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.

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.

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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:

Study Info

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

Info of Treatment in each row

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.

Diversity Info

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

Oiling Info (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

Experimental Design info (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

Response Properties

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

Response to oiling

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

Meta-analysis Response

LRR (log response ratio to oiling) = 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): d² = (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