) and/or the response type measured
 (population, community or ecosystem-level). We found that diversity can
 influence the effects of oiling, but the direction and magnitude of this
 diversity effect varied. Diversity reduced the negative impact of oiling
 for within-trophic-level responses, and tended to be stronger for
 taxonomic than genetic diversity. Further, diversity effects were largely
 driven by the presence of highly resistant or quick to recover taxa and
 genotypes, consistent with the insurance hypothesis. However, we found no
 effect of diversity on the response to the combination of oil and
 dispersant exposure. We conclude that areas of low biodiversity may be
 particularly vulnerable to future oil disturbances and provide insight
 into the benefit of incorporating multiple measures of diversity in
 restoration projects and management decisions.
description: This dataset was generated from a data synthesis of 5 mesocosm
 experiment conducted by Alabama Center for Ecological Resilience (ACER)
 researchers that evaluated the effect of biodiversity on oil effects in
 coastal ecosystems. We gathered relevant data from the GRIIDC database
 (https://data.gulfresearchinitiative.org) using the keyword ACER and/or
 obtained experimental data directly from each sub-group. This data set is
 compromised of the elements (i.e., mean, sample size and variance) from
 each experiment required to conduct a formal meta-analysis of these
 results.
description: Two Data Files Included:
 Oileffects_ALL_Final = Oil or oil + dispersant effect size
 calculation data set with meta-data for each experiment, study and
 response variable DiversityEffect_MA_Final = Diversity
 effect size calculation data set with meta-data for each experiment, study
 and response variable   For both datasets, data parameters and units as described below: <b>Study Info</b> Author = first author’s surname Year.conduct = year study was conducted or if published, publication date Study = separate number assigned to each individual study  Exp.no = Letter representing the experiment within a study that has multiple experiments or treatments (corresponds on the Other.treatment.level column) Substudy = combination of study and exp. no Resp.no = response number in row (corresponds with Response type column) within each study Time.no = time when response measured (initial, final, etc.) Rep.id = concatenate of study, Exp.no, Resp.no to give unique identifier  GRIIDC = GRIIDC doi for each experiment <b>Info of Treatment in each row</b> Oil Exposure Trmt = Oil, dispersant or both were exposed to Oil exposure concentration = concentration of oil (or dispersant, dispersant + oil) being tested (if multiple levels tested in experiment). NA if there were not multiple levels tested, and instead the concentration is in the Oil Amount column. Diversity Treatment = monoculture (averaged), max monoculture (best or least affected monoculture in oiling treatment) or polyculture (averaged) Polyculture.Level = number of species or genotypes in polyculture treatment. NA if not a polyculture treatment in Diversity Treatment as no levels existed. Max Mono Treatment = identification (species or genotype) of the max (best) monoculture treatment; best monoculture was recorded as the monoculture (on average) that was least affected by oil exposure. NA if not a max.mono in Diversity Treatment as not applicable. <b>Diversity Info</b> Div.cat = category of diversity manipulated (i.e. taxonomic, genetic or functional) # Poly.treat = # of polyculture levels tested in study Poly.Levels = description of polyculture levels (i.e. 2 species, 4 species, etc.) div.design = additive vs. substitutive  Broad taxa classification = phylum (animal vs plant) of target organisms manipulated More specific classification = class or order of target organisms Species = genus and species of manipulated/target organism Target response species = species that response was measured on <b>Oiling Info </b>(If ? in any oiling info column, this variable was unclear from GRIIDC, and/or publication) # oil.levels = # oil exposure levels manipulated in the study Oil levels = list of oil levels used in study Oil Amount = amount of oil exposed to in study Dispersant = Y or N dispersant treatments included in study dispersant.trmt = Dispersant only, or Dispersant + Oil   Disp. amount = amount of dispersant exposed to in study Oil.type = source of oil Disp.type = source of dispersant Oil.weathered = was oil weathered prior to addition – yes or no oiling.method = pulse or press oiling <b>Experimental Design info </b>(If NA in any column design/other trmt column, means that experiment only had diversity and oiling treatments, and no additional experimental treatments) Other.treatments = additional treatments beside oiling and diversity. Or if multiple types of diversity manipulated within experiment, those are indicated here as well. #treatment.levels = # of levels of other treatments Other.treat.level = Description of other treatment level in the experiment Exp.type = mescosom or microcosm (beakers), & indoor/outdoor Season = season experiment conducted during other.treat.level = other treatment level for response in this row <b>Response Properties</b> Response.type = actual response variable data in the row Response.cat = more specific category of response (i.e. fitness, growth, predation, etc.) Response.level = Population, community or ecosystem level response Response units = actual units of the response variable (i.e. density, proportion survived) Duration = experiment duration (total) If.oil.pulse.timesince = units of time since oil exposure (if pulse exposure) # times Sample = # of dependent replicates sampled throughout the experiment duration Time.response = time point of response measured in row <b>Response to oiling</b> Mean.oiling = mean response to oiling treatment Sd.oiling = standard deviation of response to oiling treatment n.oiling = sample size in oiling treatment Mean.no.oiling = mean response to no oiling treatment Sd.no.oiling = standard deviation of response to no oiling treatment n.no.oiling = sample size in no oiling treatment <b>Meta-analysis Response</b> LRR (log response ratio to oiling) <b>= </b>LN((Mean.oiling)/( Mean.no.oiling)) Var LRR (variance of log response ratio) =〖 Sd.oiling〗^2/((n.oiling)*〖Mean.oiling〗^2 )+〖 Sd.no.oiling〗^2/((n.no.oiling)*〖Mean.no.oiling〗^2 ) Hedge d (effect size): d<sup>2</sup><sup> </sup>= (Mean.oiling-Mean.no.oiling )/√(((n.oiling-1) 〖 Sd.oiling〗^2+ (n.no.oiling-1) 〖 Sd.no.oiling〗^2 )/(n.oiling+n.no.oiling-2 )) x J J is a correction factor for small sample sizes (J = 1- 3/(4 (n.oiling+n.no.oiling-2 )-1 ) ) Var Hd (variance of Hedge’s d effect size) =  Notes = any other information about how calculated means, etc. for the individual response variable