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Research scope and experimental design
The study was conducted on rice production in Tar Pat Village, Maubin, Myanmar (16.617° N, 95.680° E) in the wet season (WS) 2014 and the dry seasons (DS) of 2015 and 2016. Sin Thukha variety with a growing time of 135 days was used for all 3 seasons. Best practices were identified based on the indicators of energy balance, cost-benefits, and GHGE for a functional unit (FU) of 1 ha of rice production. The last factor (GHGE) was estimated using the attributional LCA27,28,29 approach following LCA ISO standard ISO1404:44. Figure 1 shows the system boundary covering all processes of rice production from preharvest (cultivation) to postharvest (until milling). The primary data were collected in harvest and postharvest processes while the secondary data of pre-harvest processes were used in the system analysis. The conversion factors for energy and GHGE of the agronomic inputs, fuel and power consumption, and related transportations were interpreted from the ECOINVENT 3 database (version 3.3)19.
Figure 1

Inputs and outputs of the research system.

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Table 1 shows the research treatments with their major features and applied practices in the different seasons. For the WS2014, a comparative analysis was conducted for two farmer practices (FPs) and one improved practice (IPR). The two farmer practices corresponded to the scenarios of stacking rice plants in the field for 1 week (FP1w) and for 4 weeks (FP4w) after manual cutting. The IPR scenario involved threshing within 12 h after harvest. In addition, the IPR included use of a flatbed dryer for drying the rice, and hermetic bags for storage, instead of sun drying and farmer-granary bags for storage under FP.
Table 1 Scenarios and post-harvest operations covered in the study in Maubin, Ayeyarwady delta, Myanmar during three rice cropping seasons.
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In the dry season of 2015 and 2016, the analysis was conducted for two scenarios, which were farmer practice (FP) and improved post-harvest operations with a combine harvester, flatbed dryer, and hermetic storage (IPRc). Neither of the scenarios had delays or stacking of the rice plants, because farmers were able to thresh the rice immediately after it was manually harvested. The practices involved in FP were manual operations such as cutting of the mature rice plants and sun-drying. The scenarios of DS2015 and DS2016 differed from the WS2014 because the farmers did not stack rice in the DS prior to threshing.
The experiment was set up in fields of farmers on 4110 m2 for WS2014 and 5850 m2 for both DS2015 and DS2016. Each scenario was replicated 5 times in the different plots and were distributed using a completely randomized design (CRD). For the WS2014 experiment, there were 15 plots for three scenarios with each plot 270 m2. The paddy was harvested and processed based on the respective IPR and FP scenarios. The FP scenario included a thresher locally fabricated by the farmers based on the axial threshing principle that was powered by a two-wheel tractor with a 15 HP diesel engine (Fig. 2a), sun drying, and granary bags containing approximately 50 kg of paddy each. The IPR scenario involved a TC-800 axial flow thresher with a 7.5 HP engine (Fig. 2b)30. Compared to the farmer thresher, the imported unit was manufactured and marketed by a branded company in the Philippines and tested by IRRI to ensure good performance. For DS2015 and DS2016, there were 10 plots for two scenarios with each plot 390 m2. Rice was harvested using a Kubota-DC-70G combine harvester with 70 HP (Fig. 2c). A flatbed dryer and hermetic bags for storage of dried grain were used for IPR in all three seasons. The flatbed dryer with 4 t batch−1 capacity (Fig. 2d) was locally made based on published designs6, and was used for the IPR in both the wet and dry seasons. Hermetic bags for storage, also called “Super bags”31 hold 50 kg of paddy per bag. Milling operations were in-situ measured at the local rice mill (two-stage milling system) with 1 t h−1 capacity, located at Maubin, and were applied for both FP and IPR scenarios.
Figure 2

(a) Farmer thresher. (b) Imported thresher, TC-800. (c) Combine harvester Kubota-DC-70G. (d) Flatbed dryer 4 ton batch−1.

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Measurement and quantification of harvest and post-harvest losses
Shattering loss during cutting, stacking, and combine harvesting was determined through sampling of 5 plots using 1 m2 quadrants for each scenario. Shattering losses were calculated based on the ratio between shattered grains and yield at an adjusted moisture content of 14% wet basis (MC). In threshing, the grain losses were quantified in the stacked rice, at the separation process, in the cleaning process, and under the machine during the threshing operation. The sum of these losses comprised the threshing loss. The design did not quantify losses caused in drying and storage during handling, and grain lost to birds and rodents. See Htwe et al.9 for estimate of losses caused by rodents at this study site. The losses associated with discoloration, milling recovery (MR), and head rice recovery (HRR) caused by in-field stacking, delay of drying and storage methods were measured after milling.
MR and HRR were measured on milled rice. Three subsamples of 500 g of paddy were taken randomly from the grain harvested in each plot. The samples were cleaned using a Seedburo Paddy Blower, then 250 g of filled grain were passed twice in a RISE 10″ Rubber Roll Husker, then through a SATAKE Abrasive Whitener, and finally, through a SATAKE laboratory rice grader. MR and HRR were calculated using Eqs. (1) and (2), respectively.

$$MR; (%)=frac{Weight;of;milled ;rice ;left(include ;broken; grainsright)}{Weight; of ;paddy ;samples} times100$$
(1)

$$HRR ;(%) =frac{Weight; of ;whole; grains}{Weight ;of; paddy ;samples} times 100$$
(2)

Discoloration of grain was caused by fungi, bacteria, and environmental conditions such as high humidity and temperature. Milled rice kernels having more than 0.5% grains with a color other than white (usually yellow) or with a spotted surface were considered discolored32. To measure discoloration, three 25 g samples of the product were collected randomly. Discolored grains with spots, streaks, or having more than 0.5% differently colored surface were separated and weighed to calculate the percentage of discoloration based on Eq. (3).

$$Discoloration ;(%)= frac{weight ;of ;discolored ;grains (text{g})}{weight ;of ;sample; (25 ;text{g})} times 100$$
(3)

Energy efficiency and GHG emissions
This study investigated net energy value (NEV) and net energy ratio (NER) which are commonly used to quantify energy efficiency of a production systems33,34,35. NEV accounted for the inputs and outputs of the systems per the FU (ha of rice production) (Eq. 4) while the NER was the ratio between the output and input energy values (Eq. 5).

$$NEV left(frac{GJ}{ha}right)=E{V}_{outputs}-E{V}_{inputs}$$
(4)

where the EVoutputs accounted for rice grain products and by-products such as broken and discolored grains, bran, husks, and straw. The EVinputs includes all the energy consumption of rice production from cultivation to milling; this includes agronomic inputs, machine production, fuel and power consumption, and labor use.

$$NER=frac{E{V}_{outputs}}{E{V}_{inputs}}$$
(5)

Table 2 shows energy values embed in the whole milled rice, broken and discolored rice, bran, husk, and straw. Energy value (EV) of rice product36 is 15.2 MJ kg−1 while that of rice bran, broken rice, and discolored rice is 9.6 MJ kg−1 (Econivent 3 database19) with an assumption that these by-products are used for cattle feed and have a similar economic value. EV of rice husk is 8.7 MJ kg−1 (Ecoinvent 3 database19) and straw is 3.5 MJ kg−120 based on an asumption that partially harvested straw were collected for mushroom production. The collected amount of rice straw was about 50% of the grain yield at harvest34. The EV per kg was then translated to the FU based on the grain yield and post-harvest losses measured in the experiments. In particular, rice husk and bran were assumed to be 20 and 10% of the milled rice produced, respectively. EV of the cultivation (excluding harvesting and transportation) was about 12 and 16 GJ ha−1 for the small-farm irrigated rice production in the WS and DS, respectively, as reported in research in the same region (Ayeyarwaddy delta of Myanmar)37. EV of machine production was accounted for via a depreciation of 5 years. Fuel and power consumption of harvest and post-harvest operations were measured and translated to EV using the coversion factors. The energy of manual labor was calculated based on the metabolic equivalent of tasks (MET) (Table 2). Ainsworth et al.38 described the MET as the ratio of the human metabolic rate when performing an activity to the metabolic rate at rest. This ratio is converted to an energy value as MJ per hour working using the method described by Quilty et al.39 with the assumption of a mean Asian human body weight of 55 kg. For paddy transportation, the tractor-hauled trailor option was used for all scenarios with a distance of 15 km from the field to the station of drying, storage, and milling.
Table 2 Conversion factors for energy and GHGE.
Full size table

The GHGE were accounted for the whole production from cultivation to milling. The yield and grain losses were taken into account through a rice product recovery ratio as shown in Eq. (6).

$$GHGE ;(text{kg} ,{text{ha}}^{-1})=frac{text{G}H{G}_{cultivation}+ GH{G}_{harvest} + GH{G}_{postharvest} }{Product; recovery; ratio}$$
(6)

where GHGcultivation for the irrigated rice cultivation in Myanmar was about 2000 and 1200 kgCO2-eq ha−1 in WS and DS, repectively, as reported in recent research at the same site40. GHGE of harvest and post-harvest operations were calculated based on emissions generated during production of harvest and post-harvest equipment, input materials, and fuel consumption during operations. The unit of in-field emissions was per ha while that of off-field emissions was per kg of rice grains. The off-field emission values were therefore translated to attribute for the FU (ha) based on rice yield (kg ha−1). Furthermore, the associated unit (kg of rice grains) was considered as the product at the end of the life-cycle boundary, its carbon footprint therefore accounted for the post-harvest losses or product recovery. The recovered rice product (whole grains) was calculated from the grain yield with a consideration of harvest and postharvest losses. The postharvest losses were brokenness (HRR) and discoloration at harvest, drying, storage, milling, and handling. The rice product recovery ratio was calculated based on Eq. (7).

$$Product ;recovery; ratio =left(1-Los{s}_{harvesting}right)* HRR*(1-Discoloration)$$
(7)

The conversion factors for GHGE of related fuel and power consumption, machine production, and transportation are shown in Table 2. In particular, GHGE from the electric power consumption for drying and milling was translated from Ecoinvent 3 data (version 3.3) for the “rest of the world (ROW)”.
Cost–benefit analysis
Similar to the energy efficiency analysis, cost-benefits were quantified through the net income value (NIV) (Eq. 8) and net income ratio (NIR) (Eq. 9). NIV accounted for the cost of production and income value (IV) of products and co-products while the NIR was the ratio between NIV and the input cost.

$$NIV left(frac{$US}{text{ha}}right)=I{V}_{(whole; rice + broken; rice + discolored; rice+bran+husk+ straw)}-(Cos{t}_{cultivation}+{ Cost}_{left(harvest; and ;post {text{-}}harvestright)}),$$
(8)

$$NIR=frac{NIV}{(Cos{t}_{cultivation}+{ Cost}_{left(harvest ;and ;post{text{-}}harvestright)})}$$
(9)

The price of rice product was $US 400 per t1. Price of discolored rice was assumed to be the same as bran price, which is $US 140 per t1. Cost of the cultivation (excluding harvesting and transportation) was about 650 $US ha−1 for small-farm irrigated rice production in the Ayeyarwaddy delta of Myanmar37. Costs of the post-harvest operations were calculated based on the corresponding depreciation, maintenance, interest, energy consumption, and labor of all related equipment used in the operations from harvesting to milling. The component costs of input materials, labor, and energy included in the analysis were collected based on assessments in Myanmar in 2018 (Table 3).
Table 3 Cost and life span of different component costs of input materials, labor, and energy based on assessments conducted in the Ayeyarwady Delta region of Myanmar in 2018.
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Harvesting loss was used to conduct a sensitivity analysis for NIV and NIR for both the wet and dry seasons. This analysis only applied for the improved post-harvest operations with the flatbed dryer and hermetic storage.
Statistical analysis and software
Analysis of Variance (ANOVA) Single Factor and Two-Factor with replication and F-Test Two-Sample for Variances tools incorporated in Excel were used to evaluate the effects of the contrasting post-harvest management scenarios on the measured post-harvest losses, energy, and GHGE. The ECOINVENT-3 database (version 3.3)19 in association with Cumulative Energy Demand 1.09 method41 and the Global Warming Potential—100 years (GWP100a) presented in IPCC 201342, were used to interpret the conversion factors of energy (MJ) embedded and GHGE (CO2-eq) from the agronomic inputs and fuel consumption. All these databases and methods (Ecoinvent, Cumulatiive Energy Demand, and IPCC) are incorporated in SIMAPRO version 8.5.0.041. More

Data collection
This study uses a compiled data set that includes 12,072 native Lepidoptera species, 2079 native plant genera, and 24,037 different host plant-native Lepidoptera interactions from across the contiguous United States. From these data we extracted the plant information of 83 counties and a Lepidoptera list for the corresponding 25 states. The full dataset will be publicly available in a forthcoming data manuscript.
Lepidoptera range and host plant data
The Lepidoptera species data were compiled from historic citable sources (Supplementary Data 6) of range and host plant records. We originally compiled a similar list for the Mid-Atlantic region29. This dataset was updated to include more states and counties to include on the National Wildlife Federation website48. Non-plant host records (e.g., detriphagous, algae, fungi, lichen, and insect predators) are included, as well as Lepidoptera without known host plant associations, but not considered in this analysis. Plant ranges are to the county, Lepidoptera ranges are to state, and host plant-Lepidoptera records are relationships between a plant genus and Lepidoptera species. Plant genus was included as the unit of interaction because data on Lepidoptera- host plant associations are most accurate and available at the genus level. Although more specific data are occasionally available (e.g., Lepidoptera records to plant species), we limited our analysis to the genus scale in order to make equitable inferences across the geographic and ecological scope of this analysis.
Plant distribution data
The current list uses the Biota of North America Program49 (BONAP) as its major source for plant nomenclature. We used the BONAP database as our source for plant distributions because it specifies North American plant ranges that currently occur beyond their historic native range due to anthropogenic and natural expansion (e.g. Osage orange, Maclura pomifera).
Using the BONAP, a county-level survey was made for each state used in this study. Every county within those states was surveyed and BONAP records include records from adjacent counties. We classified plants into three categories; native, non-native, and adventive. A plant species is classified as adventive if it is native to North America but not in that specified region. Each plant genus was reviewed individually in each state. Genus records that fell entirely in one category resulted in all county records being designated that category. Any plant genus that had species that fell in two or more categories was examined county by county, with adjacent records being noted but not included. The state records for each plant genus are labeled in various combinations of the three categories. County-level data designate a genus either containing native records, or only non-native. For our study, we focused only on native plants, excluding non-native and adventive records (except for parameterizing probabilities of host-plant switching, see below for details).
Eighty-three counties in 25 states (Alabama, Arizona, Arkansas, California, Colorado, Delaware, Florida, Georgia, Idaho, Illinois, Kansas, Maine, Massachusetts, Michigan, Minnesota, Montana, New York, North Dakota, Ohio, Oregon, Pennsylvania, South Carolina, Tennessee, Texas, and Utah) were examined. At least three counties in each state (except Delaware) were used. Two counties from each state were selected from dissimilar ecoregions within each state. Ecoregions were determined using the Commission for Environmental Cooperation’s Ecological Regions of North America map50. For county selection we used the level 2 designation of terrestrial ecoregions (50 separate categories); however, for subsequent analyses, we used the level 1 designation (15 categories). As much as possible, counties that bordered other counties within an ecoregion were used to alleviate the issue of BONAP including records from adjoining states. At least one more county was added to meet the parameters of the latitude study and to fill out ecoregions. Up to five counties were used in some states. While the majority of counties were chosen without criteria beyond ecoregion status, Chase County KS was chosen based on its presence in the ‘South-central Semi-Arid Prairies’ ecoregion, as well as high natural grassland cover relative to other agriculturally dominated Kansas counties.
County data
A series of counties were selected along three primary latitude bands (Latitudes 46, 40, and 34). The latitude and longitude of each county were determined by the county seat using Google Earth. In most cases the county seat was centrally located within the state. A few county seats are not centrally located, most notably Monroe County in Florida where the county seat is Key West. We determined the land area (in km2, excluding inland, coastal, Great Lakes, and territorial sea water) for each county using information from the US Census (https://www.census.gov/quickfacts/fact/note/US/LND110210). Counties varied from 415 to 26,368 km2 with an average of 4519 ± 5598 SD.
Lepidoptera-host plant data
The host plant records for each Lepidoptera species include all known literature records. Not all host plant-Lepidoptera associations occur in every county, state or even the USA due to differences in plant distributions. Thus, we filtered the Lepidoptera list from each state to exclude any Lepidoptera species whose host plant did not occur in the selected county. The final dataset per county includes (1) all plant genera known to occur in the county, (2) all Lepidoptera known to use at least one plant genus that occurs in the county and (3) all host plants used by Lepidoptera that could potentially occur in the county.
Statistical methods
All analyses were conducted using program R, Version 3.5.151.
Distribution analysis
We first determined what distribution best fit our data in order to derive parameters that could be compared among counties. We used the package ‘goft’52 to conduct goodness of fit tests for the Exponential, Gamma, and Pareto distributions on each county separately. Functions in the ‘goft’ package use parametric bootstrap tests for the null hypothesis that a distribution fits a tested distribution. We tested each county (n = 83) and each distribution type (n = 3) separately. Using the distribution that best fit our datasets, we then used the function ‘fitdistr’ from the ‘MASS’ package53 to use maximum likelihood fitting to obtain parameters (e.g., shape α and scale θ) for the Plant-Lepidoptera distributions for each county separately.
We then tested for differences in the distribution of caterpillar richness among plants by county-level diversity and location metrics. For each county, we compared the α and θ of the distribution with county plant richness, Lepidoptera richness, ecoregion, latitude, and county land area. To test whether α or θ varied by ecoregion we used an analysis of variance (ANOVA) with Tukey’s post hoc multiple comparisons of means. To test whether α or θ changed with increasing plant richness, Lepidoptera richness, or land area, we used linear regression. Based on scatter plots of the data, no nonlinear relationships were necessary.
Network analysis
To determine conservation targets, we identified keystone plant species24,25 using methods from Harvey et al.26. To perform this analysis, we used binary networks of host-plant caterpillar interactions. Ecological networks are ideal to determine cascading extinction rates of specialists following host plant loss54. For this analysis and our following simulation, we chose one representative county dataset from each state from our 83 available counties (25 counties total). Our method to identify target keystone species at a national scale consists of three steps.
Species richness
On a per-county basis, we first identified how many Lepidoptera species are recorded in the literature as using each plant genus for growth and reproduction.
Extinction sensitivity
We also determined the ‘extinction sensitivity’ of each plant; in other words, how many Lepidoptera species are at risk of extirpation with the loss of a host plant? In our context, the sensitivity means “specialization”, caterpillars that use only one genus of the plant are considered especially sensitive to the removal of that plant. We modified the “nb.extinct” function from Harvey et al.26 so that we could calculate the number of extinct herbivores following the removal of a plant. This function was repeated for each county to acquire a number of species that were specialists to each host plant.
Network stability
Then we used a network-based approach to assess the effect of each plant genus on network stability. We used a binary matrix where each record of a caterpillar on a host plant indicates the existence of an interaction. The results given from a binary interaction network are correlated with those from a network weighted by abundance55. Here, the community stability index represents the minimum interactions required for the system to be stable where smaller values are the most stable, i.e., the more resilient a network is to disturbance, and large values are the most unstable. We calculated the stability index as the real part of the dominant eigenvalue of a Jacobian matrix following methods from Sauve et al.27 (see Appendix 1 in ref. 27 for definition and details). To acquire baseline stability, we conducted 175 iterations and took the median value. We determined that 175 was the minimum number of iterations needed to acquire a stability value ± 0.001 resolution using simulations on test datasets. For this analysis we only considered woody plants in our analysis because (1) woody plants tend to host the most diverse caterpillar communities29 and (2) computation time to include all plant genera was prohibitive.
To quantify the effect of each plant on total network stability, we reran the analysis, iteratively removing each plant genus and then recalculating the stability26. Then we subtracted the new stability values from baseline stability to find the median change when each plant was removed where negative values indicate reductions in network stability and positive values indicate increases. We then multiplied the stability value by −1 so that increases in this metric indicated an increase in a plant’s importance to stability to make this value comparable in direction to network structure and extinction sensitivity (i.e., increases in Lepidoptera diversity and # of specialist species).
Standardizing results across counties
For each analysis (step 1–3) we scaled our final values from 0–1 using this equation for each plant in each county separately:

$$frac{{{mathrm{x}} + left| {{mathrm{minimum}}}; {{mathrm{x}}} right|}}{{{mathrm{maximum}}; {mathrm{x}} + left| {{mathrm{minimum}}}; {{mathrm{x}}} right|}}.$$
(1)

We then took the mean of our three values to obtain a final ‘score’ for each plant genus per county. Finally, we identified which plant genera had the highest values over all the counties by plotting the means for each plant genera (n = 288) and assessing outliers (values that were 1.5× the interquartile range).
Field-based host plant-caterpillar interaction data
Field sampling
To compare the results from the network analysis on literature-based data collection with results from field-based data collection, we used caterpillar interactions recorded from native host plants from Richard et al.56 (hereafter: Mid-Atlantic dataset). Caterpillar surveys were conducted in 2011 within 8 hedgerows in New Castle County, DE and Cecil County, MD. This dataset contains plants surveyed in both native- ( >95% native plant biomass, n = 4) and nonnative-dominated ( >75% nonnative plant biomass, n = 4) hedgerows. Sites were all located within Mid-atlantic decidous piedmont forest, and were separated by at least 100 m.
In June–July, an observer walked a 100 m transect on days in which foliage was not wet to collect caterpillars in each site. Observers sampled all caterpillars using the total search approach57 to methodically inspect leaves, twigs, and branches of all woody plants within a 2-m3 area along the transect. Each search was conducted for 5 m every 2 m along the 100 m transect. In total, each hedgerow treatment was searched for a total of 1000 min in both June and July. All caterpillars were identified to species or morphospecies using Wagner57, Wagner et al.58, and various web sources. Caterpillars that could not be identified in the field were measured and then brought to the lab to be reared to adulthood for later identification using the literature and the University of Delaware Insect Reference collection.
Data management and analysis
Because our network analysis was based on native woody plant genera, we excluded all non-native plant genera in the Mid-Atlantic dataset. We calculated the number of times each plant was searched and excluded all plant species that were searched 1 chosen host plant) and total interaction richness supported (i.e. all interactions between a plant and a caterpillar consumer).
Host-plant switching
We also included the potential for host plant switching by including a probability of using plants not recorded in the host plant literature. We calculated the probability of random host plant switching as:

$${P}_{{mathrm{ic}}}left( {{mathrm{host plant shift}}} right) = frac{{{E}_{{mathrm{ic}}}}}{{{N}_{mathrm{c}} – {H}_{{mathrm{ic}}}}} * {N},$$
(2)

where Pic is the probability of host plant shifting by Lepidoptera species i in county c, Eic is the proportion of non-native plants used by Lepidoptera species i in county c, Nc is the number of native host plants in county c, Hic is the number of total native host plants used by Lepidoptera species i in county c, and N is the total number of plants included in the simulation (from 1 to 50). This equation gives the probability that Lepidoptera species i in county c shifted to at least one of N plants used in the simulation. Using this probability, we used the sample function in R to predict whether any of the possible Lepidoptera species were included or not in each iteration and added each unique species to those included from known hosts.
Simulation output
For each county, we iterated these scenarios over 100 iterations with random draws of plant genera and keystone genera. We chose 100 iterations for each county to accurately estimate means while maintaining computational efficiency. To standardize across counties, we calculated the percent of Lepidoptera supported out of all potential phytophagous Lepidoptera (for woody plants and herbaceous plants separately) and percent interactions in each iteration. For each county and iteration, we plotted the median value. Simulations were run for woody and herbaceous plants separately.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More

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These bizarre ancient species are rewriting animal evolution – read by Benjamin Thompson
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The Cambrian explosion, around 541 million years ago, has long been regarded as a pivotal point in evolutionary history, as this is when the ancient ancestors of most of today’s animals made their first appearances in the fossil record.
Before this was a period known as the Ediacaran – a time when the world was believed to be populated by strange, simple organisms. But now, modern molecular research techniques, and some newly discovered fossils, are providing evidence that some of these organisms were actually animals, including ones with sophisticated features like legs and guts.
This is an audio version of our feature: These bizarre ancient species are rewriting animal evolution
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