Computational protocol: Repeated Lake-Stream Divergence in Stickleback Life History within a Central European Lake Basin

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Protocol publication

[…] Our prime interest was to investigate lake-stream divergence in age and size at reproduction. To quantify age at reproduction, we retrieved the left and right sagittal otolith from all specimens in each lake-stream pair. The otoliths were cleaned mechanically using fine forceps, dried, mounted in 20 µl Euparal on a microscope slide, and inspected under a stereomicroscope at 50x magnification by a single person (DM) blind to the specimens’ origin. Illumination was from above on a black background to optimally visualize the opaque and transparent ring zones used for age determination following (representative otoliths from different age classes are shown in ). Left and right otoliths always produced consistent results. A total of 4 specimens (<2% of all specimens investigated) displayed unclear otolith ring patterns and could thus not be aged unambiguously. Excluding these specimens from analysis did not affect any conclusions; hence we present results based on the full data set. Differences in age composition between lake and stream fish were tested separately for each system through non-parametric permutation tests randomizing the response variable (age) 9999 times over the predictor (habitat) , and using the lake-stream difference in average age as test statistic. All statistical inference in this study is based on analogous permutation tests.To quantify body size at reproduction, we digitized 16 homologous landmarks on the photograph of each specimen by using TpsDig . TpsRelw was then used to calculate centroid size from the landmark configurations. This size metric, hereafter referred to as ‘body size’, was considered more robust to variation in overall body shape and feeding or reproductive status than size metrics such as standard length or linearized body mass. (Using the latter as body size metric, however, produced very similar results in all analyses.) To test for lake-stream divergence in body size, we used the difference in average size between the habitats as test statistic.In addition to age and size at reproduction, we investigated divergence in fecundity and egg size. For this, clutches of gravid females ready for spawning were collected in the field by gently squeezing the females’ abdomen, and preserved in ethanol. We then counted the total number of eggs (fecundity) under a stereomicroscope, dried all eggs at 50°C for 48 h, and determined their total dry mass. Egg size was then expressed as the total clutch dry mass divided by total egg number (i.e., the average dry mass of a single egg). This investigation used mainly females collected in 2012 for this specific purpose only (and hence not included in ; lake: COE, COW, N = 11 each; stream: COW, CON, COE, N = 9, 1, 1), but additionally involved a few females also used for the other analyses (details given in ). Testing for lake-stream divergence in fecundity and egg size was then performed in a single analysis for each trait by pooling data across the two lake sites and the three stream sites. (Restricting the analysis to the COW system with sufficient data from each habitat produced similar results.) As above, the difference in trait means between the habitats was used as test statistic. [...] The major goal of our genetic investigation based on nuclear and mitochondrial markers was to quantify population structure within and among the replicate lake-stream systems in the LC basin. Of particular interest was the detection of strong genetic divergence within lake-stream systems, suggesting effective habitat-related barriers to gene flow. An additional goal was to explore the relationship between stickleback in the LC basin and fish from nearby water bodies. The present work greatly extends a previous population genetic study partly involving fish from the LC basin in that new lake-stream pairs are analyzed, samples from the Rhine and Danube are included, and a greater number of genetic markers are used.We first extracted DNA from pectoral and caudal fin tissue on a MagNA Pure LC extraction robot (Roche) by using the Isolation Kit II (tissue). Next, we amplified eight microsatellites with labelled primers in two separate multiplex PCRs by using the QIAGEN multiplex kit and following the manufacturer’s protocol. All PCRs included a negative control to check for contamination. The microsatellite markers were chosen to be far from known quantitative trait loci in stickleback, and to lie on different chromosomes. They included the markers Stn67, Stn159, Stn171, and Stn195 used previously , , and additionally Stn28, Stn99, Stn119, and Stn200 . For the latter, we designed our own primer pairs (primer sequences for all eight markers are provided in ). PCR products were run on an ABI3130xl sequencer (Applied Biosystems), and alleles scored manually in PeakScanner v1.0. Input files for the different population genetic programs were prepared by using CREATE .The microsatellite data were first used to estimate differentiation among all 11 samples by Weir & Cockerham’s FST calculated with GENETIX v4.0.5.2 (P-values based on 999 permutations). To account for variation in heterozygosity within populations , we also calculated standardized FST after data transformation with RECODEDATA v0.1 . Next, we tested whether neighboring lake and stream samples qualified as genetically distinct populations by performing a genetic clustering analysis using STRUCTURE (v2.3.1; , ) separately in each lake-stream pair (note that the COS system represents two pairs, both involving the same lake sample). The assumed number of populations (K) ranged from one to three, with each level replicated five times under the admixture and independent allele model with 100’000 iterations (20′000 iterations burnin). An additional analysis examined population structure among the 11 pooled samples, using K = 1–12. STRUCTURE results were combined using Structure Harvester v.0.6.92 , and interpreted following , . The microsatellite data set is provided in Table S3.The above analyses using rapidly evolving microsatellites were complemented by a more coarse-grained investigation of genetic relationships based on single nucleotide polymorphisms (SNPs) within a 305 bp segment of the mitochondrial D-loop. Sample size was 18–32 individuals per site, 256 in total. Primers and PCR amplification conditions were as in . Products were sequenced on an ABI3130xl sequencer (Applied Biosystems). We used jModelTest v0.1.1 to determine the most appropriate model of sequence evolution (‘F81’; ), identified the most probable genealogical relationship by the maximum-likelihood method implemented in PAUP* v4.0 , and generated a haplotype genealogy for visualization following . All D-loop sequences are deposited in GenBank (accession numbers JX436521-JX436776). […]

Pipeline specifications

Software tools Structure Harvester, jModelTest
Applications Phylogenetics, Population genetic analysis
Organisms Hemisus marmoratus, Danio rerio