Ecology

The flightless dodo went extinct in the seventeenth century. Biotech company Colossal Biosciences plans to resurrect it.Credit: Hart, F/Bridgeman Images

A biotech company announced an audacious effort to ‘de-extinct’ the dodo last week. The flightless birds vanished from the island of Mauritius — in the Indian Ocean — in the late seventeenth century, and became emblematic of humanity’s negative impacts on the natural world. Could the plan actually work?Colossal Biosciences, based in Dallas, Texas, has landed US$225 million in investment (including funds from the celebrity Paris Hilton) — having previously announced plans to de-extinct thylacines, an Australian marsupial, and create elephants with woolly mammoth traits. But Colossal’s plans depend on huge advances in genome editing, stem-cell biology and animal husbandry, making success far from certain.“It’s incredibly exciting that there’s that kind of money available,” says Thomas Jensen, a cell and molecular reproductive physiologist at Wells College in Aurora, New York. “I’m not sure that the end goal they’re going for is something that’s super feasible in the near future.”Iridescent pigeonsColossal’s plan starts with the dodo’s closest living relative, the iridescent-feathered Nicobar pigeon (Caloenas nicobarica). The company plans to isolate and culture specialized primordial germ cells (PGCs) — which make sperm and egg-producing cells — from developing Nicobars. Colossal’s scientists would edit DNA sequences in the PGCs to match those of dodos using tools such as CRISPR. These gene-edited PGCs would then be inserted into embryos from a surrogate bird species to generate chimeric — those with DNA from both species — animals that make dodo-like egg and sperm. These could potentially produce something resembling a dodo (Raphus cucullatus).To gene-edit Nicobar pigeon PGCs, scientists first need to identify the conditions that allow these cells to flourish in the laboratory, says Jae Yong Han, an avian-reproduction scientist at Seoul National University. Researchers have done this with chickens, but it will take time to identify the appropriate culture conditions that suit other birds’ PGCs.A greater challenge will be determining the genetic changes that could transform Nicobar pigeons into Dodos. A team including Beth Shapiro, a palaeogeneticist at the University of California, Santa Cruz, who is advising Colossal on the dodo project, has sequenced the dodo genome but has not yet published the results. Dodos and Nicobar pigeons shared a common ancestor that lived around 30 million to 50 million years ago, Shapiro’s team reported in 20161. By comparing the nuclear genomes of the two birds, the researchers hope to identify most of the DNA changes that distinguish between them.Insights from ratsTom Gilbert, an evolutionary biologist at the University of Copenhagen, who also advises Colossal, expects the dodo genome to be of high quality — it comes from a museum sample he provided to Shapiro. But he says that finding all the DNA differences between the two birds is not possible. Ancient genomes are cobbled together from short sequences of degraded DNA, and so are filled with unavoidable gaps and errors. And research he published last year comparing the genome of the extinct Christmas Island rat (Rattus macleari) with that of the Norwegian brown rat (Rattus norvegicus)2 suggests that gaps in the dodo genome could lie in the very DNA regions that have changed the most since its lineage split from that of Nicobar pigeons.Even if researchers could identify every genetic difference, introducing the thousands of changes to PGCs would not be simple. “I’m not sure it’s feasible in the near future,” says Jensen, whose team is encountering difficulties making a single genetic change to the genomes of quail.Focusing on only a subset of DNA changes, such as those that alter protein sequences, could slash the number of edits needed. But it’s still not clear that this would yield anything resembling a wild dodo, says Gilbert. “My worry is that Paris Hilton thinks she’s going to get a dodo that looks like a dodo,” he says.A further problem will be the need to find a large bird, such as an emu (Dromaius novaehollandiae), that can act as the surrogate, says Jensen. “Dodo eggs are much, much larger than Nicobar pigeon eggs, you couldn’t grow a dodo inside of a Nicobar egg.”Chicken embryos are fairly receptive to PGCs from other birds, and Jensen’s team has created chimeric chickens that can produce quail sperm — efforts to generate eggs have failed so far. But he thinks it will be far more challenging to transfer PGCs — particularly heavily gene-edited ones — from one wild bird into another.Conservation boon?Colossal chief executive Ben Lamm acknowledges these hurdles, but argues they aren’t dealbreakers. Work towards dodo de-extinction will help with conservation efforts for other birds, he adds. “It will bring a lot of new technologies to the field of bird conservation,” agrees Jensen.Vikash Tatayah, conservation director at the Mauritian Wildlife Foundation in Vacoas-Phoenix, is also enthusiastic about the attention dodo de-extinction could bring to conservation. “It’s something we would like to embrace,” he says.But he points out that the predators that threatened the dodo in the seventeeth century haven’t gone away, whereas most of its habitat has. “You do have to ask,” he says, “if we could have such money, wouldn’t it be better spent on restoring habitat on Mauritius and preventing species from going extinct?” More

Flash recordingAll field recording and experiments were performed at the paddy field in the Northern Chita Peninsula, Aichi Prefecture, central Japan, in June and July between 2003 and 2016. The ambient temperature at the firefly’s active period was measured using a thermometer. The flashes were recorded with a digital video camera (NV-GS-400, Panasonic, Japan) mounted on a tripod at a height of 30–50 cm from ground and a distance of 1.0–1.5 m away from the specimen. Isolated specimens were selected for recording to exclude the background light from other nontarget specimens. When another specimen appeared near the target specimen, the video recording was cancelled. When a female copulated during video recording in the field, her flashes until 1 min before copulation were regarded as those of a ‘receptive female’. To record the flashes of a ‘mated female’, the female specimens already mated were prepared in aquariums (because virgin and mated females cannot be distinguished in the field): the eggs were obtained from wild female specimens collected one year before at the same field and reared to adults; immediately after emergence the virgin female was confined in a small container with two cultured males for two nights to facilitate copulation. As the parents of the reared specimens were collected from the observation field (same genetic background), the rearing temperature was almost the same as that of the natural field, the emergence period of the cultured specimens overlapped with that of the natural population, the adult body sizes of the reared and natural specimens were indistinguishable, and the flash pattern of the cultured mated females was indistinguishable from that of the wild (potentially) mated females. Thus, we believe that there was no influence of different rearing environments, i.e., the flash behavior of the cultured mated female specimens is expected to be substantially the same as that of wild mated female specimens. To distinguish them from wild (potentially) mated females, the elytra of cultured mated females were marked with colored ink before placing them in the field, and after three days, the flashes of ink-marked specimens were recorded. Of note, we never observed male attraction and copulation in any of the mated females used for field observation; thus, the mated females were unreceptive.Waveform analysisSequential still images were captured from video files at 30 frames per second using VirtualDub (GPL), and then the light intensities in the images were qualified (8-bit linear gray scaling from black to white at 0–255) using ImageJ software. In this study, we defined ‘flash’ as a luminescent waveform from baseline to baseline and ‘flickering’ as fluctuation above baseline in a single flash. The waveforms containing a saturated signal (255, white) were omitted. The waveforms of the maximum signal value lower than 50 were also omitted because of the difficulty in separating signal and noise. Approximately 10–90 waveforms per individual were analyzed; thus, the effect of the occasional interruption of the flash recording by the specimen’s movement and/or vegetation swinging between the specimen and the video camera is statistically ignorable. FD is defined as the time interval between the beginning and the end of a flash (Fig. S1). Flicker intensity (FI) was defined as$${text{FI}} = left{ {begin{array}{*{20}l} {mathop {max }limits_{1 le i le n} left( {frac{{{text{min}}left( {p_{i} ,p_{i + 1} } right) – t_{i} }}{{min left( {p_{i} , p_{i + 1} } right) + t_{i} }}} right)} hfill & {{text{if}} , n ge 1} hfill \ 0 hfill & {{text{if}} , n = 0} hfill \ end{array} } right.$$where p, t, and n denote the peak and the trough (local extrema) in the waveform of a flash and the number of toughs in the flash, respectively (Fig. S1). In total, we measured the FD and FI values of 347, 94, and 355 waveforms from 13 sedentary males, 7 receptive females, and 8 mated females, respectively. We did not consider the flash brightness as a factor because the measured value of the light intensity depends largely on the distance between the light source and the detector; thus, the actual brightness of the lantern cannot be practically measured in the field.e-FireflyFor male attraction experiments, we built an electronic LED device, the e-firefly, to generate patterned flashes with various FDs and FIs using a chip LED (green type, λmax = 568 nm, Everlight Electronics, Taiwan; Figs. S2 and S3) with a microcontroller PIC16F628A (Microchip Technology, USA) (see Figs. S4-S5). An example of the program for the microcontroller is shown in Supplementary Data S1. The brightness was constant in all programs. Flickering frequency ranged between 5–12 Hz, which corresponds to that of sedentary male flashes (approximately 10 Hz)15. To prevent direct access of the attracted specimen to the light source, the chip LED was covered by a steel net painted green (see Fig. S2). For flying male attraction experiments, when the male landed within a 100-mm distance from the e-firefly, we judged the attraction to be a success; otherwise, it was a failure. For sedentary male attraction experiments, the e-firefly was placed 200–300 mm away from the sedentary male. When the approaching male touched the steel net covering the e-firefly, to warrant a positive approach, we measured the time the male remained on the net. If the male did not move away from the net for more than 2 min, we judged the attraction to be a success (strict criterion for judgment); otherwise, it was a failure.Spectral measurementThe luminescence spectra of e-firefly and A. lateralis were measured using a Flame-S spectrophotometer (Ocean Insight, USA). The living A. lateralis specimens were anesthetized on ice and frozen at − 20 °C until use. The lantern started luminescence by thawing at room temperature, and the spectrum was measured during luminescence (within 5 min).Statistical analysisFirst, we considered a discriminant analysis using a logistic regression model that discriminates between receptive females and others in the observational data. We fitted several models with combinations of FD and FI, quadratic terms of FD and FI (FD2, FI2), interaction of FD and FI (FD (times) FI), and temperature (T) as explanatory variables. Based on Akaike’s information criteria (AIC) values and model simplicity, we chose the logistic regression model with FD, FI, FD2 and T as explanatory variables. Let (p)(({varvec{x}})) denote the conditional probability that a flash is from a receptive female given ({varvec{x}}=left(mathrm{FD},mathrm{ FI},mathrm{ T}right)) and (widehat{p})(({varvec{x}})) denote its estimate. The coefficients of the logistic regression model are estimated as follows.
[Model for the observational data with temperature (T)]
$$begin{gathered} {text{log}}frac{{hat{p}}}{{1 – hat{p}}} = begin{array}{*{20}l} { – 32.26 + 69.69 times FD – 43.47 times FI – 76.63 times FD^{2} + 0.87 times T} hfill \ {~quad left( {6.50} right)quad quad left( {15.37} right)quad quad quad left( {8.56} right)quad quad quad quad left( {17.44} right)quad quad quad left( {0.19} right)~~} hfill \ end{array} hfill \ quad {text{AIC: 84}}{text{.75}} hfill \ end{gathered}$$[Model for the observational data without temperature (T)]$$begin{gathered} {text{log}}frac{{hat{p}}}{{1 – hat{p}}} = begin{array}{*{20}l} { – 7.69~ + 47.57 times FD~ – 38.29 times FI~ – 52.86 times FD^{2} ~} hfill \ {~;left( {1.86} right)quad quad left( {9.68} right)quad quad quad left( {7.08} right)quad quad quad quad left( {11.38} right)~~} hfill \ end{array} hfill \ quad {text{AIC: 114}}{text{.89}} hfill \ end{gathered}$$where values in parentheses indicate standard deviations. The same applies hereafter. Temperature (T) is included in the model not because it affects the occurrence of receptive females but because it affects the FD and/or FI of receptive females. The AIC value increased by 30, which is substantial, when temperature was excluded from the model.Figure 2 shows the FD and FI of each flash from receptive females, mated females and males with the discriminant boundaries of receptive females from others for (p=0.5).We next considered a discriminant analysis for the experimental data. Let ({q}^{f}({varvec{x}})) denote the conditional probability that a flying male is attracted to a flash of ({varvec{x}}=left(mathrm{FD},mathrm{ FI},mathrm{ T}right)) and lands, and ({widehat{q}}^{f}({varvec{x}})) denote its estimate. Among several models we fit, the smallest AIC value is attained by the logistic regression model with FD, FI and T as explanatory variables, but the AIC is not much different from the model with FD and FI only.
[Model for flying males with temperature (T)]
$$begin{gathered} {text{log}}frac{{hat{q}^{f} }}{{1 – hat{q}^{f} }} = begin{array}{*{20}l} { – 0.74~~ – 2.42 times FD – 16.82 times FI + 0.31 times T} hfill \ {~;left( {4.01} right)quad quad left( {0.83} right)quad quad quad left( {4.88} right)quad quad quad quad left( {0.20} right)~} hfill \ end{array} hfill \ quad {text{AIC}}:66.96 hfill \ end{gathered}$$

[Model for flying males without temperature (T)]
$$begin{gathered} {text{log}}frac{{hat{q}^{f} }}{{1 – hat{q}^{f} }} = begin{array}{*{20}l} { – 5.36~ – 1.72 times FD – 13.69 times FI} hfill \ {~;left( {1.49} right)quad quad left( {0.63} right)~quad quad left( {4.09} right)~~} hfill \ end{array} hfill \ quad {text{AIC}}:67.61 hfill \ end{gathered}$$
For sedentary males, the model with the smallest AIC value includes all the quadratic terms of FI and FD but not temperature. Let ({q}^{s}({varvec{x}})) denote the conditional probability that a sedentary male is attracted to a flash of ({varvec{x}}=left(mathrm{FD},mathrm{ FI},mathrm{ T}right)) and ({widehat{q}}^{s}left({varvec{x}}right)) denote its estimate. The logistic regression model for ({q}^{s}({varvec{x}})) with the best AIC value is given as follows.
[Model for sedentary males]
$${text{log}}frac{{hat{q}~^{s} }}{{1 – hat{q}~^{s} }} = begin{array}{*{20}l} { – 0.68~ + 7.84 times FD~ + 48.17 times FI – 5.35 times FD^{2} – 166.70 times FI^{2} – 65.67 times FD times FI} hfill \ {;left( {0.97} right)quad quad quad left( {2.99} right)quad quad quad left( {17.74} right)quad quad quad left( {1.74} right)quad quad quad quad left( {72.34} right)quad quad quad quad left( {17.67} right)~} hfill \ end{array}$$
Figure 3 shows successes and failures of attraction of flying males on the left and sedentary males on the right with estimated discriminant boundaries.Let us now estimate probabilities that a flying male is attracted and lands or a sedentary male is attracted to a flash when a flash is from a receptive female or when a flash is either from a sedentary male or mated female. The probability that a flying male is attracted and lands when a flash is from a receptive female is a conditional probability and is expressed as follows.$$begin{aligned} Pleft(left.begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} right|begin{array}{*{20}c} {text{Receptive }} \ {{text{female}}} \ end{array} right) & = frac{{Pleft( {begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} {text{ and }}begin{array}{*{20}c} {text{Receptive }} \ {{text{female}}} \ end{array} } right) }}{{Pleft( {begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} } right)}}, \ Pleft( {begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} } right) & = mathop int_{Omega } Pleft(left. begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} right|{varvec{x}} right)fleft( {varvec{x}} right)d{varvec{x}} = mathop int_{Omega }pleft( {varvec{x}} right) fleft( {varvec{x}} right)d{varvec{x}} hspace{5mm}{text{and}} \ Pleft( {begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} {text{ and }}begin{array}{*{20}c} {text{Receptive }} \ {{text{female}}} \ end{array} } right) & = mathop int_{Omega } Pleft(left. begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} right|varvec{x} right)Pleft(left. begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} right|{varvec{x}} right)fleft( {varvec{x}} right)d{varvec{x}} \ & = mathop int_{Omega } pleft( varvec{x} right)q^{f} left( {varvec{x}} right)fleft( {varvec{x}} right)d{varvec{x}}mathbf{.} \ end{aligned}$$Integrals are taken over the domain (Omega) of ({varvec{x}}=(FD, FI, T)) of all females and males, and (f({varvec{x}})) is the joint density function of ({varvec{x}}.) Because (f({varvec{x}})) is unknown, we use the empirical distribution of the observational data, and conditional probabilities given ({varvec{x}}) are replaced with their estimates by logistic regression models. Let ({{varvec{x}}}_{i}=left(F{D}_{i}, F{I}_{i}, {T}_{i}right), i=mathrm{1,2},dots N) denote the (i) th observation in the observational data. The estimates of probabilities are given as follows:$$begin{aligned} hat{P}left( {begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} }right) & = frac{1}{N}mathop sum limits_{i = 1}^{n} hat{p}left( {{varvec{x}}_{i} } right) hspace{15mm} {text{and}} \ hat{P}left( {begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} {text{ and }}begin{array}{*{20}c} {text{Receptive }} \ {{text{female}}} \ end{array} } right) & = frac{1}{N}mathop sum limits_{i = 1}^{n} hat{p}left( {{varvec{x}}_{i} } right) hat{q}^{f} left( {{varvec{x}}_{i} } right). \ end{aligned}$$Thus,$$hat{P}left( left. begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} right| begin{array}{*{20}c} {text{Receptive }} \ {text{female}} \ end{array} right) = frac{{mathop sum nolimits_{i = 1}^{n} hat{p}left( {{varvec{x}}_{i} } right) hat{q}^{f} left( {{varvec{x}}_{i} } right)}}{{mathop sum nolimits_{i = 1}^{n}hat{p}left(varvec{x}_i right)}}.$$Similarly, we have$$begin{aligned} hat{P}left( left.begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array}right| {text{Others}} right) & = frac{{mathop sum nolimits_{i = 1}^{n} (1 – hat{p}left( {{varvec{x}}_{i} } right)) hat{q}^{f} left( {{varvec{x}}_{i} } right)}}{{mathop sum nolimits_{i = 1}^{n} (1 – hat{p}left( {{varvec{x}}_{i} } right))}} \ hat{P}left( left. begin{array}{*{20}c} {text{Sedentary male}} \ {text{is attracted}} \ end{array} right| begin{array}{*{20}c} {text{Receptive }} \ {text{female}} \ end{array} right)& = frac{{mathop sum nolimits_{i = 1}^{n} hat{p}left( {{varvec{x}}_{i} } right) hat{q}^{s} left( {{varvec{x}}_{i} } right)}}{{mathop sum nolimits_{i = 1}^{n} hat{p}left( varvec{x}_{i} right)}}hspace{15mm} {text{ and}} \hat{P}left(left. begin{array}{*{20}c} {text{Sedentary male}} \ {text{is attracted}} \ end{array}right| {text{Others}} right) & = frac{{mathop sum nolimits_{i = 1}^{n} left( {1 – hat{p}left( varvec{x}_{i} right)} right) hat{q}^{s} left( {varvec{x}_{i} } right)}}{mathop sum nolimits_{i = 1}^{n} left( {1 – hat{p}left( varvec{x}_{i} right)} right)} . \ end{aligned}$$The estimated probabilities are shown in Table 1.Table 1 Estimated probabilities of a flying male and a sedentary male being attracted to flashes from a receptive female and from others.Full size table More

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