Ecology

Generation timeDuring the infectious period, an infected individual may produce a secondary infection. However, the individual’s infectiousness is not constant during the infectious period, but it can be approximated by the probability distribution of the generation time (GT), which accounts for the time between the infection of a primary case and the infection of a secondary case. Unfortunately, such distribution is not as easy to estimate as that of the serial interval, which accounts for the time between the onset of symptoms in a primary case to the onset of symptoms of a secondary case. This is because the time of infection is more difficult to detect than the time of symptoms onset. Ganyani et al.27 developed a methodology to estimate the distribution of the GT from the distributions of the incubation period and the serial interval. Assuming an incubation period following a gamma distribution with a mean of 5.2 days and a standard deviation (SD) of 2.8 days, they estimated the serial interval from 91 and 135 pairs of documented infector-infectee in Singapore and Tianjin (China). Then, they found that the GT followed a gamma distribution with mean = 5.20 (95% CI = [3.78, 6.78]) days and SD = 1.72 (95% CI = [0.91, 3.93]) for Singapore (hereafter GT1), and with mean = 3.95 (95% CI = [3.01, 4.91]) days and SD = 1.51 (95% CI = [0.74, 2.97]) for Tianjin (hereafter GT2). Ng et al.28 applied the same methodology to 209 pairs of infector-infectee in Singapore and determined a gamma distribution with mean = 3.44 (95% CI = [2.79, 4.11]) days and SD 2.39 (95% CI = [1.27, 3.45]; hereafter GT3). Figure 3 shows the probability density functions (PDF) of such distributions, fGT. The differences between them are remarkable. For example, the 54.5%, 81.0%, and 80.7% of the contagions are produced in a pre-symptomatic stage (in the first 5.2 days after primary infection) assuming GT1, GT2, and GT3, respectively.Figure 3Probability density function of the generation time distribution, fGT(t), of GT1 (blue line; Singapore27), GT2 (yellow line; Tianjin27), GT3 (red line; Singapore28), and GTth (black line; theoretical distribution). Bars are the discretized version, (widetilde{{f_{GT} }}left( n right)), of the PDF of GTth.Full size imageTheoretically, assuming that the incubation periods of two individuals are independent and identically distributed, which is quite plausible, the expected/mean values of the GT and the serial interval should be equal29,30. The mean of the serial interval is easier to estimate than that of the GT. For that reason, we assume a mean serial interval as estimated from a meta-analysis of 13 studies involving a total of 964 pairs of infector-infectee, which is 4.99 days (95% CI = [4.17, 5.82])31, is more reliable than the aforementioned means of the GT. This value is within the error estimates of the means of GT1 and GT2, but not for GT3. Then, we construct a theoretical distribution for the GT that follows a gamma distribution (hereafter GTth) with mean = 4.99 days and SD = 1.88 days. This theoretical distribution can be seen in Fig. 3 and approximates the average PDF of three gamma distributions with mean = 4.99 and the SD of GT1, GT2, and GT3. We assume a conservative CI = [1.51, 2.39] for the theoretical SD, defined with the minimum and maximum SD values of GT1, GT2, and GT3. GTth shows 63.1% of pre-symptomatic contagions.
R

0

from r
In theory, the basic reproduction number R0 can be estimated as far as the intrinsic growth rate r, and the distributions of both the latent and infectious periods are known26,32,33,34. The latent period accounts for the period during which an infected individual cannot infect other individuals. It is observed in diseases for which the infectious period starts around the end of the incubation period, as happened with influenza35 and SARS36. However, from Fig. 3 it is inferred that COVID-19 is transmissible from the moment of infection, and we will assume a null latent period. Then, if the GT follows a gamma distribution, R0 can be estimated from the formulation of Anderson and Watson32, which was adapted to null latent periods by Yan26 as$$ R_{0} = frac{{mean_{GT} }}{{1 – left( {1 + mean_{GT} cdot r cdot frac{1}{{shape_{GT} }}} right)^{{ – shape_{GT} }} }} cdot r, $$
(4)
where meanGT is the mean GT and shapeGT is one of the two parameters defining the gamma distribution, which can be estimated as$$ shape_{GT} = frac{{left( {mean_{GT} } right)^{2} }}{{left( {SD_{GT} } right)^{2} }}. $$
(5)
For GTth, we get R0 = 1.50 (CI = [1.41, 1.61]) for REMEDID I(n) and R0 = 1.76 (CI = [1.60, 1.94]) for official I(n). For the other three GT distributions, R0 ranges from 1.39 (CI = [1.27, 1.58]) to 1.51 (CI = [1.34, 1.80]) for REMEDID I(n) and from 1.59 (CI = [1.40, 1.88]) to 1.78 (CI = [1.51, 2.23]) for official I(n) (Table 1). In all cases, R0 from GTth are within those from the three known GT distributions and indistinguishable from them within the error estimates. The lower (upper) bound of the CI is estimated as the minimum (maximum) R0 obtained from all the possible combinations of 100 evenly spaced values covering the CI of r, meanGT and SDGT. Then, following the Bonferroni correction, the reported CI present at least a 85% of confidence level for GT1, GT2, and GT3, but it cannot be assured for GTth since the CI of its SD is unknown. In general, all these R0 estimates are lower than those summarised by Park et al.20.Table 1 R0 and HIT values of the ancestral SARS-CoV-2 variant estimated from GT1, GT2, GT3, and GTth, and REMEDID and official infections. For date0, “Dec.” means December 2019, and “Jan.” means January 2020.Full size tableAlternatively, R0 can be estimated by applying the Euler–Lotka equation29,33,$$ R_{0} = frac{1}{{mathop smallint nolimits_{0}^{ + infty } e^{ – rt} cdot f_{GT} left( t right)dt}}. $$
(6)
In this case, we get values closer to previous estimates20. In particular, for GTth, we get R0 = 2.12 (CI = [1.81, 2.48]) for REMEDID I(n) and R0 = 2.92 (CI = [2.28, 3.75]) for official I(n). For the other three GT distributions, R0 ranges from 1.63 (CI = [1.43, 1.90]) to 2.21 (CI = [1.59, 2.95]) for REMEDID I(n) and from 1.97 (CI = [1.59, 2.54]) to 3.11 (CI = [1.84, 4.90]) for official I(n) (Table 1). The CI are estimated as in Eq. (4).R0 from a dynamical modelWe designed a dynamic model with Susceptible-Infected-Recovered (SIR) as stocks that accounts for the infectiousness of the infectors. Such a model is a generalisation of the Susceptible-Exposed-Infected-Recovered (SEIR) model37. Births, deaths, immigration and emigration are ignored, which seems reasonable since the timescale of the outbreak is too short to produce significant demographic changes. For the sake of simplicity, the recovered stock includes recoveries and fatalities, and it is denoted as R(t). A random mixing population is assumed, that is a population where contacts between any two people are equally probable. Time is discretized in days, so the real time variable t is replaced by the integer variable n. As a consequence, the derivatives in the differential equations defining the dynamic model explained below are discrete derivatives.The size of the population is fixed at N = 100,000, and then, for any day n we get$$ tilde{S}left( n right) + left( {mathop sum limits_{k = 0}^{20} tilde{I}left( n-k right)} right) + tilde{R}left( n right) = N, $$
(7)
where (tilde{S}left( n right)), (tilde{I}left( n right)), and (tilde{R}left( n right)) are the discretized versions of S(t), I(t), and R(t) and (tilde{I}) is assumed to be null for negative integers. The summation is a consequence of the infectiousness, which is approximated according to the GT, whose PDF is discretized as$$ widetilde{{f_{GT} }}left( n right) = mathop smallint limits_{n – 1}^{n} f_{GT} left( t right) dt, $$
(8)
from n = 1 to 20. Figure 3 shows (widetilde{{f_{GT} }}left( n right)) for GTth. Truncating at n = 20 accounts for 99.99% of the area below the PDF of all the GT. Then, an infected individual at day n0 is expected to produce on average$$ widetilde{{R_{e} }}left( {n_{0} + n} right) cdot widetilde{{f_{GT} }}left( n right) $$
(9)
infections n days later, where (widetilde{{R_{e} }}left( n right)) is the discretized version of Re(t). From this expression, it is obvious that values of (widetilde{{R_{e} }}left( n right) < 1) will produce a decline of infections. Conversely, infections at day n0 are produced by all individuals infected during the previous 20 days as$$ tilde{I}(n_{0} ) = tilde{R}_{e} left( {n_{0} } right) cdot left( {mathop sum limits_{n = 1}^{20} tilde{I}left( {n_{0} - n} right) cdot widetilde{{f_{GT} }}left( n right)} right), $$ (10) whose continuous version has been reported in previous studies29,38. The expression in brackets is called total infectiousness of infected individuals at day n039. According to Eq. (1), Eq. (10) can be expressed in terms of R0 as$$ tilde{I}(n_{0} ) = R_{0} cdot frac{{tilde{S}left( {n_{0} } right)}}{N} cdot left( {mathop sum limits_{n = 1}^{20} tilde{I}left( {n_{0} - n} right) cdot widetilde{{f_{GT} }}left( n right)} right). $$ (11) As we want a dynamic model capable of providing (tilde{I}left( {n_{0} } right)) from the stocks at time step n0 − 1, we replaced (tilde{S}left( {n_{0} } right)) by (tilde{S}left( {n_{0} - 1} right)) in Eq. (11). This assumption makes sense in a discrete domain since the infections at time n0 take place in the susceptible population at time n0 − 1. Then, assuming that all stocks are set to zero for negative integers, our dynamic model can be expressed in terms of Eq. (7) and the following differential equations:$$ delta tilde{I}(n_{0} ) = R_{0} cdot frac{{tilde{S}left( {n_{0} - 1} right)}}{N} cdot left( {mathop sum limits_{n = 1}^{20} tilde{I}left( {n_{0} - n} right) cdot widetilde{{{text{f}}_{GT} }}left( n right)} right) - tilde{I}(n_{0} - 1), $$ (12) $$ delta tilde{S}left( {n_{0} } right) = {-}tilde{I}left( {n_{0} } right), $$ (13) $$ delta tilde{R}left( {n_{0} } right) = tilde{I}left( {n_{0} - 21} right), $$ (14) where (delta tilde{I}), (delta tilde{S}), and (delta tilde{R}) are the (discrete) derivatives of (tilde{I}), (tilde{S}), and (tilde{R}), respectively. Applying the initial conditions (tilde{S}left( 0 right) = N - 1), (tilde{I}left( 0 right) = 1), and (tilde{R}left( 0 right) = 0), it is assumed that the outbreak was produced by only one infector. The latter is not true in Spain, since several independent introductions of SARS-CoV-2 were detected40. However, for modelling purposes it is equivalent to introducing a single infection at day 0 or M infections produced by the single infection n days later. Then, the date of the initial time n = 0 is accounted as a parameter date0, which is optimised, as well as R0, to minimise the root-mean square of the residual between the model simulated (tilde{I}left( n right)) and the REMEDID and official I(n) for the period from date0 to n0.The model was implemented in Stella Architect software v2.1.1 (www.iseesystems.com) and exported to R software v4.1.1 with the help of deSolve (v1.28) and stats (v4.1.1) packages, and the Brent optimisation algorithm was implemented. For REMEDID I(n) and GTth, we obtained date0 = 13 December 2019 and R0 = 2.71 (CI = [2.33, 3.15]). Optimal solutions combine lower/higher R0 and earlier/later date0 (Fig. 4), which highlights the importance of providing an accurate first infection date to estimate R0. When the other three GT distributions were considered, we obtained similar date0, ranging from 12 to 17 December 2019, and R0 values ranging from 2.08 (CI = [1.86, 2.42]) to 2.85 (CI = [2.05, 3.25]; see Table 1). For official infections, date0 was set to 1 January 2020 for all cases, and R0 ranged from 1.81 (CI = [1.64, 2.07]) to 2.41 (CI = [1.80, 2.91]). The CI are estimated as in Eq. (4).Figure 4Root-mean square (RMS) of the residuals between infections from the model, which depends on date0 (x-axis) and R0 (y-axis), and REMEDID (from MoMo ED) and official infections. Parameters optimizing the model are highlighted in purple. RMS larger than 1275 (left panel) and 103 (right panel) are saturated in white.Full size image More

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Genome sequencing and assemblyGenome assemblies ranged in size from 799.9 Mbp in Melanocharis versteri to 1053.5 Mbp in Sericornis nouhuysi. The number of scaffolds ranged from 14,086 scaffolds in Melipotes ater to 87,957 scaffolds in Ficedula hyperythra and N50 ranged between ca. 40 Kbp to and 25 Mbp. Benchmarking Universal Single-Copy Orthologs (BUSCO) analyses of genome completeness ranged from a high proportion of complete BUSCOs in Melipotes ater, 86.8% to only 66.7% complete BUSCOs in Rhipidura albolimbata. For most species, the proportions of complete BUSCOs were 75–80%. Overall, the proportion of missing BUSCOs was low, ranging from 6.6% in Melipotes ater to 15.2% in Rhipidura albolimbata (see Supplementary Table 1 for all genome assembly statistics and Supplementary Fig. 1 for the number of SNP variants per species).Kinship analyses of individuals within populationsSampling of closely related individuals can dramatically bias estimates of population structure and demographics. Two Pachycephala schlegelii individuals (A117 and A118) showed a pairwise kinship coefficient of 0.144, indicative of being half-siblings. The two individuals were collected at the same locality on the same date. Similarly, two Ifrita kowaldi individuals (D116 and D117) showed a pairwise kinship coefficient of 0.135, also suggestive of being half-siblings. In this case, the individuals were collected on the same sampling locality on two consecutive days. To not bias downstream demographic analyses, one of the P. schlegelii (A118) and one of the I. kowaldi (D117) individuals were excluded from all subsequent analyses. For all other species, no closely related individuals were identified.Genetic differentiationEstimated levels of differentiation between populations were initially based on three approaches; (i) calculation of FST (the fixation index), which quantifies the degree of genetic differentiation between populations based on the variation in allele frequencies, ranging between 0 (complete mixing of individuals) and 1 (complete separation of populations) (Fig. 1), (ii) Standardized pairwise FST used to conduct a Principal Component Analysis (PCA) in order to visualize population structure (Supplementary Fig. 1) and (iii) Admixture analysis as implemented in STRUCTURE (a clustering algorithm that infers the most likely number of groups [K]), in which individuals are grouped into clusters according to the proportion of their ancestry components (Supplementary Fig. 1). As a preliminary analysis, we calculated FST and constructed PCA plots for the four congeneric (incl. Sericornis/Aethomyias [until recently placed in the genus Sericornis]) species pairs in our study (Supplementary Fig. 2), which were aligned using the same reference genome. This was done to ascertain that no samples had been misidentified and to gauge levels of differentiation between distinct species. All species were genetically well separated and FST values ranged from 0.08 for the two Ptiloprora species to 0.20 for the two Ficedula species.For five out of six species from Buru/Seram, genetic differentiation (FST) was high between islands (Fig. 1), and comparable to differentiation between named congeneric species in this study (e.g. Ptiloprora and Melipotes); Ceyx lepidus (FST = 0.16), Thapsinillas affinis (FST = 0.15), Ficedula buruensis (FST = 0.13) and Pachycephala macrorhyncha (FST = 0.09). In contrast, differentiation in Ficedula hyperythra was consistent with population-level differentiation (FST = 0.04). In all cases, individuals from Buru and Seram were clearly differentiated in the PCA and STRUCTURE plots (Supplementary Fig. 1A). For Ceyx lepidus, Ficedula buruensis and Pachycephala macrorhyncha, samples were collected at multiple elevations and we therefore calculated genetic differentiation between elevations (Buru: 1097 m versus 1435 m and Seram: 1000 m versus 1300 m) to determine any potential parapatric differentiation along the gradients. In all possible comparisons, FST values did not differ significantly from 0. Moreover, PCA plots showed that samples did not cluster according to elevation (Supplementary Fig. 3A).Three of the thirteen New Guinean population pairs occurring in Mount Wilhelm and Huon showed relatively high genetic divergences: Melipotes fumigatus/ater (FST = 0.08), Paramythia montium (FST = 0.09) and Ifrita kowaldi (FST = 0.07) (Fig. 1) with populations clearly separated (Supplementary Fig. 1). By contrast, the two lowland species Toxorhamphus novaeguineae and Melilestes megarhynchus showed little genetic differentiation, FST = 0.00. For the remaining species, genetic differentiation between Mount Wilhelm and Huon ranged between FST = 0.01–0.05. Despite this moderate level of genetic differentiation, the populations of Mount Wilhelm and Huon could be clearly distinguished in the PCA plots. In all cases STRUCTURE suggested a scenario with K = 2 with some mixing of individuals, except for Rhipidura albolimbata, in which K = 1 was suggested.For five bird species we included an additional population from Mount Scratchley, which is also situated in the Central Range but ~400 km to the southeast of Mount Wilhelm. Genetic differentiation of this population from the other two populations was comparable with that between Mount Wilhelm and Huon. The highest genetic differentiation was found in Paramythia montium (FST = 0.10 both between Mount Wilhelm and Mount Scratchley and between Huon and Mount Scratchley). In the case of Peneothello sigillata, the Mount Scratchley population appeared genetically well-differentiated from both the populations of Mount Wilhelm (FST = 0.06) and Huon (FST = 0.07). In both cases, STRUCTURE suggested a scenario of K = 3, with individual assignments matching the three geographically circumscribed populations. For Pachycephala schlegelii, genetic differentiation was relatively high between Huon and Mount Scratchley (FST = 0.05), but low between Mount Wilhelm and Mount Scratchley (FST = 0.01). Accordingly, STRUCTURE suggested a scenario with K = 2 groups. For the remaining two species Sericornis nouhuysi showed some differentiation (FST = 0.03) between Mount Wilhelm and Huon and Aethomyias papuensis showed minor differentiation (FST = 0.02 between Mount Scratchley and Huon (Supplementary Table 2), but for both species, STRUCTURE suggested a scenario of K = 2 with considerable mixing of individuals between populations.Samples from Mount Wilhelm were collected at elevations ranging from 1700 to 3700 m, again allowing us to test for differences within populations on a single slope, a finding that would be consistent with incipient parapatric speciation. No species showed significant differences in FST when comparing individuals from different elevations, and concordantly there was little clustering of individuals by elevation in the PCA plots. Even when individuals were collected as far as 2000 elevational meters apart (as in the case of Origma robusta), genetic differentiation was low (FST = 0.01). In Huon, all samples were collected at the same elevation, except for Ifrita kowaldi, for which genetic differentiation of FST = 0.03 was found between individuals collected at 2300 m and 2950 m (Supplementary Fig. 3B, Supplementary Table 2). These analyses however, suffer from very small sample sizes that hinder a thorough analysis of parapatric speciation events. Furthermore, we note that divergence with gene flow may not manifest as a genome-wide phenomenon (at least, not until the taxa are so differentiated that gene flow has ceased). Instead, it may proceed via selection acting to create small ‘islands of differentiation’ within the genome against a background of negligible differentiation22,23. Such analyses require large sample sizes and are therefore not possible herein.Correlations between genetic divergence and elevationIf lineages colonize mountains from the lowlands, followed by range contraction and differentiation in the highlands, we would expect a signature of larger genetic differentiation (FST) between populations inhabiting higher elevations. We found no relationship between genetic differentiation (FST) and the altitudinal floor (the lowest elevation at which a species/population occurs) for the five Moluccan species, but for all New Guinean taxa with the exception of Melipotes fumigatus/ater we found a significant positive correlation (r = 0.83, p  More

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General characterization of seven newly isolated HMO-2011-type phagesIn this study, we used four Roseobacter strains (FZCC0040, FZCC0042, FZCC0012, and FZCC0089) and one SAR11 strain (HTCC1062) to isolate phages. FZCC0040 and FZCC0042 belong to the Roseobacter RCA lineage [22], FZCC0012 shares 99.8% 16S rRNA gene identity with Roseobacter strain HIMB11 [57], and FZCC0089 belongs to a newly identified Roseobacter lineage located close to HIMB11 and SAG-019 lineages (Supplementary Fig. 1).A total of seven phages were newly isolated and analyzed in this study (Table 1). The complete phage genomes range in size from 52.7 to 54.9 kb, harbor 62 to 84 open reading frames (ORFs), and feature a G + C content ranging from 33.8 to 48.6%. Compared to other HMO-2011-type phages, pelagiphage HTVC033P has a relatively lower G + C content of 33.8%, similar to the G + C content of its host HTCC1062 (29.0%) and of other described pelagiphages [21, 26,27,28]. The G + C content of other six roseophages ranges from 42.2 to 48.6%, which is also similar to the G + C content of the hosts they infect (44.8 to 54.1%).Despite their distinct host origins, these phage genomes show considerable similarity in terms of gene content and genome architecture (Fig. 1). They all display clear similarity with the previously reported SAR116 phage HMO-2011 [20] and HMO-2011-type RCA phages [22]. Overall, these phages share 19.2 to 79.1% of their genes with previously reported HMO-2011-type phages and all contain homologues of HMO-2011-type DNA replication and metabolism genes, structural genes, and DNA packaging genes. Moreover, their overall genome structure is conserved with that of HMO-2011-type phages. Considering these observations, we tentatively classified these seven phages into the HMO-2011-type group. Of the 11 currently known HMO-2011-type isolates, one infects the SAR116 strain IMCC1322, one infects the SAR11 strain HTCC1062, and the remaining nine all infect Roseobacter strains; this suggest that HMO-2011-type phages infect diverse bacterial hosts. HTVC033P is the first pelagiphage identified to belong to the HMO-2011-type viral group. Our study has also increased the number of known types of pelagiphages. To date, pelagiphages belonging to a total of nine distinct viral groups have been isolated and analyzed [21, 26,27,28].Fig. 1: Alignment and comparison of genomes of HMO-2011-type isolates and representative HMO-2011-type MVGs from major subgroups.HMO-2011-type phage isolates are shown in red. Phages isolated in this study are indicated with red asterisks. Predicted open reading frames (ORFs) are represented by arrows, with the left or right arrow points indicating the direction of their transcription. The numbers inside the arrows indicate ORF numbers. ORFs annotated with known functions are marked using distinct colors according to their functions. HMO-2011-type core genes are indicated with blue asterisks. The color of the shading connecting homologous genes indicates the level of amino acid identity between the genes. To clearly present the genomic comparison, several MVGs were rearranged to start from the same gene as in the HMO-2011-type phages. DNAP DNA polymerase, Endo endonuclease, RNR ribonucleoside-triphosphate reductase, PhoH phosphate starvation-inducible protein, MazG MazG nucleotide pyrophosphohydrolase domain protein, ThyX thymidylate synthase, GRX glutaredoxin, TerS terminase small subunit, TerL terminase large subunit.Full size imageIdentification and sequence analyses of HMO-2011-type MVGsTo identify HMO-2011-type MVGs, we performed a metagenomic mining and retrieved a total of 207 HMO-2011-type MVGs (≥50% genome completeness) from viromes in the worldwide ocean, from tropical to polar oceans (Supplementary Table 1). These MVGs range in size from 29.2 to 67.9 kb and their G + C content range from 31.3 to 52.4%. In addition, 45 HMO-2011-type MVGs were also identified from some non-marine habitats, suggesting that HMO-2011-type phages are widely distributed worldwide (Supplementary Table 1).Genomic analysis confirmed that all HMO-2011-type MVGs exhibit genomic synteny with HMO-2011-type phages (Fig. 1). Although some of these HMO-2011-type MVGs are highly similar to their cultivated relatives, most MVGs appear to have more genomic variations. To resolve the evolutionary relationship among the HMO-2011-type phages, a phylogenetic tree was constructed based on the concatenated sequences of five core genes. We found that HMO-2011-type phages are evolutionarily diverse and can be separated into at least 10 well-supported subgroups ( >2 members), with 140 MVGs clustering into previously identified HMO-2011-type groups (subgroups I and III in Fig. 2A) [22], and the remaining 67 MVGs forming new subgroups (Fig. 2A). Among these HMO-2011-type subgroups, three contain cultivated representatives (subgroups I, III, and IX). Subgroup I contains the greatest number of phages, including six cultivated representatives and 123 MVGs (Fig. 2A). The cultivated representatives in subgroup I include a phage that infect SAR116 strain and five phages that infect Roseobacter strains. Subgroup III contains four cultivated representatives that infect two Roseobacter strains, and 17 MVGs. Pelagiphage HTVC033P and nine MVGs form subgroup IX. Other subgroups have no cultivated representatives yet. The results of phylogenomic analysis showed that subgroups I to VI are closely related, whereas subgroups VII to X are located on a separate branch and are more distinct from the subgroups I to VI, which suggests that these subgroups are more evolutionarily distant. A phylogenomic-based approach with GL-UVAB workflow [53] was also performed to cluster these HMO-2011-type genomes, which showed similar grouping results (Supplementary Fig. 2).Fig. 2: Phylogenomic and shared-gene analyses of HMO-2011-type phages.A A maximum-likelihood tree was constructed using concatenated sequences of five hallmark genes. HMO-2011-type phages were grouped into 10 subgroups based on the phylogeny. Shading is used to indicate the subgroups. HMO-2011-type phage isolates are shown in red. Genomes containing an integrase gene are indicated by red triangles. The G + C content and completeness of the genomes are indicated. Scale bar indicates the number of amino acid substitutions per site. B Heatmap showing the percentage of shared genes between HMO-2011-type genomes. Phages in the same subgroup are boxed.Full size imageA previous study suggested the use of the percentage of shared proteins as a means of defining phage taxonomic ranks and proposed that phages with ≥20 and ≥40% orthologous proteins in common can be grouped at the taxonomic ranks of subfamily and genus, respectively [58]. Overall, most of the calculated percentages between HMO-2011-type genomes fall within the 20 to 100% range and most of the percentages between genomes within the same subgroup fall within the 40 to 100% range (Fig. 2B). Therefore, our results suggest that the HMO-2011-type is roughly a subfamily-level phage taxonomic group containing at least ten genus-level subgroups in the Podoviridae family.Conserved genomic structure and variation in HMO-2011-type phagesOf the 1235 orthologous protein groups (≥2 members) identified in HMO-2011-type genomes, only 254 proteins groups could be assigned putative biological functions (Supplementary Table 2). Comparative genomic analysis clearly revealed the conserved functional module structure of all HMO-2011-type genomes. All HMO-2011-type phage genomes can be roughly divided into the DNA metabolism and replication module, structural module and DNA packaging module (Fig. 1). Most of the homologous genes are scattered in similar loci of the HMO-2011-type genomes. Core genome analysis based on complete HMO-2011-type genomes revealed that HMO-2011-type genomes share a common set of ten core genes (Fig. 1). These core genes are mostly genes related to essential function in phage replication and development, including genes encoding DNA helicase, DNA primase, DNA polymerase (DNAP), portal protein, capsid protein, and terminase small and large subunits (TerL and TerS) as well as several genes with no known function, suggesting that phages in this group employ similar overall infection and propagation processes (Fig. 1).Most members in subgroups I and III and one member in subgroup II possess a tyrosine integrase gene (int) located upstream of the DNA replication and metabolism module, whereas all subgroup IV to X genomes contain no identifiable lysogeny-related genes. This result suggests that members of subgroups IV to X might be obligate lytic phages. Integrase genes typically occur in the genomes of temperate phages and are responsible for site-specific recombination between phage and host bacterial genomes [59, 60]. In subgroup III, RCA phage CRP-3 has been experimentally demonstrated to be capable of integrating into the host genome [22]. Thus, certain int-containing HMO-2011-type phages are also likely to be temperate phages.In the DNA metabolism and replication modules, genes encoding DNA primase, DNA helicase, DNAP, ribonucleotide reductase (RNR), and endonuclease can be identified; and DNA helicase, DNA primase, and DNAP are core to all HMO-2011-type phages. All reported HMO-2011-type phages contain an atypical DNAP, in which a partial DnaJ central domain is located between the exonuclease domain and the DNA polymerase domain [20, 22]. The Escherichia coli DnaJ protein, a co-chaperone [61], has been shown to be involved in diverse functions [62] and to be critical for the replication of phage Lambda [63,64,65]. The sequence analysis revealed that DNAP sequences of these seven new HMO-2011-type phages and 207 MVGs also present this unusual domain structure and contain two repeats of the CXXCXGXG motifs involved in zinc binding [66] in the partial DnaJ domain (Supplementary Fig. 3). RNR gene is frequently detected in subgroups I, II, III, IV, V, and X genomes but not in the other subgroup genomes. RNRs, which are widely distributed in diverse phage genomes, are involved in catalyzing the reduction of ribonucleotides to deoxyribonucleotides, and thus play a crucial role in providing deoxyribonucleoside triphosphates for phage DNA biosynthesis and repair [67,68,69]. RNR genes clustered with the RNR gene in phage HMO-2011 were previously reported to dominate the class II viral RNRs in examined marine viromes [69]. In the remaining two modules, genes involved in phage structure (e.g., genes encoding capsid and portal proteins), packaging of DNA (TerL and TerS genes), and cell lysis were detected. The proteins encoded by these genes play key roles in phage morphogenesis and virion release.Examination of the distribution of the orthologous groups among the subgroups revealed clear pan-genome differences in various subgroups (Fig. 3). Most subgroups harbor subgroup-specific genes not identified in other subgroups, although  no function has yet been assigned to most of these genes. Notably, the phages in subgroups VII, VIII, and IX possess genomic features that differentiate them from phages in other subgroups, specifically with regard to the G + C content and gene content. The members of these three subgroups are closely related to each other in the phylogenetic tree and harbor several subgroup-specific genes. The G + C content of the phage genomes in these subgroups ranges from 31.9 to 35.4%, significantly smaller than other subgroups but similar to the G + C content of SAR11 bacteria and other known pelagiphages. HTVC033P is the only cultivated representative of subgroup IX. The aforementioned results suggest that the phages in subgroup VII, VIII, and IX might have related bacterial hosts and are highly likely to be pelagiphages. The host prediction using RaFAH tool also assigned Pelagibacter as their potential hosts (Supplementary Table 1). Subgroup X is located near these three subgroups in the phylogenetic tree, and the G + C content of the phages in this subgroup ranges from 34.4 to 39.0%. The host prediction assigned Roseobacter as their potential hosts. The hosts of this subgroup still remain to be experimentally investigated.Fig. 3: Distribution and functional classification of orthologous protein groups across HMO-2011-type genomes.Only orthogroups containing >10 members or showing subgroup-specific features are shown. Subgroup-specific genes are boxed in red. Genes that are absent in a specific subgroup are boxed in orange.Full size imageMetabolic capabilities of HMO-2011-type phagesAll HMO-2011-type phage genomes harbor several host-derived auxiliary metabolic genes (AMGs) potentially involved in diverse metabolic processes. Some AMGs in HMO-2011-type phages have been discussed previously [20, 22].Subgroups VII, VIII, IX, and X possess distinct AMGs as compared with the other subgroups. For example, the genes encoding FAD-dependent thymidylate synthase (ThyX, PF02511) and MazG pyrophosphohydrolase domains are absent in all subgroups VII, VIII, IX, and X genomes but frequently detected in other subgroup genomes. ThyX protein is essential for the conversion of dUMP to dTMP mediated by an FAD coenzyme and is therefore a key enzyme involved in DNA synthesis [70, 71]. The thyX gene is commonly found in microbial genomes and phage genomes. Phage-encoded ThyX has been suggested to compensate for the loss of host-encoded ThyA and thus play crucial roles in phage nucleic acid synthesis and metabolism during infection [72]. Except in the case of subgroups VII, VIII, IX, and X genomes, the mazG gene, which encodes a nucleoside triphosphate pyrophosphohydrolase is sporadically distributed in HMO-2011-type genomes. MazG protein is predicted to be a regulator of nutrient stress and programmed cell death [73] and has been hypothesized to promote phage survival by keeping the host alive during phage propagation [74]. The Escherichia coli MazG can interfere with the function of the MazEF toxin–antitoxin system by decreasing the cellular level of (p)ppGpp [73]. However, a recent study showed that a cyanophage MazG has no binding or hydrolysis activity against alarmone (p)ppGpp but has high hydrolytic activity toward dGTP and dCTP, and it was speculated to play a role in hydrolyzing high G + C host genome for phage replication [75]. Whether the MazG proteins encoded by HMO-2011-type phages play a similar role in phage propagation remained to be investigated.Five MVGs in subgroup I contain a gene encoding a DraG-like family ADP-ribosyl hydrolase (ARH). In cellular ADP-ribosylation systems, ARH catalyzes the cleavage of the ADP-ribose moiety, and thereby counteract the effects of ADP-ribosyl transferases [76]. It has been reported that ARH in Rhodospirillum rubrum regulates the nitrogen fixation [77]. However, the function of this phage-encoded ARH in the phage propagation process remains unclear.We also observed that several MVGs possess genes involved in iron–sulfur (Fe–S) cluster biosynthesis, including an Fe–S cluster assembly scaffold gene (iscU) that involved in Fe–S cluster assembly and transfer [78] and an Fe–S cluster insertion protein gene (erpA). Fe–S cluster participates in a wide variety of cellular biological processes [79]. The discovery of these genes suggests that these phages may play important roles in Fe–S cluster biogenesis and function.The gene encoding sodium-dependent phosphate transport protein (PF02690) has been identified in eight subgroup I genomes. The Na/Pi cotransporter family protein is responsible for high-affinity, sodium-dependent Pi uptake, and thus the protein plays a critical role in maintaining phosphate homeostasis [80]. This gene might function in the transport of phosphate into cells during phage infection. The presence of Na/Pi cotransporter genes suggests that some HMO-2011-type phages may have the potential to regulate host phosphate uptake in phosphate-limited ocean environments in order to benefit phage replication and propagation.Identification and phylogenetic analysis of HMO-2011-type DNAPsThe genetic diversity and geographically distribution of HMO-2011-type phages in marine environments was further inferred from DNAP gene analyses. A total of 2433 HMO-2011-type DNAP sequences with sequence sizes ranging from 540 to 779 amino acids were identified and subjected to phylogenetic analysis (Supplementary Table 3).Among the identified HMO-2011-type DNAPs, 2030 sequences were retrieved from the GOV 2.0 Tara expedition upper-ocean viral populations (0–1000 m), from tropical to polar regions. HMO-2011-type DNAP genes were identified from all analyzed upper-ocean viromes, suggesting the global prevalence of HMO-2011-type phages in upper oceans.A previous study revealed that marine viromes contain various types of tailed phage genomes that encode a family A DNAP gene [81]. To estimate the importance of HMO-2011-type phages, we calculated the proportion of HMO-2011-type DNAPs based on the number of HMO-2011-type DNAP sequences and the total number of family A DNAP sequences ( >470 aa) in each GOV 2.0 viral population dataset. This analysis revealed that HMO-2011-type DNAPs accounted for up to 19.7% of all family A DNAPs in each GOV 2.0 dataset (Supplementary Table 4). We found that the HMO-2011-type DNAP sequences appear to be more dominant in epipelagic viromes than in mesopelagic viromes (p  More

Deer capture and samplingWe captured 100 mule deer (54 females, 46 males) during November 2018–February 2019, avoiding capture and sampling of juveniles. We attempted to distribute captures throughout the ~23 km2 study area described by Miller et al.5 to minimize spatial disparities in comparing contemporary and past data, and to assure marks were widely distributed for December ground counts to estimate deer abundance5,35,36. Field and sampling methods generally followed those used elsewhere5,31,37. Field procedures were reviewed and approved by the CPW Animal Care and Use Committee (file 14–2018).We pursued deer on foot and darted them opportunistically, delivering sedative combinations intramuscularly via projectile syringe. Premixed immobilization drug combinations included either nalbuphine (N; 0.9 mg/kg) or butorphanol (B; 0.5 mg/kg) combined with azaperone (A; 0.2 mg/kg) and medetomidine (M; 0.2 mg/kg)38, with standard total doses for respective combinations based on an estimated mass of 70 kg (average drug volume per animal was 1.3 ml NMA, 1.4 ml BAM). We collected rectal mucosa biopsies to determine CWD infection status37. We also collected whole blood and marked all deer with individually identifiable ear tags and some with telemetry (n = 51) or visual identification (n = 12) collars. Ages were estimated to the nearest year via tooth replacement and wear patterns39; observers used a pocket reference guide in the field to help assure consistency. To antagonize sedation upon completion of handling and sampling, each deer received 5 mg atipamezole/mg M administered, injected intramuscularly.Prion diagnosticsFormalin-fixed tissue biopsies were processed and analyzed by immunohistochemistry (IHC) at the Colorado State University Veterinary Diagnostic Laboratory (Fort Collins, Colorado USA; CSUVDL) for evidence of CWD-associated prion (PrPCWD) accumulations using monoclonal antibody F99/97.6.1 (VMRD Inc., Pullman, Washington, USA)40 and standard IHC methods24,37,41, except that the CSUVDL’s IHC staining machine (Leica Microsystems Inc., Buffalo Grove, Illinois, USA) was different from that used in earlier studies (Ventana Medical Systems, Oro Valley, Arizona, USA). Biopsies were evaluated microscopically and classified as positive (infected) or not detected (negative) based on PrPCWD presence or absence; the same pathologist (T. R. Spraker) read biopsies for both the current and prior5 studies.We included only data from deer with biopsies providing ≥3 lymphoid follicles in analyses involving infection status in order to maintain a relatively high (≥90%) probability of detecting infected individuals24. Two animals with low follicle counts that died shortly after capture were excepted by substituting postmortem IHC results. Limiting the acceptable follicle count excluded seven females (two 225SS, five 225SF) and two males (one 225SS, one 225SF) from some analyses. One male deer was 225FF and one female deer was missing a blood sample and thus not assigned to a PRNP gene group; these two individuals also were excluded from some analyses (e.g., Table 1).
PRNP genotypingWe used DNA extracted from whole blood buffy coat aliquots (n = 99) to screen for the presence of sequences at PRNP gene codon 225 that encode for serine (S) and/or phenylalanine (F) in the mature prion polypeptide, classifying individuals as 225SS, 225SF, or 225FF16,36,42. Methods generally followed those described by Jewell et al.16. Briefly, we extracted DNA using the DNeasy® blood and tissue kit (Qiagen, Valenica, California, USA). We amplified the complete open reading frame (ORF) plus 25 bp of 5′ flanking sequences and 53 bp of 3′ flanking sequences in the PRNP coding region using polymerase chain reaction (PCR). Purified DNA was combined in a 0.2 ml PCR tube containing a puReTag Ready-To-Go PCR bead (illustra™, GE Healthcare Bio-Sciences Corp, Piscataway, New Jersey, USA). Each PCR bead contained 2.5 units puReTag DNA polymerase, 10 mM Tris-HCI, 50 mM KCl, 1.5 mM MgCl2, 200 µM of each deoxynucleoside triphosphate, and stabilizers, including bovine serum albumin. For each PCR assay, 1 μL of each primer at 200 nM, 22 μL of RNase-free water and 1 μL of approximately 100 ng total genomic DNA was added for a final volume of 25 μL. Primers used for amplification were forward (MD582F, 5′-ACATGGGCATATGATGCTGACACC-3′) and reverse (MD1479RC, 5′-ACTACAGGGCTGCAGGTAGATACT-3′) described by Jewell et al.16. Reactions were thermal-cycled in a PTC 100 (MJ Research) at 94 C for 5 min and then 32 cycles of 94 C for 7 s, 62 C for 15 s, 72 C for 30 s and a final cycle of 72 C for 5 min, and kept at 4 C until inspected for successful amplification by agarose gel electrophoresis. As confirmed by LaCava et al.19, the MD582F and MD1479RC primers developed by Jewell et al.16 specifically amplify the functional PRNP gene ORF, thereby excluding confounding effects that could arise from the presence of a processed pseudogene that occurs in a majority of deer (Odocoileus spp.)42.We used EcoRI restriction digestion of the PCR-amplified PRNP region16—a validated assay targeting the singular polymorphism at codon 225 in mule deer—to screen all 99 samples for presence of S or F codons. Aliquots (10 μl) of completed PCR reactions were incubated with 10 U EcoRI (New England Biolabs) in a total volume of 12 μl containing 50 mM NaCl, 100 mM Tris/HCl, 10 mM MgCl2, 0.025% Triton X-100 (pH 7.5) at 37 C for 2–16 h followed by the addition of 2.5 μl 6× concentrate gel loading solution (Sigma- Aldrich) per sample, and the inspection of products by agarose gel electrophoresis for the presence of one 897bp-sized band for 225SS, two bands—one 897 bp and one 719 bp—for 225SF, or one 719 bp-sized band for 225FF. As noted by Jewell et al.16, occurrence of TTC (the F codon) at position 225 creates an EcoRI recognition DNA sequence and cleavage site GAATTC from codons 224–225, whereas TCC (the S codon) creates the sequence GAATCC, which is not cut by EcoRI. When incubated with EcoRI, PCR products with a TTC codon at position 225 yielded cleavage fragments of the predictable sizes listed16. Because no other sites within the PRNP ORF DNA sequence are potentially transformable to GAATTC with one base change, this represents a specific genotyping method for assessing the S225F polymorphism in mule deer16.To confirm findings from EcoRI screening, we examined sequences of the complete PRNP ORF from 20 samples that showed evidence of cleavage indicating 225*F and 6 samples without cleavage identified as 225SS. For DNA sequencing, we used primers 245 (5′-GGTGGTGACTGACTGTGTGTTGCTTGA-3′), 12 (5′-TGGTGGTGACTGTGTGTTGCTTGA-3′) and 3FL1 (5′-GATTAAGAAGATAATGAAAACAGGAAGG-3′; Integrated DNA Technologies). Sanger sequencing was done on purified PCR product by Eurofins Genomics (Louisville, Kentucky, USA). Sequence chromatograms were viewed and DNA sequence alignments and comparisons were made using the MAFFT multiple sequence alignment program v7.450 module, software platform v2020.2.3 of Geneious Prime. Sequencing confirmed the presence of coding for F in all samples identified as 225*F by EcoRI digestion, as well as the absence of such coding in samples identified by EcoRI digestion as 225SS. Moreover, presence of AGC at codon 138 in all sequenced samples reconfirmed that the primers we used had amplified the functional PRNP gene42.Statistics and reproducibilityFor analyses, we tabulated IHC-positive and -negative results to estimate apparent prevalence of prion infection. We also tabulated the number of individuals assigned to PRNP genotypes and to age groupings as described. Age groupings were selected based on relevance to CWD epidemiology in mule deer1,5,8,12,16,17,18,20,24,31,37. Assuming a ~2-year disease course5,8,17 and relative scarcity of end-stage disease in 225SS deer More

NEWS
11 January 2022

Landmark Colombian bird study repeated to right colonial-era wrongs

A re-run of a 100-year-old, US-led bird survey will inform future conservation efforts — but be helmed by local researchers.

Luke Taylor

Luke Taylor

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Ornithologist Andrés Cuervo takes a selfie of a team of Colombia Resurvey Project researchers during an expedition in Caquetá.Credit: Andrés M. Cuervo

Colombia has more animal and plant species per square kilometre than anywhere else in the world. Pioneering US bird scientist Frank Chapman once said that the country was so rich in biodiversity that when his research team explored the area in the early twentieth century, it could have studied a single mountain range for five years and still not have mapped all of its fauna.More than 100 years later, Colombian researchers are redoing his legendary bird survey, which is a reference for ornithologists the world over. They are surveying the areas that Chapman catalogued between 1911 and 1915, to investigate how a century of war, global warming and industrialization has affected the landscape and its biodiversity.
The world’s species are playing musical chairs: how will it end?
But this project will not snatch birds and whisk them to a museum abroad — as Chapman’s team did. Instead, local scientists will keep specimens in Colombia and engage with local communities during their expeditions, to include them in the momentous endeavour, improve the quality of the research and set an ethical standard for future fieldwork.Chapman and at least 5 other collectors shot many of the nearly 16,000 birds that they hauled back to the American Museum of Natural History in New York City, offering local residents little explanation — or credit. “You wouldn’t like it if I came to your house, surveyed it without permission, took photos and then went back to Colombia without telling you what I had found,” says Nelsy Niño-Rodríguez, the Colombia Resurvey Project’s community-relations coordinator, who is an ornithologist at the Alexander von Humboldt Biological Resources Research Institute in Bogotá. Without local guides knowledgeable about Colombia and its birds, Chapman couldn’t possibly have located and collected so many specimens, says Natalia Ocampo-Peñuela, a research partner on the resurvey project and a conservation ecologist at the University of California, Santa Cruz. Yet Chapman’s logs hardly mention guides; when they are discussed, it’s usually in racist or pejorative terms, she says.“His interest was to feed his curiosity, his scientific intellect and the museum,” she adds, but not to inform the wider population — and definitely not the local populations.A changed landscape Colombian researchers have dreamt of re-running Chapman’s expeditions for decades. But it wasn’t possible until the past few years, because many areas were inaccessible owing to armed conflict. Following a landmark peace deal in 2016, remote regions that had been under the control of the Revolutionary Armed Forces of Colombia (FARC), a left-wing guerrilla group, once again opened to exploration. That, and an infusion of funding from the Colombian government and international donors, meant researchers could attempt a resurvey.

Birds of Colombia: top, many-banded araçari (Pteroglossus pluricinctus); left, pileated finch (Coryphospingus pileatus); right, white-fringed antwren (Formicivora grisea).Credit: Andrés M. Cuervo

Chapman visited Colombia because he thought that its geography made it one of the most biodiverse places in the world. He theorized that the presence of the Andes Mountains, combined with the country’s position bridging South and Central America, made it an evolutionary melting pot.
FARC and the forest: Peace is destroying Colombia’s jungle — and opening it to science
Although Colombia is still home to around 10% of the world’s biodiversity, the forests once explored by Chapman have changed immensely. Pristine jungles have been cleared to create uniform pastures resembling golf courses, says Andrés Cuervo, an ornithologist at the National University of Colombia in Bogotá who is one of the directors of the resurvey project. The dirt tracks that Chapman and his team traversed on mules are now roads. And climate change has pushed birds to higher elevations and altered their migratory patterns.Seeking to understand the effects of these changes on biodiversity, researchers launched the Colombia Resurvey Project in 2019. The main objective is to gather bird specimens, including DNA and tissue samples, to compare the modern population with Chapman’s collection. The team, which includes US researchers as well as local ones, has so far conducted 6 expeditions, visiting 14 of Chapman’s original sites — leaving 60 to go. A useful catalogue The researchers are finding that they have to venture deep into the forest to find birds that were once a stone’s throw from Chapman’s campsites, Cuervo says. And some species are nowhere to be found, including the red-ruffed fruitcrow (Pyroderus scutatus) — almost certainly lost when the trees in its territory were cut down to grow avocados, he adds.

Resurvey project researchers Jessica Diaz (right) and Andrés Sierra (left) record data from a mist net, used to collect birds during expeditions.Credit: Andrés M. Cuervo

The team has also confirmed that birds dependent on unique ecosystems are being replaced by generalist species — which are more adaptable to fragmented forest and a disrupted diet — reducing the country’s biodiversity1. Larger species and fruit eaters seem to have been hit particularly hard over the past century, because they require vast expanses of forest to thrive.
Illegal mining in the Amazon hits record high amid Indigenous protests
The effects of climate and landscape changes on bird populations in the tropics are not well understood, so the project will inform future conservation efforts, researchers say. “It’s almost impossible to imagine all the ways in which this data can potentially be used down the road,” says John Bates, curator and head of life sciences at the Field Museum of Natural History in Chicago, Illinois. Members of the resurvey project hope their catalogue will have as much impact as Chapman’s. It will include resources such as a genomic map illustrating birds’ evolution, generated from DNA samples. “We are collecting the most complete set of specimens that one can imagine so that scientists from now and the future can answer questions that we haven’t thought of,” Ocampo says.Taking chargeThe Colombia Resurvey Project team especially hopes that its anti-colonial approach will resonate with the scientific community. The researchers run workshops before each excursion to inform local communities about why they are planning to kill some birds, and how this is important for conservation and science. They are storing the specimens at the National University of Colombia, where the birds will be digitally catalogued, so that people can view them online, listen to audio of their song and scroll through interactive maps of the expeditions. And the team is creating birdwatching tours at the expedition sites to boost tourism.
Brazilian road proposal threatens famed biodiversity hotspot
Involving communities leads to better results, Niño-Rodríguez says. For instance, even if some Indigenous people do not know the scientific names for birds, they might be able to identify them on sight and know where they are most likely to be found. And community knowledge of how the forests have changed has passed from generation to generation, so local residents are able to fill gaps when satellite data and research logs aren’t available.It’s equally important to the researchers that those leading the project are from Colombia. They say it’s common for local experts to help visiting foreign researchers to find new species and make discoveries, but be excluded from the scientific process and the credit. “We don’t want to be the guys with the permits or the guys who facilitate the logistics of someone else’s research,” Cuervo says. “We want to do our own high-quality research, and we want it to be available for people to use.” This time around, the American Museum of Natural History is a partner on the project, rather than its lead. “Although the Chapman expedition was conducted with help and permissions from the Colombian government, today’s expeditions appropriately look much different than they did in Chapman’s time,” says a museum spokesperson, adding that the museum “is proud of the very active relationship it maintains with Colombia’s scientific institutions through education and research”.
Colombia: after the violence
Meanwhile, project researchers are training curious members of local communities in how to identify birds scientifically, so they can continue to log species with their cameras and mobile phones once the researchers leave the forest. Areas previously ruled by FARC guerrillas are now falling under the control of other armed groups, which might not let outsiders in, so local residents could soon be the only people who have access to some of Colombia’s most biodiverse jungles and the birds that inhabit them.“Hopefully we won’t have to wait another hundred years for scientists to return to these sites and assess their bird fauna,” Cuervo says. “Communities can do it with empowerment and interest in their biodiversity and surroundings.”

Nature 601, 178-179 (2022)
doi: https://doi.org/10.1038/d41586-021-03527-x

References1.Gómez, C., Tenorio, E. A. & Cadena, C. D. Conserv. Biol. 35, 1552–1563 (2021).PubMed 
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