Biodiversity influences the effects of oil disturbance on coastal ecosystems
Cite this dataset
Zerebecki, Robyn; Heck, Kenneth; Valentine, John (2023). Biodiversity influences the effects of oil disturbance on coastal ecosystems [Dataset]. Dryad. https://doi.org/10.5061/dryad.ht76hdrhm
Abstract
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.
Methods
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.
Usage notes
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