Long-term studies are essential to understanding the process of evolution and the mechanisms of adaptation. Generally, these studies require commitments beyond the life-time of researchers. Here, a powerful method is described that dramatically advances state-of-the-art data collection to generate longitudinal data in natural systems.
Long-term studies enable the identification of eco-evolutionary processes that occur over extended time periods. In addition, they provide key empirical data that may be used in predictive modelling to forecast evolutionary responses of natural ecosystems to future environmental changes. However, excluding a few exceptional cases, long-term studies are scarce because of logistic difficulties associated with accessing temporal samples. Temporal dynamics are frequently studied in the laboratory or in controlled mesocosm experiments with exceptional studies that reconstruct the evolution of natural populations in the wild.
Here, a standard operating procedure (SOP) is provided to revive or resurrect dormant Daphnia magna, a widespread zooplankton keystone species in aquatic ecosystems, to dramatically advance the state-of-the-art longitudinal data collection in natural systems. The field of Resurrection Ecology was defined in 1999 by Kerfoot and co-workers, even though the first attempts at hatching diapausing zooplankton eggs date back to the late 1980s. Since Kerfoot's seminal paper, the methodology of resurrecting zooplankton species has been increasingly frequently applied, though propagated among laboratories only via direct knowledge transfer. Here, an SOP is described that provides a step-by-step protocol on the practice of resurrecting dormant Daphnia magna eggs.
Two key studies are provided in which the fitness response of resurrected Daphnia magna populations to warming is measured, capitalizing on the ability to study historical and modern populations in the same settings. Finally, the application of next generation sequencing technologies to revived or still dormant stages is discussed. These technologies provide unprecedented power in dissecting the processes and mechanisms of evolution if applied to populations that have experienced changes in selection pressure over time.
Long-term studies are critical to understanding ecological and evolutionary processes in nature and in assessing how species respond to and persist during environmental change1. This is because eco-evolutionary processes happen across generations and changes in the environment occur over long time spans. Furthermore, long-term studies provide key empirical data that improve the accuracy of predictive modeling to forecast evolutionary responses of natural ecosystems to environmental changes2. The accuracy of these models is critical to implement management and conservation strategies to preserve biodiversity and ecosystem services.
Excluding a few exceptional cases (e.g., Galapagos Darwin finches3 and algae4), long-term studies are largely limited to species with short generation time that can be propagated in the laboratory5,6,7,8. Hence, the processes underpinning evolutionary dynamics remain elusive. Because of logistic difficulties associated with accessing temporal samples, empirical data are studied more frequently in a spatial than in a temporal context, and temporal eco-evolutionary processes are inferred or modeled from spatial data. This approach is known as 'space-for-time' substitution9, whereby space is adopted as a surrogate to study temporal evolutionary dynamics. The main limitation of the 'space-for-time' substitution is that rates of adaptation at different spatial scales differ from temporal variation in the same population; hence, inferences based on replacement of time with space are biased10.
A powerful alternative that allows studying evolutionary dynamics in natural ecosystems over time is the analysis of ecological and genetic changes in species producing dormant stages11. These dormant stages accumulate to form stratified biological archives that can be accurately dated and paleolimnologically characterized12,13. Importantly, these dormant stages can be resuscitated and used in laboratory experiments, where their evolutionary response to environmental change can be directly measured. Historical populations can be competed against their modern evolved descendants to study fitness changes and the function of genes evolving in step with environmental change14,15,16.
Dormant stages include seeds, cysts, spores, and egg banks. Although the first studies on resuscitated dormant eggs dates back to the late 1980s17, and a handful of studies have applied this technique in the early 1990s18,19, the field of Resurrection Ecology has been formally established by the seminal paper of Kerfoot and co-workers in 199920. This practice has been applied mainly in paleolimnological reconstructions of freshwater species17,21,22. However, a SOP is not yet available. Here, a step-by-step description of the resurrection protocol applied to dormant eggs of the zooplankton species Daphnia magna is provided, from the sampling of sediment to the establishment of clonal cultures from hatchlings. Steps of the SOP that are readily transferable to other species of Daphnia, as well as steps that may require additional optimization, are discussed.
Daphnia are freshwater zooplankters present in the majority of lotic habitats23 . Daphnia species are either obligate asexual or cyclical parthenogens. D. magna is a cyclical parthenogen that reproduces clonally under favorable environmental conditions24. When environmental conditions deteriorate, male production occurs and sexual recombination leads to the formation of fertilized eggs that enter a state of dormancy protected from the environment by a chitin case called ephippium. A proportion of these dormant eggs hatch when favorable environmental conditions return. However, a large proportion of the dormant egg bank never has a chance to hatch and thus build up biological archives over time. Dormant stages remain buried in the sediment of lakes and ponds and can be resurrected for the study of evolutionary dynamics over extended time periods. Because dormant eggs of D. magna are the result of sexual recombination, they are a good representation of the natural genetic diversity of the species25. Moreover, they can be maintained via clonal reproduction in the laboratory. These characteristics provide the unique advantage of isogenic model organisms, while retaining the natural genetic diversity.
Two key studies are presented to demonstrate the advantages of directly comparing historical and modern descendants of the same population of D. magna experiencing environmental selection pressure over time. D. magna specimens were resurrected from Lake Ring (Denmark), a shallow (5 m depth; surface 22 ha) mixed pond that has experienced an increase in average temperature and heat waves occurrence over time. D. magna (sub)populations were resurrected along this temporal gradient spanning 60 years (1960–2005) and studied to investigate evolutionary response to temperature warming. In the first study in a common garden experiment, changes in fitness-linked life history traits were measured in response to an increase in temperature of +6 °C, in line with the predictions of the Intergovernmental Panel for Climate Change for the upcoming 100 years26. In the second study, a mesocosm experiment was used to measure the competitive abilities of the three (sub)populations under warming. These experiments combined show that in presence of warming as the only stress, all life history traits and populations show a high level of plasticity and have equal competitive abilities. These findings suggest that warming as a single stress does not impose significant fitness costs, at least in the population studied here.
The following SOP provides a step-by-step description of the protocol used to resurrect Daphnia magna dormant eggs, including a detailed description of sampling, isolation of ephippia from the sediment, and establishment of clonal cultures (Figure 1).
Figure 1: Step-by-step guide to resurrection of Daphnia magna. Sediment from a natural freshwater habitat (A) is sampled with a piston corer (B). The sediment core (C) is sliced in incremental layers of 1 or 0.5 cm (D). Each layer of sediment is stored in a sample zip lock bag (E) in dark and cold conditions (4 °C). Each layer of sediment is weighed and sieved using geological sieves (1 mm and 125 µm mesh sizes, F). White background trays are used to isolate Daphnia magna ephippia (G). Decapsulated dormant eggs (H) are transferred to Petri dishes and exposed to light and temperature stimuli to induce hatching. Hatchlings are transferred to separate jars (I) to establish isoclonal lines. Please click here to view a larger version of this figure.
1. Sampling of Sediment Cores
Figure 2: Cartoon of the piston coring procedure. Piston corer, a hollow tube with an internal sliding seal (the piston) that produces a weak vacuum. When the piston touches the sediment-water interface, the weight pushes the core barrel into the sediment and the vacuum causes the sediment being cored to enter and move up the tube without disturbing the sediment layers. Please click here to view a larger version of this figure.
2. Sieving of Sediment Layers
Figure 3: Daphnia magna ephippium. Dormant Daphnia magna eggs immediately after decapsulation. The ephippium (A), the inner egg membrane (B), and the dormant eggs (C) are shown. Scale bar = 500 µm.
3. Decapsulation of Ephippia and Hatching
4. Establishing Isoclonal Lines of Daphnia magna
5. Key Studies
NOTE: A description of two key studies is provided in which resurrected D.magna (sub)populations from the sedimentary archive of Lake Ring (Denmark) are used to assess the evolutionary response to warming. Three (sub)populations were resurrected from the following time periods: 1960-1970, 1970-1985, and >1999. D. magna hatching success from the sedimentary archive ranged between 11 and 58% (Figure 4). From the hatchlings obtained from each timeperiod, a random subset was chosen for the two key studies described here. These studies were designed to assess whether (sub)populations resurrected from different time periods along the temperature gradient showed differences in fitness-linked life history traits (5.1), and whether they had different competitive abilities (5.2) after exposure to warming.
Figure 4: Hatching success in a sedimentary archive sampled from Lake Ring. The proportion of successful hatchlings along the sedimentary archive of Lake Ring used in the key studies. Please click here to view a larger version of this figure.
Figure 5: Adult female Daphnia magna. Adult female Daphnia magna with parthenogenetic eggs in the brood chamber. The distance between the head and the base of the tail spine is used to measure size of the animal. The red lines indicate the size measurements. Scale bar = 500 µm.
Long-term empirical data are critical to the understanding of evolutionary dynamics and persistence of natural populations. Such data are generally challenging to obtain because of logistic difficulties associated with accessing temporal samples and the requirement of committing long-term to data collection. In the two key studies presented here, empirical evidence of the response to temperature of a central zooplankter in freshwater ecosystems is provided over evolutionary times. This is enabled by the use of layered dormant egg banks that provide the opportunity to study the response of historical populations and their modern descendants to environmental stress in common experimental settings.
Common garden experiment
The common garden experiment showed that all life history traits responded to temperature (Figure 6 and Figure 7). The ANOVA analysis revealed that all (sub)populations respond to temperature via plasticity (Table 2), except for mortality, which is unresponsive. Evidence of evolutionary changes (differences among (sub)populations) was observed only in population growth rate (Table 2), which significantly increased in two of the three (sub)populations at 24 °C (Figure 6).
Figure 6: Common garden experiment. Reaction norms for life history traits (fecundity, size, and age at maturity) and population growth rate (r) are shown for each (sub)population under temperature warming (24 °C) as compared to the common garden and current temperature regime (18 °C). The population growth rate 'r' is calculated using the Eulerian equation (1). Confidence intervals are shown. (Sub)populations are color coded: (i) blue: 1960-1970; (ii) green: 1970–1985; (iii) red: >1999. Please click here to view a larger version of this figure.
Figure 7: Mortality. Mortality rates per (sub)population (1960-1970; 1970-1985; >1999) are shown under warming (24 °C) as compared to modern temperature regimes (18 °C). Please click here to view a larger version of this figure.
Mesocosm experiment
After four weeks of selection, represented by warming at 24 °C, the frequency of the three (sub)populations did not change significantly (χ2 = 0.55, P = 0.76) as compared to the initial inoculum (Figure 8). Among the 30 genotypes inoculated in the mesocosm experiment, the majority was identified after four weeks of selection (Figure 9). Specifically, 70% of the inoculated genotypes were recovered, compatible with Poissonian expectation of recovering at least one representative of each genotype in a sample of 32 individuals.
Figure 8: Competition experiment – population frequency. Population-averaged median and quartiles (25th and 75th), is shown for the three (sub)populations of D. magna after four weeks of selection in mesocosm competition experiments (24 °C), as compared to an initial equal frequency (at the start). (Sub)populations are color coded as shown in Figure 6. Please click here to view a larger version of this figure.
Figure 9: Competition experiment – genotype frequency. Genotype frequencies — averaged median and quartiles (25th and 75th), are shown after four weeks of exposure to warming (24 °C) as compared to an initial equal frequency of genotypes (dotted line). Names on the x-axis are the inoculated genotypes ID, grouped per (sub)population (blue, 1960-1970; green, 1970-1985; red, >1999). Please click here to view a larger version of this figure.
Locus | AN | Size range (bp) | Primers (5’-3’) | Dye label | Repeat motif | Tm | |
B008 | HQ234154 | 150–170 | F: TGGGATCACAACGTTACACAA | VIC | (TC)9 | 56 | |
R: GCTGCTCGAGTCCTGAAATC | |||||||
B030 | HQ234160 | 154–172 | F: CCAGCACACAAAGACGAA | PET | (GA)11 | 56 | |
R: ACCATTTCTCTCCCCCAACT | |||||||
B045 | HQ234168 | 118–126 | F: GCTCATCATCCCTCTGCTTC | NED | (TG)8 | 56 | |
R: ATAGTTTCAGCAACGCGTCA | |||||||
B050 | HQ234170 | 234–248 | F: TTTCAAAAATCGCTCCCATC | 6FAM | (GAA)6 | 56 | |
R: TATGGCGTGGAATGTTTCAG | |||||||
B064 | HQ234172 | 135–151 | F: CTCCTTAGCAACCGAATCCA | 6FAM | (TC)8 | 56 | |
R: CAAACGCGTTCGATTAAAGA | |||||||
B074 | HQ234174 | 196–204 | F: TCTTTCAGCGCACAATGAAT | NED | (GT)9 | 56 | |
R: TGTGTTCCTTGTCAACTGTCG | |||||||
B096 | HQ234181 | 234–240 | F: GGATCTGGCAGGAAGTGGTA | VIC | (AC)15 | 56 | |
R: TTGAACCACGTCGAGGATTT | |||||||
B107 | HQ234184 | 250–274 | F: GGGGTGAAGCATCAAAGAAA | PET | (CT)8 | 56 | |
R: TGTGACCAGGATAAGAGAAGAGG |
Table 1: Microsatellite multiplex. The NCBI Accession Number (AN), the multiplex information, the PCR primer sequences, the PCR size range, the repeat motif, the dye used to label the forward primer, and the annealing temperature (Tm) are shown.
Pop Growth rate (r) | Df | F | P | |
Evolution (Pop) | 2 | 30.309 | <0.001 | |
Plasticity (Temp) | 1 | 531.546 | <0.001 | |
Evol. Plasticity (Pop x Temp) | 2 | 65.137 | <0.001 | |
Mortality | Df | F | P | |
Evolution (Pop) | 2 | 2.234 | 0.1162 | |
Plasticity (Temp) | 1 | 2.679 | 0.1071 | |
Evol. Plasticity (Pop x Temp) | 2 | 1.8657 | 0.164 | |
Fecundity | Df | F | P | |
Evolution (Pop) | 2 | 1.8852 | 0.1633 | |
Plasticity (Temp) | 1 | 6.8934 | 0.0117 | |
Evol. Plasticity (Pop x Temp) | 2 | 1.6511 | 0.203 | |
Size at maturity | Df | F | P | |
Evolution (Pop) | 2 | 0.211 | 0.8106 | |
Plasticity (Temp) | 1 | 11.1361 | 0.0017 | |
Evol. Plasticity (Pop x Temp) | 2 | 0.6586 | 0.5225 | |
Age at maturity | Df | F | P | |
Evolution (Pop) | 2 | 0.7811 | 0.4637 | |
Plasticity (Temp) | 1 | 8.0764 | 0.0066 | |
Evol. Plasticity (Pop x Temp) | 2 | 0.088 | 0.9159 |
Table 2: Analysis of variance(ANOVA). Analysis of variance testing whether changes in life history traits and population growth rate of the resurrected (sub)populations exposed to warming are explained by evolutionary adaptation (populations), plasticity (temperature treatment), and their interaction term (evolution of plasticity). Significant p-values (p<0.05) are shown in bold.
Supplementary Video 1: Sampling of sediment cores. The use of a Big Ben corer is shown. Big Ben is a core tube of approximately 1.5 m in length with an internal tube diameter of 14 cm. It consists of a piston on a rope and a corer head, to which rods are attached to drive the tube into the sediment. A core catcher is used to support the core tube that is deployed from a small vessel. The piston is pushed down into the sediment by gravitational pressure. A framework is used to support the core tube during the extrusion process carried out using a modified bottle jack that pushes the piston upwards. Each sediment layer is collected on a flat metal surface and transferred to transparent sampling bags for long term storage [dark and cold (4 °C) conditions]. Please click here to download this file.
Supplementary Video 2: Sediment sieving. The equipment required for sieving sediment is a precision scale, white sampling trays and geological sieves. From each sediment layer, at least 5 g are retained for radiometric dating. The remainder of the sediment is used to isolate ephippia. The sediment is sieved through two geological sieves, one with 1 mm and a second with 125 µm mesh size, piled on top of each other. Medium is poured on the 1 mm mesh sieve to separate clay, large invertebrates, and particulate matter. Medium poured on the second sieve with 125 µm mesh separates D. magna ephippia and small particulate matter. Aliquots of sediment are then transferred to a white sampling tray. D. magna ephippia are spotted by eye in the white background tray. Ephippia from each layer are collected in separate Petri dishes. Please click here to download this file.
Supplementary Video 3: Decapsulation. Under a stereomicroscope, D. magna ephippia are opened with microdissection forceps by applying pressure on the spine of the chitin case. The inner egg membrane is delicately removed and resting eggs gently transferred with a Pasteur pipette to a Petri dish containing 10 mL of medium. Please click here to download this file.
Supplementary Video 4: Hatching. After exposure to a long photoperiod and 20 °C, embryo development resumes between 48 h and few weeks. When development is complete, the embryos break free from the egg shell and freely swim in the medium. Please click here to download this file.
Due to the high thermal conductivity of water, freshwater ecosystems are at higher risk of biodiversity loss than terrestrial ecosystems in the face of global warming34. It is, therefore, critical to understand the response of keystone species in these ecosystems and identify coping mechanisms to survive thermal stress. The understanding of these mechanisms at the species and community levels can help predict how species are impacted by global warming and how the effect on individual species cascades to other trophic levels. Ultimately, understanding mechanisms of responses to global warming enables the identification of remediation strategies to mitigate extinctions.
The case studies presented here show that the response of D. magna to temperature increase is pervasively mediated by plasticity in life history traits and that response to temperature increase alone does not impose clear fitness costs, at least in the population studied here. High plasticity in life history traits is supported by non-significant differences in competing abilities of the (sub)populations in the presence of warming. However, longer-term competition experiments on multiple populations may be necessary to generalize these findings.
Resurrection of dormant stages provides an unprecedented resource to study mechanisms of adaptation and trajectories of a species' evolution through time10. Zooplankton species benefit from a rapid generation time (about 2 weeks), and the viability of dormant stages, which allows an ancestor to compete headtohead against its own descendants, or to 'replay' evolution that starts from various past states. Resurrection ecology essentially enables the investigation of whether a particular evolutionary outcome is contingent on some prior event. The identification of the genetic elements of evolution is currently possible in laboratory experiments using microorganisms for which 'ancestral lines' are frozen and resuscitated for comparative analysis with their evolved descendant6. However, one of the main limitations of working with laboratory organisms is that the 'ancestral state' is an already shifted baseline. The study of dormant stages allows the sampling of specimens from time predating any stress event (e.g., pristine environmental conditions) and to measure evolutionary trajectories from undisturbed environmental conditions to various past states until modern times. In recent years, the study of DNA polymorphism in resurrected or still dormant zooplankton stages has provided important insights into past demographic and adaptive processes that have contributed to the genetic makeup of present-day populations14,16,25,33,35,36. With the higher accessibility of high throughput sequencing technologies, the genome and transcriptome of resurrected or still dormant stages can be sequenced and the type and number of genetic changes accumulated in evolving populations over time measured.
The resurrection SOP presented here has important applications in the field of multi-omics on two levels. Multi-omics technologies can be applied to resurrected specimens, allowing an exhaustive analysis of the molecular elements involved in adaptive responses to environmental selection pressure. In addition, omics technologies can be applied to decapsulated but still dormant stages. So far, the application of high throughput sequencing technologies to resting stages has been limited by the requirement of a large amount of input material. These limitations are being lifted37. With the lowering requirements for input material and progress in nanofluidics, whole genome sequencing (WGS) is now possible from as little as 1 ng or a few pg of starting material38. The use of whole genome amplification (WGA) and whole transcriptome amplification (WTA) techniques, enabling the enrichment of DNA and RNA from very small amounts of tissue, has revolutionized both metagenomics39,40 and medical research41. These technologies applied to decapsulated dormant eggs enable the exceeding of limitations associated with viability of dormant stages and the investigating of extended time periods (e.g., centuries).
The resurrection of invertebrate communities producing resting stages enables the alignment of community histories with known changes in the natural landscapes, or with environmental changes inferred from analyses of the sediments orsoils2. The analysis of community changes in response to environmental change provides us with the ability to quantify eco-evolutionary feedbacks42 that have substantial consequences on population persistence43, trophic interactions44, community assembly45, and changes in ecosystem functions and services46. Finally, accurate predictions about biological responses to environmental change are paramount to guide the protection of biodiversity47. Current predictive models are inaccurate in this respect because they do not take into account important biological mechanisms such as demography, dispersal, evolution, and species interactions. Understanding how these processes change over time and using this information as a prior in forecast modelling will improve our ability to predict species and community persistence in the face of environmental change2.
The application of the SOP presented here is not without challenges. The primary limitation of resurrecting dormant stages is the need of specialized equipment for sampling. Additionally, the entire process, from sediment sieving to establishment of clonal cultures, requires considerable hands-on time.
Some of the SOP steps presented here are readily transferable to other Daphnia species. These are: sampling, establishment of clonal lines, and experimental design. However, other steps of the SOP may require further optimization tailored to the species under study. Decapsulation is often applied to D. magna specimens to improve hatching success. However, this approach may not be suitable for smaller specimens. Hatching stimuli can also vary among species48 and among conspecific specimen49. Hence, an ad hoc optimization of the hatching steps of the SOP may be required prior to applications to other crustaceans. Whilst the hatching success of the D. magna population resurrected from Lake Ring (30.5% across the sedimentary archive) is in line with previous results49, hatching success varies with the preservation state of the sediment, the species50,51, and the geographic origin of the sediment48. Future studies on the mechanisms that regulate entry and progression through the phases of diapause is required to identify optimal hatching stimuli tailored to different species.
Finally, background knowledge of the study system, in particular the presence of the species of interest over time, is advisable. This can be achieved via historical records. If historical records are not available, sampling and screening of the surface layers of the lake sediment prior to core sampling is advisable, although it may provide information only on the most recent history.
The authors have nothing to disclose.
This work was supported by the NERC highlights grant (NE/N016777/1). Ensis Ltd, Environmental Scientific Services, Environmental Change Research Centre, University College London sampled and dated the sediment core.
sampling bags | Fisher Scientific | 11542783 | Sampling bag revolve round wires closure system and safety tabs sterile polyethylene with writing area clear 89µm thickness 140mm x 229mm, 720mL Fisherbrand |
piston corer | ENSIS ltd | na | Long, heavy tube plunged into the sea, lake, pond floor to extract samples of mud sediment. Piston corers have a viariable diameter and are generally in PVC |
precision scale | Veritas-M124A | TLP-50 | Analytical Balance |
geological sieve | UKGE limited | SV7521 | 200 mm diameter geological sieve – 1 mm mesh |
geological sieve | UKGE limited | SV7525 | 200 mm diameter geological sieve – 0.125 mm mesh |
white sampling tray | nhbs | http://www.nhbs.com/title/view/159614?ad_id=1509 | Standard mulipurpose lab trays |
pasteur pipette | Globe Scientific inc. | 138020B | Transfer Pipet, 1.7mL, General Purpose, 87mm, Bulb Draw – 0.9mL |
stereo microscope | nikon | smz800 | Microscope with magnification range 1x -8x linked to a camera control unit |
petri dish | EduLab | 153-533 | Sterile 90mm diameter plastic petri dish |
glass jars | compak | Round Jam Jars 4oz | 100 mL jars |
glass jars | compak | Atum Jars/ Bonta Jar 10oz | 200 mL jars |
glass jars | bottlecompanysouth | 500ml Food Jam Jar With Twist Off Lid | 500 mL jars |
statistical software R | https://cran.r-project.org/ | na | Free online GNU language and environment for statistical computing and graphics |
microdissection forceps | Fisher Scientific | 41122405 | Fine point stainless steel forceps for microdissections |
image software | https://imagej.nih.gov/ij/index.html | na | Open source ImageJ image processing toolkit written in Java |
mesocosm | amazon | na | Nobby Fauna-Box III, 41 x 23 x 29 cm, 20.0 Liter |
mirocentrifuge tubes | Sigma_Aldrick – Merck | Z606340 | premium microcentrifuge tubes 1.5 mL |
AGENCOURT DNAdvance | Beckman Coulter | A48705 | DNA extraction kit |
size standard | Thermo Fisher Scientific | 4322682 | LIZ500 – Size standard compatible with ABI sequencers |
ABI3032 sequencer | ABI | na | Sequencer used to perform fragment analysis or sanger sequencing |