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Study areaWe conducted the study in the best-preserved stands of the Białowieża Forest, strictly protected within the Białowieża National Park (hereafter BNP; coordinates of Białowieża village: 52°42′N, 23°52′E). The extensive Białowieża Forest (c. 1500 km2) straddles the Polish-Belarusian border, where the climate is subcontinental with annual mean temperatures during May–July of 13–18 °C, and mean annual precipitation of 426–940 mm66,67.The forest provides a unique opportunity to observe animals under conditions that likely prevailed across European lowlands before widespread deforestation and forest exploitation by humans66,68,69. The stands have retained a primeval character distinguished by a multi-layered structure, frequent fallen and standing dead trees, and a high species richness66,70. The stands are composed of about a dozen tree species of various ages, up to several hundred years old. The interspecific interactions and natural processes have been little affected by direct human activity.We conducted observations mostly within the three permanent study plots (MS, N, W), totalling c. 130 ha, and in other nearby fragments of primeval oak-lime-hornbeam Tilio-Carpinetum or mixed deciduous-coniferous Pino-Quercetum stands. However, a small number of observations from adjacent managed deciduous forest stands were also included. For details of the study area see71,72,73.Study speciesOur study system focused on ground-nesting Wood Warblers Phylloscopus sibilatrix, blowflies Protocalliphora azurea, and Myrmica or Lasius ants, which occurred in the birds’ nests.The Wood Warbler is a small (c. 10 g) insectivorous songbird that winters in equatorial Africa and breeds in temperate European forests, typically rearing one or two broods each year74. Wood Warblers build dome-shaped nests for each breeding attempt, composed of woven grass, leaves and moss, and lined with animal hair73. The nests are situated on the ground among moderately sparse vegetation, often under a tussock of vegetation or near a fallen tree-branch or log (see examples in Supplementary Fig. S2)53,75. The breeding season of Wood Warblers begins in late April–early May and ends in July–August, when nestlings from replacement clutches (after initial loss) or second broods leave the nest. The typical clutch size in BNP is 5–7 eggs, and the nestling stage lasts 12–13 days74,76.Wood Warbler nests are inhabited by various arthropods, including Myrmica ruginodis or M. rubra ants, and less often Lasius platythorax, L. niger or L. brunneus. The ants foraged and/or raised their own broods within the Wood Warbler nests52. The Myrmica and Lasius ant species are common in Europe77,78. Their colonies contain from tens to thousands of workers, and can be found on the forest floor, e.g. in soil, within or under fallen dead wood, in patches of moss, or among fallen tree-leaves53,77,78. All of the ant species found in the Wood Warbler nests are predators of other arthropods77,79,80.Blowflies, Protocalliphora spp., are obligatory blood-sucking (hematophagous) ectoparasites that reproduce within bird nests. The occurrence, abundance, and impact of blowflies on Wood Warbler offspring is largely unknown, similar to many other European songbirds that build dome-shaped nests. Adult blowflies emerge in late spring and summer to lay eggs on the birds’ nesting material or directly onto the skin of typically newly hatched nestlings14,26. The blowfly larvae hatch within two–three days, and develop in the structure of warm bird nests for another 6–15 days, during which they emerge intermittently to feed on host blood, before finally pupating within the nests14,25,26,27.Data collectionNest monitoring and measurements of nestlingsWe searched for Wood Warbler nests daily from late April until mid-July in 2018–2020, by following birds mainly during nest-building. Nests were assigned to a deciduous or mixed deciduous-coniferous habitat type, depending on the tree stand where they were found. We inspected nests systematically, according to the protocol described in Wesołowski and Maziarz76. The number of observer visits was kept to a minimum to reduce disruptions for birds or potential risks of nest predation.We aimed to establish the dates of hatching (day 0 ± 1 day), nestlings vacating the nest (fledging; ± 1 day) or nest failure (± 1–2 days). When nestlings hatched asynchronously, the hatching date corresponded to the earliest record of nestling hatching. The dates of fledging or nest failure were the mid-dates between the last visit when the nestlings were present in the nest, and the following visit, when the nest was found empty. Nest failure was primarily due to predation, which is the main cause of the Wood Warbler nest losses in BNP76,81 and elsewhere in Europe82,83.To assess fitness consequences for birds of variable weather conditions, blowfly abundance and/or ant presence, we measured nestling growth and determined brood reduction (i.e. the mortality of chicks in the nest) from hatching until fledging. To define brood reduction, we assessed the number of hatchlings (nestlings up to 4 days old) and the number of fledglings leaving the nests. To ensure accurate counting and avoid premature fledging of nestlings, we established the number of fledglings on the day of measurement, when all nestlings were temporarily extracted from the nest.We measured nestling growth on a single occasion when they were 6–9 days old (median 8 days), almost fully developed but too young to leave the nest. The measurements lasted for less than 10–15 min at each nest to minimise any potential risk of attracting predators. For each nestling we measured (using a ruler) the emerged length of the longest (3rd) primary feather vane (± 0.5 mm) on the left wing84,85, and body mass to the nearest 0.1 g using an electronic balance. The length of the feather vane is closely linked to feather growth86 and is one of the characteristics of nestling growth85,87. We treated the length of the primary feather vane and body mass as indices of nestling growth rate under varying conditions of weather, blood-sucking ectoparasites, or ant presence.Extraction of arthropods from bird nestsTo assess the number of blowflies and to establish the presence of ants, we checked the contents of 129 nests (including 11 nests from the managed forest stands) at which Wood Warbler nestlings had been measured. The sample included 86 successful breeding attempts (where a minimum of one nestling successfully left the nest), 27 failed (predated) nests (remnants of nestlings were found, but the nest structure remained intact), and 16 nests with an unknown fate (nestlings were large, so were capable of leaving the nest, but no family were located or other signs indicating fledging).Due to ethical reasons, we were unable to collect the Wood Warbler nests and extract the ectoparasites and ants from them while they were in use by the birds. Removing the nests and replacing them with dummy nests would cause unacceptable nest desertion by adults. Therefore, we assessed the occurrence and number of blowflies or ant presence after Wood Warbler nestlings fledged or the breeding attempts failed naturally. We retrospectively explored the changes in blowfly infestation14, including the effect of ant presence53 in the same nests.We collected nests from the field as soon as a breeding attempt ended, within approximately five days (median 1 day) following fledging or nest failure (nest structure remained intact). The delay of nest collection would not bias the ectoparasite infestation, as blowfly larvae pupate within bird nests and stay there after the hosts abandon their nests; puparia can be still found in nests collected in autumn or winter14. As the likelihood of finding ant broods (larvae or pupae associated with workers) was rather stable with the delay of nest collection53, the method seemed reliable also for assessing the presence of ant broods (35 of all 71 Wood Warbler nests containing ants). Only the number of nests with lone foraging ant workers could be underestimated, potentially inflating the uncertainty of tested relationships. However, as ants usually re-use rich food resources88, foraging Myrmica or Lasius ant workers might regularly exploit warbler nests, increasing the chances of finding the insects in the collected nests.Wood Warbler nests were collected in one piece, with each placed into a separate sealed and labelled plastic bag. We carefully inspected the leaf litter around the nests, and the soil surface under them, to make sure that all blowfly larvae or pupae were collected. We transported the collected nests to a laboratory, where we stored them in a fridge for up to 5–6 days before the arthropod extraction.To establish the number of blowflies and the presence of ants, in 2018, we carefully pulled apart the nesting material and searched for the arthropods amongst it 52. We gathered all blowfly pupae or larvae and a sample of ant specimens into separate tubes, labelled and filled with 70–80% alcohol, for later species identification. For nests collected in 2019–2020, we extracted the arthropods with a Berlese-Tullgren funnel. During the extraction, which usually lasted for 72 h, each nest was covered with fine metal mesh and placed c. 15 cm under the heat of a 40 W electric lamp. The arthropods were caught in 100 ml plastic bottles containing 30 ml of 70–80% ethanol, installed under each funnel. After the arthropod extraction, we carefully inspected the nesting material in the same way as in 2018, to collect any blowflies that remained within the nests. The quality of information collected on the number of ectoparasites and ant presence should be comparable each year.Weather dataWe obtained the mean daily temperatures and rainfall sums from a meteorological station, operated by the Meteorology and Water Management National Research Institute in the Białowieża village, 1–7 km from the study areas.Data analysesWeather conditions affecting blowfly ectoparasitesTo explore the impact of weather on blowfly ectoparasites, for each Wood Warbler nest we calculated average temperatures from daily means, and total sums of rainfall from daily sums, for the two time-windows in which we assumed the impact of weather would be of greatest importance:

i.

the early nestling stage, when Wood Warbler nestlings were 1–4 days old. During this stage, female blowflies require a minimum temperature of c. 16 °C to become active and oviposit in bird nests27. Thus, cool and wet weather in the early nestling stage should reduce the activity of ovipositing blowflies, leading to less frequent ectoparasite infestation of Wood Warbler nests.

ii.

The late nestling stage, when the warbler nestlings were aged between over four days old and until fledging or nest failure. During this stage, blowfly larvae grow and develop in bird nests after hatching a few days after oviposition14,25,26,27. As the temperature of bird nests strongly depends on ambient temperatures21, mortality of blowfly larvae should increase in cool weather, resulting in fewer ectoparasites in nests collected shortly after the fledging of birds29.

Weather conditions affecting Wood Warbler nestling growthTo explore the impact of weather on nestling growth, for each nest we calculated the average temperatures and total sums of rainfall for the period when nestlings were over four days old and until their measurement, usually on day 8 from hatching (see above). During this stage, nestlings are no longer brooded by a parent74, so must balance their energetic expenditure between growth (feather length and body mass) or thermoregulation89. Thus, we expected that the gain in body mass and the growth of flight feathers would be reduced in nestlings during cool and wet weather, when maintaining a stable body temperature would be costly90.Statistical analysesAll statistical tests were two-tailed and performed in R version 4.1.091.The changes in blowfly infestation of the Wood Warbler nestsTo test the changes in blowfly infestation of warbler nests, we used zero-augmented negative binomial models (package pscl in R;92,93), which deal with the problem of overdispersion and excess of zeros92. In this study, hurdle and zero-inflated models fitted with the same covariates had an almost identical Akaike Information Criterion (AIC). Therefore, we presented only the results of hurdle models, which are easier to interpret than zero-inflated models. Hurdle models consisted of two parts: a left-truncated count with a negative binomial distribution representing the number of blowflies in infested nests, and a zero hurdle binomial estimating the probability of blowfly presence. We used models with a negative binomial distribution, which had a much lower AIC than with a Poisson distribution on a count part.We designed the most complex (global) model that contained a response variable of the number of blowflies in each of the 129 Wood Warbler nests. The covariates were: mean ambient temperature, total sum of rainfall, presence (or absence) of ants in the same nests, habitat type (deciduous vs mixed deciduous-coniferous forest), study year (2018–2020), the number of nestlings hatched (brood size), and nest phenology (the relative hatching date of Wood Warbler nestlings, as days from the median hatching date in a season: 23 May in 2018, 25 May in 2019 and 29 May in 2020). The initial global model also contained the two-way interaction terms that we suspected to be important: between temperature and rainfall, temperature and presence of ants, and rainfall and presence of ants.To explore all potentially meaningful subsets of models, we used the same covariates on both parts (count and binomial) of the global model. We performed automated model selection with the MuMIn package94, starting from the most complex (global) model and using all possible simpler models (i.e. all subsets)95. To attain the minimum sample size of c. 20 data points for each parameter96, we limited the maximum number of parameters to six in each part (count or binomial) of the candidate models.As some of the interaction terms appeared insignificant in the initial model selection, to minimise the risk of over-parametrisation, we included only the significant interaction term on a count part of the final global model. As described above, we performed model selection again. We tested linear relationships, as the quadratic effects of weather variables (presuming temperature or rainfall optima) appeared insignificant.To test whether blowfly infestation changed with weather in the early or late nestling stages, we twice repeated the procedure described above. The first global model included the mean ambient temperature and the total sum of rainfall for the early nestling stage, and the second global model contained weather variables for the late nestling stage. The remaining covariates were the same.A practice of including the same sets of covariates on count and binomial parts has been previously questioned97. However, our approach allowed us to comply with these objections97, as we presented only the most parsimonious models (with ΔAICc  More

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Animal experimentsTeratoma formation experiments were performed at Iwate University. All surgical procedures and animal husbandry were performed in accordance with the international guidelines of the Animal Experiments of Iwate University and were approved by the university’s Animal Research Committee (approval number A201734).Chicken embryonic fibroblasts (Rhode Island Red) were obtained from a primary culture of chicken embryonic tissue provided by Prof. Atsushi Tajima, Tsukuba University. Chicken culture cells were obtained from chicken embryos, and the acquisition of these cells did not require approval. Mouse embryonic fibroblasts (CF-1 strain) were purchased from a manufacturer (CMPMEFCFL; DS Pharma Biomedical, Osaka, Japan). Approval was not required to obtain these cells.Somatic cells were obtained from wild animals (ex., Okinawa rail). The sampling details described below do not include the exact location of sampling to protect against poaching.Fibroblast cells from Okinawa rail and Japanese ptarmigan were obtained from dead animals, such as those killed by vehicles (Fig. 1A and Supplementary Fig. 1). Approval was not required to obtain these samples.Dead Okinawa rail were found on May 21, 2008, by the Okinawa Wildlife Federation, a nonprofit organization that focuses on the conservation of wild animals in the Okinawa area in the southwest region of Japan. The organization has permission from the Japanese Ministry of the Environment (MOE) to handle and perform first aid activities on endangered animals. The dead birds were transferred the following day to the National Institute for Environmental Studies (NIES). Primary cell culture was carried out from muscle tissue and skin of the dead birds (NIES ID: 715A).On July 8, 2004, tissues recovered from dead Japanese ptarmigan (e.g., skin and retina tissues) were also transferred to NIES from Gifu University Department of Veterinary Medicine. Primary cell culture from this tissue was performed (NIES ID: 22A).Somatic cells from Blakiston’s fish owl and Japanese golden eagle were obtained from emerging pinfeathers. Concerning the Blakiston’s fish owl, the MOE carries out bird banding, of wild birds with identification tags. The emerging pinfeathers we used had been accidentally release during banding. The banding had been performed by a veterinarian at the Institute for Raptor Biomedicine Japan (IRBJ) in the Hokkaido area on June 2, 2006. IRBJ is a private organization that primarily focuses on emergency medicine first aid and care for wild avians in Hokkaido region of Japan. IRBJ is contracted to MOE to handle and administer first aid for endangered animals. The MOE banding ring was 14C0242. Since banding was carried out with the permission of MOE for capturing wildlife, we did not require the approval to obtain these avian somatic cells. On July 8, 2006, Blakiston’s fish owl pinfeathers were transferred to from IRBJ to NIES, where primary cell culture was performed (NIES ID: 215A).Concerning the Japanese golden eagle, an emerging pinfeather accidentally fell off a bird during blood collection at the Yagiyama Zoo in Sendai, Japan on July 11, 2018. Dr. Yukiko Watanabe, an IRBJ veterinarian, collected the emerging pinfeather. The sample was shipped the following day to NIES where primary cell culture was performed (NIES ID: 5228).In addition to these birds, we obtained somatic cells emerging avian pinfeathers of Steller’s sea eagle, white-tail eagle, mountain hawk-eagle, northern goshawk, Taiga bean goose, and Latham’s snipe. These samples were provided by IRBJ.Concerning the Steller’s sea eagle, an injured individual was found in Hokkaido on July 11, 2006 (ID: 06-NE-SSE-1). The eagle was transferred to IRBJ. On December 4, 2006, IRBJ veterinarian Dr. Keisuke Saito collected fallen pinfeathers. Primary cell culture was performed at NIES on December 8, 2006 (NIES ID: 369A).Concerning the white-tailed eagle, an injured individual was found in Hokkaido, Japan, on July 12, 2007 (ID: 07-NE-WTE-4). The bird was transferred to IRBJ the same day for emergency treatment. On January 15, 2008, Dr. Saito collected fallen pinfeathers. Primary cell culture was performed on January 18, 2008 at NIES (NIES ID: 492A).Concerning the mountain hawk-eagle, an injured individual was found in the Hokkaido area on August 10, 2008 (ID: 08-Tokachi-HHE-2). The bird was transferred to IRBJ the same day. The bird was treated by an IRBJ veterinarian, but died on September 8, 2008. Emerging pinfeathers were collected from the dead bird by Dr. Saito. Primary cell culture was performed on September 11, 2008 at NIES (NIES ID: 847A).Concerning the Northern Goshawk, IRBJ accepted an injured bird for treatment on June 12, 2006. Following treatment and recovery, the bird was released into the wild in the Hokkaido area on August 1, 2006. During the treatment (July 4, 2006), Dr. Saito collected fallen pinfeathers. The primary cell culture was performed at NIES on July 6, 2006 (NIES ID: 222A).Concerning the Taiga bean geese, an injured individual was found in Hokkaido on September 15, 2016 (ID: 13B8005). The injured bird was transferred to IRBJ the same day for emergency treatment. On September 16, 2016, IRBJ veterinarian Dr. Yukiko Watanabe collected fallen emerging pinfeathers. Primary cell culture was performed on September 20, 2016 (NIES ID: 4420A).Finally, concerning the Latham’s snipe, fallen pinfeathers were collected during MOE approved bird banding performed on September 17, 2006, by Dr. Saito. Dr. Saito also collected fallen emerging pinfeathers (ID: 6A22598). The samples were transferred to NIES on September 20, 2006, for primary cell culture (NIES ID: 338A).All records are available at NIES.Cell culture and preservationOkinawa rail, Japanese ptarmigan, and Blakiston’s fish owl-derived fibroblasts were preserved in liquid nitrogen for 8–12 years (Fig. 1f). The preservation solution contained 90% fetal bovine serum (FBS) and 10% dimethyl sulfoxide. Cells were preserved at a cell density of 1 × 106–4 × 106 cell/mL. During the freezing period, the cells were maintained at minus The cells were frozen at a temperature of −135 °C. Japanese golden eagle fibroblasts were used without freezing.Avian-derived fibroblasts were cultured with Kuwana’s modified avian culture medium-1 (KAv-1), which is based on alpha-MEM containing 5% FBS and 5% chicken serum23. Mouse embryonic fibroblasts were cultured in Dulbecco’s modified Eagle’s medium (DMEM) containing 10% FBS and 1% antibiotic–antimycotic mixed stock solution (161–23181; Wako Pure Chemical Industries, Osaka, Japan). All avian and mouse cells were cultured at 37 °C under 5% CO2.Reprogramming vectorWe chemically synthesized an expression cassette that included seven reprogramming factors (MyoD-derived transactivation domain-linked Oct3/4, Sox2, Klf4, c-Myc, Klf2, Lin28, and Nanog; all genes derived from mice). The self-cleaving 2A peptide was inserted at the junction of the coding region (Fig. 1g). We transferred the complementary DNA (cDNA) insert from the shuttle vector to the PiggyBac transposon vector containing green fluorescent protein (PB-CAG-GFP). Although the original transposon vector drive the expression of cDNA with the elongation factor-1 (EF1) promoter (PJ547-17; DNA 2.0, Menlo Park, CA, USA), we replaced the EF1 promoter to CAG promoter in our previous study22,24. The reprogramming vector was designated PB-TAD-7F (Fig. 1g).In addition to the PB-TAD-7F reprogramming vector, we used the PB-DDR-8F reprogramming vector to establish Japanese golden eagle iPSCs. The complete coding sequence of DDR-8F (DDR-Oct3/4, Sox2, Klf4, c-Myc, Klf2, Nanog, Lin28, and Yap) was chemically synthesized. The expression cassettes containing the eight reprogramming factors were excised from the shuttle vector using restriction enzymes. The cDNA fragments were transferred to the PB-CAG-GFP PiggyBac transposon-based vector22,24. Detailed information regarding the PB-DDR-8F reprogramming vectors is shown in Fig. 10a.Establishment of iPSCsWe transfected PB-R6F or PB-TAD-7F reprogramming vectors into mouse, chicken, Okinawa rail, Japanese ptarmigan, and Blakiston’s fish owl-derived fibroblasts using Lipofectamine 2000 transfection reagent (Thermo Fisher Scientific, Waltham, MA, USA). After hygromycin selection (Wako Pure Chemical Industries), the cells were reseeded onto a mouse embryonic fibroblast (MEF) feeder layer. On days 14–32, we picked primary iPSC-like colonies and seeded them on new MEF feeder cell plates. The detailed protocol is shown in Fig. 1h.To establish Japanese golden eagle-derived iPSCs, we transduced PB-TAD-7F or PB-DDR-8F reprogramming vectors into Japanese golden eagle pinfeather-derived somatic cells. Transfection was performed using Lipofectamine 2000 transduction reagent (11668019; Thermo Fisher Scientific) according to the manufacturer’s instructions. After hygromycin selection (Wako Pure Chemical Industries), cells were seeded onto feeder culture plates. The golden eagle iPSCs were cultured in KAv-1-based medium5.The medium used to establish avian iPSCs was supplemented with 1000 × human Leukemia Inhibitory Factor (LIF) (125–05603; Wako Pure Chemical Industries), 4.0 ng/ml basic FGF (064–04541; Wako Pure Chemical Industries), 0.75 μM CHIR99021 glycogen synthase kinase-3 inhibitor (034–23103; Wako Pure Chemical Industries), 0.25 μM PD0325901 mitogen-activated protein kinase inhibitor (163–24001; Wako Pure Chemical Industries). In addition to those supplements, 0.25 μM thiazovivin (202–18011; Wako Pure Chemical Industries) was added in the media used to generate Okinawa rail, Japanese ptarmigan, Blakiston’s fish owl, and chicken iPSCs. In the medium used to generate mouse iPSCs, we added 1000 × LIF, 0.75 μM CHIR99021, and 0.25 μM PD0325901.iPSC culture conditionsTwo types of cell culture media were used: KAv-1 for avian iPSCs and DMEM for mouse iPSCs. The composition of KAv-1 for avian iPSCs was as follows: alpha-MEM containing 5% FBS and 5% chicken serum 1% antibiotic–antimycotic mixed solution, 1% nonessential amino acids (Wako Pure Chemical Industries), and 2 mM glutamic acid was added (Nacalai Tesque, Kyoto, Japan). The composition of DMEM for mouse was follows: DMEM supplemented with 15% SSR, 0.22 mM 2-mercaptoethanol (21438–82, Nacalai Tesque), 1% antibiotic–antimycotic mixed solution, 1% nonessential amino acids5,22. As a supplement to the iPSC medium, we used 1000 × human LIF (125–05603; Wako Pure Chemical Industries), 4.0 ng/ml basic FGF (064–04541; Wako Pure Chemical Industries), 0.75 μM CHIR99021 (034–23103; Wako Pure Chemical Industries), 0.25 μM PD0325901 (163–24001; Wako Pure Chemical Industries) for the media used to culture Okinawa rail, Japanese ptarmigan, Blakiston’s fish owl, Japanese golden eagle, and chicken-derived iPSCs. The supplements for media used to culture Okinawa rail and Japanese ptarmigan-derived iPSCs included 2.5 μM Gö6983 (074–06443, Wako Pure Chemical Industries). To analyze the cellular characteristics, we focused on the Janus kinase (JAK), FGF, ROCK, and glycolytic pathways, since the dependency of these pathways can indicate differences in cellular characteristics. We used 1–10 μM JAK inhibitor I (4200099; MERCK, Darmstadt, Germany), 0.5–4 μM of PD173074, which inhibits FGF receptor (FGFR) inhibitor (160–26831; Wako Pure Chemical Industries), 10 μM of Y27632, which inhibits ROCK (036–24023; Wako Pure Chemical Industries), and 2 or 4 mM 2-deoxyglucose (2DG, D0051; Tokyo Chemical Industry, Tokyo, Japan).AP and immunological staining of fibroblasts and iPSCsA red-color AP staining kit (AP100 R-1; System Bioscience, Palo Alto, CA, USA) was used to detect AP activity of iPSCs. iPSCs were stained for SSEA-1, SSEA-3, and SSEA-4 antibodies (Supplementary Table 2). To stain the iPSCs with the SSEA antibodies, the cells were fixed in 4% paraformaldehyde in phosphate buffered saline (PBS) for 3 min. Cells were permeabilized by 0.5% Triton X-100 (35501-15; Nacalai Tesque, Kyoto, Japan) for 60 min. After three washes with PBS, the iPSCs were blocked with 1% bovine serum albumin (BSA, 01863-06; Nacalai Tesque) for 45 min. iPSCs were incubated with a primary antibody overnight and then exposed to the corresponding fluorescent-labeled secondary antibodies for 60 min. Counterstaining was performed with a 4′,6-diamidino-2-phenylindole (DAPI) solution (Cellstain-DAPI solution, DOJINDO, Kumamoto, Japan).Japanese golden eagle and chicken-derived fibroblasts were seeded in 12-well cell culture plates for immunological staining. After 48 h of incubation, F-actin staining was performed using Alexa Fluor 568 phalloidin (A12380; Thermo Fisher Scientific) according to the manufacturer’s protocol. Double staining was performed with an anti-vimentin antibody (MA5-11883; Thermo Fisher Scientific) and Alexa Fluor 488-labeled secondary antibody (A-11001; Thermo Fisher Scientific) (Supplementary Table 2). The samples were counterstained with Cellstain-DAPI solution (DOJINDO) as described above.Detection of reprogramming vectors and internal control genes from iPSCsDNA was isolated using the EZ1 DNA Tissue Kit (953034; QIAGEN, Hilden, Germany). PCR was performed with 100 ng of template DNA. Primer sequences are listed in Supplementary Tables 3 and 4. We performed PCR assays using KOD FX Neo (KFX-201; TOYOBO, Osaka, Japan). PCR was conducted by predenaturation at 94 °C for 2 min, denaturation at 98 °C for 10 s, and extension at 68 °C for 30 s, with 40 cycles of denaturation and extension. PCR products were analyzed by electrophoresis on 2.0% agarose/Tris-acetate–ethylenediaminetetraacetic acid (EDTA) gels.Sequential passagingMouse, Okinawa rail, and Japanese ptarmigan-derived primary cells and iPSCs were seeded in six-well plates with feeder cells for analysis. When cell growth became confluent, all cells and the number of cells per dish was enumerated using a Countess cell counter (Thermo Fisher Scientific). The harvested and seeded cell numbers were used to calculate the PD time as an indicator of the speed of cell growth, using the formula PD = log2 (A/B), where A is the number of harvested cells at the end of each passage, and B is the number of seeded cells at the start25.Detection of mRNA expressionTotal RNA was isolated from iPSCs using an EZ1 RNA Tissue Mini Kit (959034; QIAGEN). cDNA was synthesized from total RNA using the PrimeScript reverse transcription (RT) reagent kit (Perfect Real Time, RR047A; TaKaRa Bio, Ohtsu, Japan). Real-time PCR was performed in a 12.5 μl volume containing 2 × KOD SYBR qPCR Mix (QKD-201; Toyobo), 10 ng of cDNA solution, and 0.3 μM of each primer. The primer sequences are listed in Supplementary Tables 5–10. The reaction was performed in duplicate. The cycling program was as follows: 98 °C for 120 s (initial denaturation), 98 °C for 10 s (denaturation), 58 °C for 10 s (annealing), and 68 °C for 32 s (extension) for 40 cycles. We normalized the expression levels of the target genes to that of glyceraldehyde-3-phosphate dehydrogenase (GAPDH).Mitochondria stainingMitochondria were stained by incubation with 50 nM MitoTracker Orange (M7510; Thermo Fisher Scientific) or 20 nM tetramethyl rhodamine ethyl ester perchlorate (TMRE, T669; Thermo Fisher Scientific) for 10 min. After staining, the solution was removed, and fresh medium was added for observation.EB formation and in vitro differentiationIn vitro differentiation of Okinawa rail, Japanese ptarmigan, Blakiston’s fish owl, and Japanese golden eagle iPSCs was performed. To generate EBs, iPSCs were seeded in low-binding dishes in KAv-1 medium. After 7–14 days, floating EBs were selected and seeded in 0.1% gelatin-coated 6-well plates with KAv-1 medium. To induce differentiation into neural cells, the floating EBs were cultured in 0.1% gelatin-coated plates containing KAv-1 supplemented with 10 μM ATRA and 4.0 ng/ml FGF for 7 days.Cells were immunochemically stained after in vitro differentiation using antibody to TUJ1, alpha-smooth muscle, or Gata4 (Supplementary Table 2). Differentiated cells were stained based on the immunological staining procedure of iPSCs described above.Teratoma formation and tissue sectioningThe Animal Committee of Iwate University approved the experimental protocol for teratoma formation (approval numbers A201734, A201737). For teratoma formation, 1 × 106 iPSCs were injected into the testes of SCID mice (C.B-17/Icr-scid/scidJcl; CLEA Japan, Tokyo, Japan). After 4–34 weeks post-injection, tumor tissues were excised from the mice. Each tumor tissue was fixed with 10% formaldehyde in PBS. Fixed tissue sections were stained with hematoxylin-eosin (HE) and observed by microscopy.Immunological staining was performed in addition to HE staining. For immunological staining, antibody to TUJ1, alpha-smooth muscle, or Gata4 was used (Supplementary Table 2). The paraffin block of each teratoma was sliced to produce a section 5 μm thick. After deparaffinization, the antigen was activated with citric acid buffer (SignalStain Citrate Unmasking Solution (10×), 14746; Cell Signaling Technology, Beverly, MA, USA) by microwaving for 10 min. To block endogenous peroxidase, tissue sections were incubated with 3% hydrogen peroxide (081–04215; Wako Pure Chemical). After washing with purified water, the tissue sections were incubated with 5% goat serum (555–76251; Wako Pure Chemical) in PBS. Next, the section were incubated in a solution containing a 1:100 dilution of primary antibody overnight at 4 °C. After washing with PBS, the tissue sections were incubated with horseradish peroxidase (HRP) conjugated secondary antibody (anti-IgG (H+L chain), mouse, pAb-HRP, code no. 330; MBL Co., Ltd., Nagoya, Japan) or anti-IgG (H+L chain, rabbit, pAb-HRP, code no. 458; MBL) for 1 h (Supplementary Table 2). After washing with PBS, the tissue sections were incubated with 3,3′-diaminobenzidine substrate solution (Histostar, code no. 8469; MBL) for 5–20 min. After washing with purified water, tissue sections were counterstained with hematoxylin for 1–2 min.DNA component analysisCultured cells fixed with 70% ethanol at least 4 h under −20 °C condition. The fixed cells stained with the Muse Cell Cycle Assay Kit (Merck Millipore Corporation, Darmstadt, Germany). The stained cells analyzed with Muse Cell Analyzer (Merck Millipore Corporation) were used for DNA content analysis.Karyotype analysisOur iPSCs were treated with 0.02 mg/ml colcemid. Those iPSCs exposed to a hypotonic solution and fixed with Carnoy’s fluid. We counted the chromosomal number in 50 cells and performed a G-banding analysis in 20 cells22.Production of interspecific chimeras and their immunological stainingTo evaluate whether iPSCs derived from Japanese ptarmigan could contribute to the generation of interspecific chimeras in chick embryos, iPSCs were stained with 10 μM CellTracker Green CMFDA (5-chloromethylfluorescein diacetate, C7025; Thermo Fisher Scientific) for 30 min. Eggs of white leghorn chicken were purchased from a local farm (Goto-furanjyo, Gifu, Japan). We injected the labeled Japanese ptarmigan iPSCs into stage X chick blastoderms and cultured the embryos26. To confirm the contribution of chimera, fluorescence was observed after 72 h. To analyze the tissue-level contribution of chimera, embryos on day 5. The embryos were embedded in optimal cutting temperature compound (Sakura Finetek Japan, Tokyo, Japan), frozen in liquid nitrogen, and stored at −80 °C until use. Cryosections 20 μm in thickness were prepared using a cryostat, air-dried for 30 min at room temperature, and fixed with 4% paraformaldehyde for 2 min at room temperature. After washing three times with PBS, sections were incubated with PBS containing 5% FBS for 1 h. After blocking with FBS, the sections were incubated with an anti-hygromicin resistance gene antibody (anti-HPT2; Supplementary Table 2) overnight. After washing three times with PBS, the sections were incubated with secondary antibody (goat anti-mouse IgG, Alexa Fluor 568; Supplementary Table 2) and Cellstain- DAPI solution (DOJINDO) for 1 h.Detection of contribution of chimera from genomeWe injected Japanese ptarmigan iPSCs (without CellTracker Green CMFDA label) into a stage X chicken blastoderms. On day 5, the entire chicken embryos were collected. The genome of each embryo was collected using NucleoSpin Tissue (U0952S; MACHEREY-NAGEL, Düren, Germany). After collecting the chimeric genome, we detected the reprogramming vector cassette using genomic PCR analysis using 50 ng of template genome. To extend the target sequence, we used the KOD FX Neo (KFX-201; TOYOBO). Primer information is provided in Supplementary Table 11. This analysis was performed according to the manufacturer’s protocol. The cycling program comprised 45 cycles of 94 °C for 120 s (initial denaturation), 98 °C for 10 s (denaturation), and 68 °C for 50 s (annealing and extension). After PCR, 2% agarose gel electrophoresis was performed. Gels were stained with GelGreen (517–53333; Biotium, Inc., Fremont, CA, USA).Real-time PCR was also performed to detect the contribution of chimera. The fluorescence probe and primers designed to detect chimeric contributions are summarized in Supplementary Table 12. The template was a 30 ng genome. The analysis was performed using 1 × THUNDERBIRD Probe qPCR Mix (QPS-101; TOYOBO), 0.3 μM of each primer, 0.2 μM of probe, and 1 × Rox. Fifty cycle of 95 °C for 60 s (initial denaturation), 95 °C for 15 s (denaturation), and 60 °C for 60 s (annealing and extension) were used. The expression levels of the target genes were normalized to that of chicken Tsc-2.RNA preparation and sequencing for RNA-seq analysisTotal RNA from iPSCs, fibroblasts, and chicken embryo stage X was collected using NucleoSpin Tissue (740952.50; MACHEREY-NAGEL). Triplicate samples of all iPSCs, fibroblasts, and chicken embryo stage X were prepared. To prepare the library, we used the TruSeq Stranded mRNA LT Sample Prep Kit (RS-122-2101; Illumina, San Diego, CA, USA). The quality of the library was evaluated using the Qubit DNA Assay (Thermo Fisher Scientific) on a TapeStation with a D1000 screen tape (Agilent Technologies, Santa Clara, CA, USA). The cDNA samples were used for the sequencing reaction on an Illumina HiSeq X sequencing machine, resulting in more than 40 M reads with 150 bp ends for each sample, except chicken fibroblast No. 3, which displayed more than 40 M reads with 75 bp ends. To analyze the RNA-seq data, we used the CLC Genomic Workbench (CLC Bio, Aarhus, Denmark). In the trim read step, low-quality sequence with the quality score of the CLC workbench, 5′ end, 3′ end, and short sequences (shorter than 15 sequences) were removed. The trimmed sequence data were mapped onto the chicken reference genome. Gene expression data were obtained in this step. PCA was performed and a heat map created with CLC Genomic Workbench using gene expression data. In this step, normalization was automatically performed using TMM methods. To compare chicken cells, RNA-seq data from SRA (SRP115012 (GEO: GSE102353) and SRP087639 (GSE86592) were used. The RNA-seq data has been submitted to the DNA DataBank of Japan under accession number DRA013522 (Submission), PRJDB13093(BioProject), SAMD00444261–SAMD00444287 (BioSample).Statistics and reproducibilityNonparametric multiple comparison analysis used the Steal–Dwass test (Figs. 2e, 3 [Okinawa rail, Japanese ptarmigan, and Blakiston’s fish owl], 4d, 4f, 5b, 5d, 5f, 5h, 10i). For nonparametric independent two-group analysis, we used the Mann–Whitney U test (Fig. 3, for mouse and chicken, and 4b). Statistically significant differences are indicated by *(p  More

Deforested areas rim a highway running through the state of Amazonas, in Brazil.Credit: Michael Dantas/AFP/Getty

Countries are failing to meet international targets to stop global forest loss and degradation by 2030, according to a report. It is the first to measure progress since world leaders set the targets last year at the 26th United Nations Climate Change Conference of the Parties (COP26) in Glasgow, UK. Preserving forests, which can store carbon and, in some cases, provide local cooling, is a crucial part of a larger strategy to curb global warming.
Tropical forests have big climate benefits beyond carbon storage
The analysis, called the Forest Declaration Assessment, shows that the rate of global deforestation slowed by 6.3% in 2021, compared with the baseline average for 2018–20. But this “modest” progress falls short of the annual 10% cut needed to end deforestation by 2030, says Erin Matson, a consultant at Climate Focus, an advisory company headquartered in Amsterdam, and author of the assessment, published on 24 October.“It’s a good start, but we are not on track,” Matson said at a press briefing, although she cautioned that the assessment looks at only one year’s worth of data. A clearer picture of deforestation trends will emerge in successive years, she added.The assessment, which was carried out by a number of civil-society and research groups, including the World Resources Institute, an environmental think tank in Washington DC, comes as nations gear up for the next big climate summit (COP27), to be held in November in Sharm El-Sheikh, Egypt. Scientists agree that in order to limit global warming to 1.5–2 °C above preindustrial levels — a threshold beyond which Earth’s climate will become profoundly disrupted — deforestation must end.Tropical forests are keyTo track deforestation over the past year, the groups analysed indicators such as changes in forest canopy, as measured by satellite data, and the forest landscape integrity index, which is a measure of the ecological health of forests. The slow progress they found is mainly attributable to a few tropical countries where deforestation is highest (see ‘Progress report’). Among them is Brazil — the world’s largest contributor to tree loss — which saw a 3% rise in the rate of deforestation in 2021, compared with the baseline years. Rates also rose in heavy deforesters Bolivia and the Democratic Republic of the Congo, by 6% and 3%, respectively, over the same period.

Adapted from the 2022 Forest Declaration Assessment

The loss of tropical forests, in particular, is worrisome because a growing body of research shows that besides sequestering carbon, these forests can physically cool nearby areas by creating clouds, humidifying the air and releasing certain cooling molecules. Keeping tropical forests standing provides a massive boost to global cooling that current policies ignore, says a report, “Not Just Carbon”, released alongside the Forest Declaration Assessment.A region made up of tropical countries in Asia is the only one on track to halt deforestation by 2030, according to the assessment (see ‘Movement towards goal’). The region cut the rate at which it lost humid, old-growth forests last year by 20% from the 2018–20 baseline, mostly thanks to large strides made by Indonesia — normally one of the world’s largest contributors to deforestation — where the loss of old-growth forests fell by 25% in 2021 compared with the previous year.

Adapted from the 2022 Forest Declaration Assessment

“The progress we see is driven by exceptional results in some countries,” Matson said.Efforts by the government and corporations in Indonesia to address the environmental harms of palm-oil production were key to progress, the assessment says. For example, as of 2020, more than 80% of palm-oil refiners had promised not to cut down or degrade any more forests. And in 2018, the Indonesian government imposed a moratorium on new palm-oil plantations. But the ban expired last year, raising concerns that progress might eventually be reversed.Finance laggingGlobal demand for commodities such as beef, fossil fuels and timber drive much of the forest loss that occurs today, as industry seeks to clear trees for new pastures and resource extraction. Matson said that many governments haven’t introduced reforms, such as protected-area regulations or fiscal incentives to encourage the private sector to safeguard forests, and that this is stalling progress.“Stronger mandatory action is needed,” she said.
How much can forests fight climate change?
In particular, nations are lagging behind in terms of fiscal support for forest protection and restoration. On the basis of previous assessments, the report estimates that forest conservation efforts require somewhere between US$45 billion and $460 billion per year if nations are to meet the 2030 goal. At present, commitments average less than 1% of what is needed per year, it concludes.Matson said that nations need to improve transparency on financing by setting interim milestones and publicly reporting progress. Michael Wolosin, a climate-solutions adviser at Conservation International, a non-profit environmental organization headquartered in Arlington, Virginia, would like to see donor countries recommit to their forest finance pledges at COP27 this year.However, Constance McDermott, an environmental-change researcher at the University of Oxford, UK, cautions against focusing too much on “estimates of forest cover change and dollars spent”. Social equity for Indigenous people and those in local communities should be part of discussions relating to deforestation, but is mostly missing, she says. These communities are the best forest stewards, and more effort is needed to support them by strengthening land rights and addressing land-use challenges that they identify, she says.Otherwise, McDermott warns that “global efforts to stop deforestation are more than likely to reinforce global, national and local inequalities”. More

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arising from J. Cheng et al. Nature Communications https://doi.org/10.1038/s41467-021-22087-2 (2021)Recently, Cheng J. et al.1 cited and simulated the relative percentage tree cover to interpret the ~3000–4000 years lag between tree cover and East Asian Summer Monsoon (EASM) rainfall. They concluded that vegetation feedback has caused a lagged ecosystem response to EASM rainfall during the Holocene (11.7–0 ka). Here, we question the feasibility of using the relative percentage tree cover to measure vegetation feedback to climate. First, the land cover in northern China includes forests, grasslands, and bare land2. Cheng J. et al.1 did not consider the role of bare land in climate feedback models. Absolute land cover, including forest, grassland, and bare land can accurately reveal feedback to climate3. Second, the biome reconstructions they cited represent changes in vegetation type only, whereas the relationship between vegetation type and vegetation cover is altered by many other factors3,4,5. Third, the paper they cited6 averaged the vegetation types on a millennium scale with an interval of 1000 years, so the view that vegetation has a ~3000–4000 years lag in EASM rainfall is not credible as the lag can be enlarged by data resolution. Therefore, absolute vegetation cover, not relative cover, is a prerequisite for studying ecosystem feedback.Our previous work3 was the first to reconstruct the absolute vegetation cover in northern China based on pollen concentrations in two well-dated sediment cores. Using a random forest method, the vegetation cover at Dali Lake in the forest-steppe transition in northern China was determined for the period from 19,000 cal. yr BP to the present with a resolution of approximately 200 years. Han et al.3 showed that tree cover peaked during the early Holocene and it has gradually declined since the middle Holocene. Pollen percentages are widely used in vegetation reconstruction, but they are challenging to analyze because they can be similar in composition, despite being produced by very different flora7,8. This means they represent the relative fractions of vegetation type and not the absolute vegetation cover (Fig. 1A, B). Han et al.3 suggested that pollen concentration data are suitable for the reconstruction of absolute vegetation cover, particularly in arid and semi-arid regions. Dali Lake represents a typical lake in the semi-arid area of northern China. The random forest model showed that the area had a high tree cover during the early Holocene, which suggests that tree cover was a timely response to Holocene monsoon rainfall and there was no time lag at this specific location. Therefore, the data at Dali Lake challenge the conclusion that tree cover has a ~3000–4000 years lag to EASM rainfall.Fig. 1: Trends in pollen percentage, absolute vegetation cover and fire history during the Holocene at Dali Lake, a typical lake in the semi-arid area of northern China.Changes in the percentages of arboreal and non-arboreal pollen (A)3. Reconstructed absolute tree and grass cover (B)3, with gray, yellow, and green shaded areas indicating the standard deviation of 1000 random forest model results for total cover, grass cover, and tree cover, respectively. Normalized fire activity index in the northern region of eastern monsoonal China (C)10. Z-score of transformed charcoal value showing fire activity trend in the temperate steppe of northern China (D)11.Full size imageMoreover, Cheng J. et al.1 used −17 °C as the threshold for tree and grass transition, but this could be an incorrect citation from Bonan et al.9. In the original text by Bonan et al.9, they showed that both temperate deciduous broadleaved trees and C3 grasses have a tolerance of −17 °C for the coldest month for their survival, but this temperature is not the favored threshold for the shift from grasses to trees. Actually, the trend in absolute vegetation cover was mainly driven by summer temperature, annual precipitation, and fire incidents, which is in line with the vegetation-climate relationships at Dali Lake3. That is, higher monsoon rainfall could increase the competitiveness of trees, while increased fire could increase the competitiveness of grasses as grasses are mostly annual and perennial, and they renew faster than trees after a fire. Between 10,000 and 8000 cal. yr BP, monsoon rainfall peaked and there were relatively few fires, which led to a significant increase in absolute tree cover. Since 6500 cal. yr BP, monsoon rainfall decreased and fire increased, resulting in stronger competition by grasses, which has led to an increased grass cover and reduced tree cover3 (Fig. 1B).The impact of secondary disturbances on vegetation dynamics requires careful consideration, particularly the impact of fires on vegetation cover in semi-arid areas of China, even though vegetation growth is strongly constrained by rainfall in this region. Both the normalized fire index in the northern region of eastern monsoonal China10 and the charcoal value in the temperate steppe of northern China11 show a clear antiphase relationship with the absolute forest cover of the Dali Lake region (Fig. 1B, C, D). However, Cheng J. et al.1 did not discuss fire incidence on vegetation evolution in northern China. Fire has occurred frequently through the Holocene10,12 and it plays an important role in vegetation dynamics based on observational evidence13,14. Particularly, fire is considered as a triggering disturbance that can reduce a forest’s resilience to drought under a drying climate during the mid to late Holocene. For Dali Lake, fire and drying climate has co-driven the evolution of vegetation cover since 6500 cal. yr BP3. Moreover, in northern China, data from Daihai Lake and Hulun Nuur Lake also suggest that fire has accelerated the decline of forest cover and the transition from forest to grass during the Holocene13,14. Unfortunately, Cheng J. et al.1 did not discuss the effect of fire when citing and interpreting the observation data, as scale-dependent fires could make the transition between forest and grassland and their interactions variable.In summary, we believe there are three flaws in the data interpretation of Cheng J. et al.1. (1) They contradict the observed evolution of absolute tree cover at Dali Lake in northern China, which is representative of the marginal area of EASM. Relative percentage tree cover cannot accurately reflect a forest’s response and feedback to past climate change. (2) Both temperate deciduous broadleaved trees and C3 grasses have a tolerance of −17 °C for the coldest month for their survival. Thus, −17 °C is not a correct threshold for the shift from grasses to trees. (3) They contradict the ecological theories of secondary disturbance on vegetation dynamics. Fire and other secondary disturbances may be crucial to the transition between forest and grassland. Interpretations of vegetation feedback might be biased if these important factors are not fully considered. Based on the evidence, their conclusion that vegetation feedback causes lagged ecosystem response to EASM rainfall during the Holocene could be problematic. More