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Data from: Designing eco-evolutionary experiments for restoration projects: Opportunities and constraints revealed during Stickleback introductions

Cite this dataset

Haines, Grant; Derry, Alison; Hendry, Andrew (2024). Data from: Designing eco-evolutionary experiments for restoration projects: Opportunities and constraints revealed during Stickleback introductions [Dataset]. Dryad.


Eco-evolutionary experiments are typically conducted in semi-unnatural controlled settings, such as mesocosms; yet inferences about how evolution and ecology interact in the real world would surely benefit from experiments in natural uncontrolled settings. Opportunities for such experiments are rare but do arise in the context of restoration ecology – where different “types” of a given species can be introduced into different “replicate” locations. Designing such experiments requires wrestling with consequential questions. Q1. Which specific “types” of a focal species should be introduced to the restoration location? Q2. How many sources of each type should be used – and should they be mixed together? Q3. Which specific source populations should be used? Q4. Which type or population(s) should be introduced into which restoration sites? We recently grappled with these questions when designing an eco-evolutionary experiment with threespine stickleback (Gasterosteus aculeatus) introduced into nine small lakes and ponds on the Kenai Peninsula in Alaska that required restoration. After considering the options at length, we decided to use benthic versus limnetic ecotypes (Q1) from a mixture of four source populations of each ecotype (Q2) selected based on trophic morphology (Q3), and introduced into restoration lakes in a paired design (Q4). We hope that the present paper outlining the alternatives and resulting choices will provide the rationales clear for future studies leveraging our experiment, while also proving useful for investigators considering similar experiments in the future.

README: Designing Eco-Evolutionary Experiments for Restoration Projects: Opportunities and Constraints Revealed During Stickleback Introductions. [threespine stickleback phenotype and site data]

Description of the data and file structure

Data includes three CSV files: the first two include individual phenotype data of threespine stickleback fish, and the third includes environmental data describing the lakes at the study sites, which was only used for the geographic coordinates to make the map.

  • "AKStickleData - Sheet1_5-19-19.csv"  FishID - ID number unique to each fish Lake - Name of lake where fish was captured Mass_g - Wet mass of fish after preservation in EtOH (g) Ectoparasites_YN - presence of obvious external parasites (not used in publication) SL_mm - standard length: from tip of snout to base of posterior-most caudal fin ray (mm) BodyDepth - depth of fish as measured from base of first dorsal spine (mm) BuccalCavityLength - Length between tip of dentary and anterior tip of ectocoracoid (mm) Sex*_MFU - Sex as determined by dissection and inspection of gonads (not used in publication) ProcessedBy - Name of processor of SL, body depth, buccal cavity length, and sex GapeWidth - Width between widest point of lateral processes of premaxilla (mm) EpWidth - Width between widest point of pterotics (mm) ProcessedBy - Name of processor of gape width, pterotic width, and raker count RakerNum *- Count of gill rakers on first raker on the right side
  • "AKStickleData _7-30-19.csv" Includes same columns as above, plus the following measured using ImageJ: Caudal peduncle - depth of caudal peduncle at narrowest point (cm) Jaw Length - length of upper jaw from anterior tip of premaxilla to posterior tip of maxilla (cm) Snout Length - distance between anterior tip of premaxilla to anterior edge of eye (cm) Eye Diameter - distance between anterior and posterior edges of eye (cm) Head Length - distance between anterior tip of premaxilla and posterior edge of operculum (cm) Scale - scaling factor used to convert ImageJ measurements (measured in pixels) to cm Also contains columns for ImageJ-measured traits with color codes. These have direct measurements from images with units in pixels
  • "AK_ENV_lat-long.csv" Contains columns for Lake, Region, and LATITUDE and LONGITUDE (both in decimal degree format)

Missing data code: NA

Sharing/Access information

A later version of the data in this repository can also be found here, in the repository associated with Haines et al. (2023, ), and Haines et al. (2024, ):


Code consists of an R script including allometric adjustments of traits, linear descriminant analyses as described in the article text, and several plots of the data. The script also includes the code used to make a map depicting the locations of the study sites in the Kenai Peninsula and Mat-Su Valley, Alaska.


Canada Research Chairs, Tier 1

Natural Sciences and Engineering Research Council, Award: RGPIN-2018-04761, Discovery Grant

Natural Sciences and Engineering Research Council, Award: RGPIN-2022-03706), Discovery Grant