Ecology

ScopeWe compared disposable face masks that were used once with face masks that were sterilized and used five more times (six times in total). Sterilisation and PFE test data of the Aura 1862+ (3M, Saint Paul, Minnesota, USA) face mask indicate that this type of face mask shows good performance after multiple sterilisation cycles10,11,12. In a previous pilot study, the company CSA Services (Utrecht, the Netherlands), a sterilization facility for cleaning, disinfection and sterilization of medical instruments, was rebuild to process FFP2 face masks. In total, 18,166 single use FFP2 masks were sterilised after use in a medical autoclave. As the majority (n = 7993) were Aura 1862+ (3M, Saint Paul, Minnesota, USA), this particular type of face mask was chosen for the LCA.The total weight of the face masks and packaging together during end-of-life consists of incineration for the face masks (97%) and landfill for the carton box packaging of new face masks (3%). There is no recycling potential used in our model since the materials coming from the operating room and its packaging is commonly disposed as medical waste. In the Netherlands, no energy recovery takes place at the incineration of regulated medical waste. Therefore, no co-function was applicable for the end-of-life scenario.Recycling is often a multi-functional process that produces two or more goods. To deal with the multi-functionality in the background processes, the cut-off approach was applied to exclude the allocation of the greenhouse gas emissions to additional goods. This means that potential rest materials such as energy gained during incineration are cut-off and that the greenhouse gas emissions are fully allocated to the waste treatment processes itself.In the LCA, the ‘functional unit’ defines the primary function that is fulfilled by the investigational products and indicates how much of this function is considered18. In this study, we pragmatically chose as a definition for the protection of 100 health care workers against airborne viruses, using one FFP2 certified face mask, each during one working shift of an average of 2 h in a hospital in the Netherlands.Table 1 shows the differences between the two scenarios:

1.

100 masks including packaging, transported from production to the hospital, used and disposed.

2.

100 times use of reprocessed masks. We calculated that 27.1 masks are being produced and transported from production to the hospital. The 27.1 are being reprocessed five times, taking into account that 20% of the batch cannot be reprocessed. Therefore 80% of the batch could be used for reprocessing after each step resulting in: 27.1 (new) + 21.7 (repro 1) + 17.3 (repro 2) + 13.9 (repro 3) + 11.1 (repro 4) + 8.9 (repro 5) = 100 times of use. For each time of reprocessing the batch is transported from the hospital to the (hospital) Central Sterilization Services Department (CSSD) and disposed after five times of reprocessing.

Table 1 Comparison between reference flow 1 and 2.Full size tableCombining the functional unit with the two alternative scenarios results in the reference flows for the protection of 100 health care workers against airborne viruses, either using a face mask one single time (100 virgin masks produced for the 1st scenario), or reusing a face mask for five additional times (27.1 virgin masks produced for the 2nd scenario). For both reference flows, only FFP2 certified face masks are considered. For the calculations each mask is used for a single two hours working shift in an average hospital in the Netherlands.Life cycle inventory (LCI) analysisThe inventory data includes all phases from production (including material production and part production), transport, sterilisation to end-of-life of the life cycle of the single use and reprocessed face masks. We disassembled one face mask to obtain the weight of each individual component on a precision scale (Fit Evolve, Bangosa Digital, Groningen, the Netherlands) with a calibrated inaccuracy of 1.5%. Component information and materials were obtained from the data fact sheet provided by the manufacturer. We conducted a separate validation experiment to establish the material composition in the filtering fabric (Supplement file).This LCA with the Aura 3M masks was based on steam sterilization by means of a hospital autoclave and therefore part of this study. Therefore, face masks were placed in a sterilization bag that contained up to five masks. A total of 1000 masks were placed into an autoclave (Getinge, GSS6713H-E, Sweden) per cycle. After sterilization, the masks were transported to the hospital. Masks were reprocessed for a maximum of five times before final disposal10,11.The assessment of climate change impact is done following as closely as possible the internationally accepted Life Cycle Assessment (LCA) method following the ISO 14040 and 14044 standards19,20. The LCA examines all the phases of the product’s life cycle from raw material extraction to production, packaging, transport, use and reprocessing until final disposal19. The LCA was modelled using SimaPro 9.1.0.7 (PRé Sustainability, Amersfoort, The Netherlands). The background life cycle inventory data were retrieved from the ecoinvent database (Ecoinvent version 3.6, Zürich, Switzerland)21.To make a valid comparison between the disposable and reprocessing face masks, the system boundaries should be equal in both scenarios. The system boundaries in this study consisted of the production, the use and the disposal and waste treatment of the masks. For the reprocessed face masks, the lifecycle is extended due to the sterilisation process (Fig. 1). Therefore, the additional PPE’s and materials needed to safely process the masks (e.q. masks, gloves and protective sheets) are included in the production phase. The production of machinery for the manufacturing of the face masks and the autoclave were not included in this study.Figure 1System boundary overview of new and reprocessed face masks including waste treatment by incineration.Full size imageThe production facility for the face masks is located in Shanghai, China22,23. Further distribution took place from Bracknell, UK to Neuss, Germany and the final destination was set in Rotterdam, the Netherlands.The packaging materials were disposed in the hospital where the face masks are used primarily. After first use, face masks were transported to the sterilisation department. All masks were manually checked before reprocessing by personnel wearing PPE. Of all used Aura 1862+ facemasks that entered the CSA, approximately 10% was discarded. To remain conservative, the LCA was conducted based on a 20% rejection rate as a result of face masks which could not be reused anymore due to deformities, lipstick, and broken elastic bands.A full overview of the life cycle inventory table for the two scenarios and details on model assumptions are added in the Supplemental file (Supplemental file, Part B).Life cycle impact assessmentThe carbon footprint (kg CO2 eq) was chosen as the primary unit in the impact category. ReCiPe was applied at midpoint level and used to translate greenhouse gas emissions into climate change impact16.Uncertainty analysisThe final LCA model contains several uncertainties based on assumptions and measurement inaccuracies24. The included uncertainties were based on weighted components of the masks as well as the packaging which were measured with 1.5% inaccuracy of the precision scale apparatus. A Monte Carlo sampling25 was conducted for both alternatives (disposable and reprocessing) where input parameters for the LCA were sampled randomly from their respective statistical distributions in for 10,000 ‘runs’. Because input parameters between scenarios were partly overlapping, we compared these two scenarios directly using a discernibility analysis. This technique, establishes which scenario is beneficial for each of 10,000 Monte Carlo runs. We report the percentage of instances where the reprocessing scenario has a lower carbon footprint than the disposable scenario.Sensitivity analysisA sensitivity analysis was conducted to check the sensitivity of the outcome measures to variation in the input parameters. To determine which parameters are interesting to investigate, three aspects were considered: the variations in number of face masks per sterilization cycle (autoclave capacity), rejection rate (number of losses per cycle) and transport distance to the CSSD. Finally, we included the relative contribution of these variations. The following three parameter variations were chosen for the sensitivity analysis:

1.

Rejection percentage. The rejection rate was defined based on experiences from the participating sterilisation department and studies that show that sterilisation of the face masks up to 5 times is possible. Masks were re-used for 5 times, approximately 10% was discarded during the total life cycle. Out of this experience and to remain conservative, the total rejection rate was set on 20%. Therefore it is interesting to investigate whether variation in PFE testing outcomes or differences in user protocols influence the outcomes. This should indicate if masks from higher or lower quality can also be suitable candidates for reprocessing.

2.

Autoclave capacity, which largely depends on the loading of the autoclave. To mimic different loads of the autoclave, it is interesting to know the influence of sterilizing fewer masks per run on the model.

3.

Transport. As it is likely that many hospitals have a Central Sterilisation Services Department (CSSD) it is interesting to know the effect of having zero transportation. Moreover, in case hospitals are not willing to change the routing in their CSSD it is interesting to observe how outcomes are influenced if transportation is set on the maximal realistic value of 200 km.

The parameters have been varied with 250 and 500 face masks per sterilisation batch. A rate varying with 10% and 30% of the face masks being rejected due to quality reasons and variation in transport kilometres of 0–200 km.There is a small difference between the baselines of the sensitivity, LCIA and contribution analyses because all these are performed using separate Monte-Carlo simulations. The output of the different simulations may show minor differences due to statistical distribution.Cost price comparisonA cost analysis was made to give insight in costing from a procurement perspective. The cost analysis is conducted with five face masks that were steam sterilized per batch in a permeable laminate bag, Halyard type CLFP150X300WI-S20 and includes the expenses of energy, depreciation, water consumption, cost of personnel, overhead and compared to the prices for a new disposable 3M Aura face mask during the first and second Corona waves. Five pieces per bag were chosen in order to have enough space between the masks to sterilise each mask properly. The cost analysis is based on actual sterilization as well as associated costs compared to the prices of new disposable face masks. The costs were then related to the functional unit of protecting 100 health care workers by calculating the difference in the amount of Euros per 100 face masks. More

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These authors contributed equally: Celso H. L. Silva Junior, Nathália S. Carvalho, Ana C. M. Pessôa.Tropical Ecosystems and Environmental Sciences Laboratory (TREES), São José dos Campos, São Paulo, BrazilCelso H. L. Silva Junior, Nathália S. Carvalho, Ana C. M. Pessôa, João B. C. Reis, Aline Pontes-Lopes, Juan Doblas, Wesley Campanharo, Henrique Cassol, Yosio E. Shimabukuro, Liana O. Anderson & Luiz E. O. C. AragãoInstituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, São Paulo, BrazilCelso H. L. Silva Junior, Nathália S. Carvalho, Ana C. M. Pessôa, Aline Pontes-Lopes, Juan Doblas, Wesley Campanharo, Henrique Cassol, Luciana Gatti, Ana P. Aguiar, Yosio E. Shimabukuro & Luiz E. O. C. AragãoUniversidade Estadual do Maranhão (UEMA), São Luís, Maranhão, BrazilCelso H. L. Silva JuniorCentro Nacional de Monitoramento e Alertas de Desastres Naturais (CEMADEN), São José dos Campos, São Paulo, BrazilJoão B. C. Reis & Liana O. AndersonUniversity of Bristol, Bristol, UKViola Heinrich & Joanna HouseInstituto de Pesquisa Ambiental da Amazônia (IPAM), Brasília, Distrito Federal, BrazilAne Alencar, Camila Silva & Paulo BrandoUniversidade Estadual de Campinas (UNICAMP), Campinas, São Paulo, BrazilDavid M. LapolaEcología del Paisaje y Modelación de Ecosistemas (ECOLMOD), Universidad Nacional de Colombia (UNAL), Bogota, ColombiaDolors ArmenterasUniversidade de Brasília, Brasília, Distrito Federal, BrazilEraldo A. T. MatricardiUniversity of Oxford, Oxford, UKErika BerenguerLancaster University, Lancaster, UKCamila Silva, Erika Berenguer & Jos BarlowSouth Dakota State University, Brookings, SD, USAIzaya NumataEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA) Amazônia Oriental, Belém, Pará, BrazilJoice FerreiraUniversity of California, Irvine, CA, USAPaulo BrandoWoodwell Climate Research Center, Falmouth, MA, USAPaulo BrandoInstituto Nacional de Pesquisas da Amazônia (INPA), Manaus, Amazonas, BrazilPhilip M. FearnsideJet Propulsion Laboratory (JPL), Pasadena, CA, USASassan SaatchiUniversity of California, Los Angeles, CA, USASassan SaatchiUniversidade Federal do Acre (UFAC), Cruzeiro do Sul, Acre, BrazilSonaira SilvaUniversity of Exeter, Exeter, UKStephen Sitch & Luiz E. O. C. AragãoStockholm Resilience Centre, Stockholm University, Stockholm, SwedenAna P. AguiarSchool of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL, USACarlos A. SilvaEuropean Commission, Joint Research Centre (JRC), Ispra, VA, ItalyChristelle Vancutsem, Frédéric Achard & René BeuchleCenter for International Forestry Research (CIFOR), Bogor, IndonesiaChristelle Vancutsem More

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SubjectsIn total, twenty-two black-capped chickadees (nine males and 13 females) were tested between May and December 2019, and 16 black-capped chickadees (seven males and nine females) completed the experiment. One male and one female failed to learn Pretraining, and one female failed to learn Non-differential training (see descriptions below for training information); as a result, all three were removed from the experiment. In addition, one male and two females died of natural causes during the course of the study (see Ethics Declaration). For all birds, sex was determined by deoxyribonucleic acid analysis of blood samples37. All birds were captured in Edmonton (North Saskatchewan River Valley, 53.53°N, 113.53°W; Mill Creek Ravine, 53.52°N, 113.47°W), Alberta, Canada in January 2018 and January 2019 and were at least one year of age at capture, verified by examining outer tail rectrices38.Prior to the current experiment, all chickadees were individually housed in Jupiter Parakeet cages (30 × 40 × 40 cm; Rolf C. Hagen, Inc., Montreal, QB, Canada) in a single colony room. Therefore, birds did not have physical contact with each other, but did have visual and auditory contact. Birds had ad libitum access to food (Mazuri Small Bird Maintenance Diet; Mazuri, St. Louis, MO, USA), water with vitamins supplemented on alternating days (Monday, Wednesday, Friday; Prime Vitamin Supplement; Hagen, Inc.), a cup containing grit, and a cuttlebone. Additional nutritional supplements included three to five sunflower seeds daily, one superworm (Zophobas morio) three times a week, and a mixture of hard-boiled eggs and greens (spinach or parsley) twice a week. The colony rooms were maintained at approximately 20 °C and on a light:dark cycle that followed the natural light cycle for Edmonton, Alberta, Canada.One bird had previous experience with one operant experiment involving chick-a-dee calls but showed no difference in responding in comparison to the naïve birds. The remaining 15 birds had no previous experimental experience with black-capped chickadee-produced fee-bee songs or any experimental paradigm.Recordings of acoustic stimuliThe following acoustic stimuli were used in our previous published operant study which indicated that male and female chickadees can identify individual females via their song36. Stimuli included the songs of six female black-capped chickadees. All females were captured in Edmonton (North Saskatchewan River Valley, 53.53°N, 113.53°W; Mill Creek Ravine, 53.52°N, 113.47°W), Alberta, Canada in January 2010, 2011, 2012, and 2014, and all females were at least one year of age at capture, verified by examining outer tail rectrices38. Four females were recorded in Spring 2012 and two females were recorded in Fall 2014. Each recording session lasted approximately 1 h and all recordings took place after colony lights turned on at 08:00, specifically at 8:15. All females were recorded in silence, individually, within their respective colony room cages. Colony room cages were placed in sound-attenuating chambers for recording (1.7 m × 0.84 m × 0.58 m; Industrial Acoustics Company, Bronx, NY). An AKG C 1000S (AKG Acoustics, Vienna, Austria) microphone (positioned 0.1 m above and slightly behind the cage) was connected to a Marantz PMD670 (Marantz America, Mahwah, NJ) digital recorder (16 bit, 44,100 Hz sampling rate) and was used for all recordings. Audio recordings were analyzed and cut into individual files (songs) using SIGNAL 5.03.11 software (Engineering Design, Berkley, CA, USA).Acoustic stimuliFor the current study, a total of 150 vocalizations were used as stimuli, these vocalizations were comprised of 25 fee-bee songs produced by each of six recorded female chickadees. We ensured that all 150 were of high quality, meaning no audible interference, and all stimuli were bandpass filtered (lower bandpass 500 Hz, upper bandpass 14,000 Hz) using GoldWave version 6.31 (GoldWave, Inc., St. John’s, NL, Canada) in order to reduce any background noise outside of the song stimuli spectrum. For each song stimulus, 5 ms of silence was added to the leading and trailing portion of the vocalization and each stimulus was tapered to remove transients, in addition amplitude was equalized peak to peak using SIGNAL 5.03.11 software. When triggered, stimuli were presented at approximately 75 dB peak SPL as measured by a calibrated Brüel & Kjær Type 2239 (Brüel & Kjær Sound & Vibration Measurement A/S, Nærum, Denmark) sound pressure meter (A-weighting, slow response), a level that corresponds with the natural chickadee vocalizations amplitudes39,40,41. All dB measurements were made at the level of the request perch where birds trigger stimuli and where birds are required to remain for the length of the stimuli and all dB measurements refer to SPL.Noise stimuliAnthropogenic noise stimuli were originally created and used by Potvin and MacDougall-Shackleton42 and by Potvin, Curcio, Swaddle, and MacDougall-Shackleton43. The stimuli were recorded from an urban area in Melbourne, Victoria, Australia and other anthropogenic noise stimuli of various trains, cars, motorcycles, and lawnmowers downloaded from Soundbible.com were used. Within Victoria44 and Alberta45,46, urban traffic noise averages 60–80 dB SPL. The files used varied in length, with those recorded in Melbourne all being 10 min in length and those downloaded from Soundbible.com varying between 1–10 minutes42,43. In total 10 tracks were used with 30 total minutes of noise stimuli. Three anthropogenic noise conditions were used in the study, including Silence (no noise), Low noise (anthropogenic noise stimuli played at ~ 40 dB peak SPL), and High noise (anthropogenic noise stimuli played at ~ 75 dB peak SPL) replicating the variation of traffic noise experienced in urban areas42,43. For the Low and High noise conditions the 10 tracks repeated on a randomized loop during data collection (natural light of light/dark cycle) with, thus noise exemplars overlapped songs by chance, to further emulate urban areas. Noise stimuli had natural variations and modulations in frequency and amplitude over the course of the sound files. All dB measurements for noise stimuli included in this study refer to SPL. See Fig. 1 for female song and traffic noise stimuli spectrograms and power spectra.Figure 1(A) Spectrogram of a female fee-bee song in silence. (B) Power spectrum of female fee-bee song in silence . (C) Spectrogram of female fee-bee song in low noise. (D) Power spectrum of female fee-bee song (black) in low noise (grey). (E) Spectrogram of female fee-bee song in high noise. (F) Power spectrum of female fee-bee song (black) in high noise (grey).Full size imageApparatusFor the duration of the experiment, birds were housed individually in modified colony room cages (30 × 40 × 40 cm; described above) which were placed inside a ventilated, sound-attenuating operant chamber. See Fig. 2 for illustration of operant conditioning chamber. All chambers were lit with a full spectrum LED bulb (3 W, 250 lm E26, Not-Dim, 5000 K; Lohas LED, Chicago, IL, USA), and maintained the natural light:dark cycle for Edmonton, Alberta. Each cage within each operant chamber contained two perches and an additional perch fitted with an infrared sensor (i.e., the request perch). See Fig. 2C. Each cage also contained a water bottle, grit cup, and cuttlebone See Fig. 2G-2H. Birds had ad libitum access to water (with vitamins supplemented on alternating days; Monday, Wednesday, Friday), grit, and cuttlebone and were provided two superworms daily (a morning and afternoon worm). An opening (11 × 16 cm) located on the left side of the cage allowed the birds to access a motorized feeder, with a red LED light, and equipped with an infrared sensor47. See Fig. 2B,D–F. The purpose of the sensor was so that food was only available as a reward for correct responses to auditory stimuli during the operant discrimination task. We should note that performance of the discrimination task is required for access to food and thus maintains motivation. For operation and data collection, a personal computer connected to a single-board computer48 scheduled trials and recorded responses to stimuli. Stimuli were played from a personal computer hard drive through a Cambridge Integrated Amplifier (model A300 or Azur 640A; Cambridge Audio, London, England). Data is downloaded once a day in order to reduce stress on subjects as all equipment must be tested following download, requiring contact with subjects. Stimuli played in the chamber through a Fostex full-range speaker (model FE108 Σ or FE108E Σ; Fostex Corp., Japan; frequency response range 80–18,000 Hz) located beside the feeder. See Sturdy and Weisman49 for a detailed description of the apparatus. See Fig. 2 for an illustration of the operant conditioning chamber set-up.Figure 2Illustration of the operant conditioning chamber, including: (A) speaker, (B) automated feeder, (C) request perch fitted with infrared photo-beam assembly, (D) feeder cup, (E) electrical inputs, (F) red LED, (G) water bottle, (H) and cuttlebone. Also shown is the feeder opening, and additional perches. To simplify, the sketch the front and floor of the chamber, and the enclosure’s acoustic lining are not included.Full size imageProcedureOperant conditioningOur current operant conditioning go/no-go set-up is used to understand how birds perceive auditory stimuli. By training the birds to respond to particular stimuli and withhold responding to other stimuli we can compare responses to both types of stimuli. The go/no-go paradigm requires the birds to learn which stimuli require correct responses (go), providing reinforcement (food), and which stimuli require birds to withhold responding (no-go), resulting in the avoidance of punishment (lights out).The current study follows nine stages, after learning to use the operant conditioning set-up, birds then go through Non-differential training (stage 1) where they will be exposed to all stimuli that will be used in the experiment and to ensure that the birds respond to the stimuli equivalently. Then birds complete Discrimination training (stage 2) where birds on two categories of sounds. One category is rewarded, the other category is punished. Then the Discrimination-85 (stage 3) phase prepares birds for future trials where there is no reward nor punishment. After this point, birds will follow three series (Silence; Low; High) of Discrimination-85 with noise (stage 4, 6, 8) and a corresponding Probe with noise (stage 5, 7, 9), meaning that that each subject will repeated the two discrimination tasks three times with different noise conditions, with the order of noise conditions randomized among individuals. The detailed procedures for each stage are described in the following.Non-differential trainingThe purpose of Non-differential training is to engender a high level of responding on all trials, across all stimuli. Once a bird learned to use the request perch fitted with a sensor as well as learned to use the feeder to obtain food then Pretraining began. During Pretraining, birds were trained to respond to a 1 s tone (1,000 Hz) in order to receive access to food. Pretraining occurred over an approximately 15-day period in order to allow acclimatization to the chamber, feeder, and speaker. Following Pretraining was Non-differential training. During Non-differential training, birds received food for responding to all fee-bee song stimuli. All trials began when a bird landed on the request perch and remained on the perch for between 900–1100 ms, at which point a randomly-selected song stimulus played. Songs were presented in random order from trial to trial until all 150 stimuli had been triggered and played without replacement; once all 150 stimuli were played, a new random sequence initiated. In the event that the bird left the request perch during a stimulus presentation, the trial was deemed interrupted, and resulted in a 30 s lights out of the operant chamber. If the bird entered the feeder within 1 s after the stimulus (any stimulus) was played, it was given 1 s access to food, followed by a 30 s intertrial interval. If a bird remained on the request perch during the stimulus presentation and the 1 s following the completion of the stimulus, then the bird received a 60 s intertrial interval with the lights on. Birds continued on Non-differential training until they completed six 450-trial blocks at ≥ 60% responding on average to all stimuli, at least four 450-trial blocks at ≤ 3% difference in responding to future rewarded versus future unrewarded Discrimination stimuli, at least four 450-trial blocks at ≤ 3% difference in responding to future rewarded versus unrewarded Discrimination stimuli. Then following a day of free feed (during which birds had ad libitum access to a food cup) birds completed a second round of Non-differential training in which they completed at least one 450-trial block that met each of the above requirements. A 450-trial block consisted of the bird experiencing each of the 150 stimuli three times. For the current study the average time to complete Non-differential training ranged from 10 to 41 days (M = 21.43, SD = 9).Discrimination trainingDiscrimination training procedures included only 114 out of the 150 training stimuli that were previously presented in non-differential training, and responses to these stimuli were now differentially reinforced. Specifically, correct responses to half of the stimuli (“rewarded stimuli”, S+) were positively reinforced with 1 s access to food, and incorrect responses to the other half (“unrewarded stimuli”, S−) were instead punished with a 30-s intertrial interval of lights off within the operant chamber. In regard to criterion, Discrimination training continued until a bird completed six 342-trial blocks with a discrimination ratio between their respective S+ and S− of greater than 0.80 with the last two blocks being consecutive. For discrimination ratio calculations see Response Measures below.The current subjects were randomly assigned to either a True category discrimination group (n = 10) or Pseudo category discrimination group (n = 6). Furthermore, chickadees in the True category discrimination group were divided into two subgroups: (a) True 1 (n = 5; three females and two males) discriminated between 57 rewarded fee-bee songs produced by three individual female chickadees (S+) and 57 unrewarded fee-bee songs produced by another three individual female chickadees (S−); and (b) True 2 (n = 5; two females and three males) discriminated between the same songs with opposite rewards, properly, the 57 rewarded (S+) fee-bee songs were the S− from True 1 and the 57 unrewarded (S−) fee-bee songs were the S+ from True 1. For birds in the True category discrimination the average number of blocks completed per day for Discrimination training ranged from 2.4–4.4 blocks (3.3 ± 0.7 blocks).In similitude, the Pseudo category discrimination group was divided into two subgroups: (a) Pseudo 1 (n = 3; two females and one male) discriminated between 57 randomly-selected rewarded (S+) fee-bee songs and 57 randomly-selected unrewarded (S−) fee-bee songs; and (b) the second subgroup Pseudo 2 (n = 3; one female and two males) discriminated between the same songs with opposite rewards, meaning, the 57 rewarded (S+) fee-bee songs were the S− from Pseudo 1 and the 57 unrewarded (S−) fee-bee songs were the S+ from Pseudo 1 (S+) fee-bee songs and 57 randomly-selected unrewarded (S−) fee-bee songs. To explicate, the purpose of the two Pseudo groups was to include a control in which subjects are required to memorize each vocalization independent of the producer rather than be trained to categorize songs according to individual chickadees as the True groups have been. All birds remained in their respective groups (True 1 and 2; Pseudo 1 and 2) for the duration of the study. For birds in the Pseudo category discrimination the average number of blocks completed per day for Discrimination training ranged from 3.3–6.1 blocks (4.34 ± 1.2 blocks).Discrimination-85 phaseDiscrimination-85 was identical to the above Discrimination training except that rewarded songs were reinforced with a reduced probability, P = 0.85. Therefore, for 15% of trials when a rewarded stimulus was played and a bird correctly responded, no access to food was triggered. Instead, a 30 s lights on intertrial interval occurred. The change in reinforcement occurs in order to prepare birds for Probe trials in which novel song stimuli were neither rewarded with access to food nor unrewarded with a lights out, instead nothing occurs. Discrimination-85 continued until birds completed two consecutive 342-trial blocks with a discrimination ratio of at least 0.80.Discrimination-85 phase with noiseAll subjects followed three series (Silence; Low; High) of Discrimination-85 with noise and a corresponding Probe with noise and the order of noise stimuli was randomly-selected for each bird. Discrimination-85 with noise was identical to the Discrimination-85 phase except one of the three noise stimuli conditions (Silence; Low noise, 40 dB SPL; High noise, 75 dB SPL) was played over the song stimuli. The noise stimuli condition was randomly-selected for each bird. Each bird went through three series of Discrimination-85 with noise (Silence; Low; High) until reaching criteria: two consecutive 342-trial blocks with a discrimination ratio of at least 0.80. Here, we were interested in how the addition of noise would impact discrimination between rewarded and unrewarded female song stimuli.Probe phase with noiseFollowing each Discrimination-85 phase with noise was a corresponding Probe phase with noise. During Probe the reinforcement contingencies from Discrimination-85 were maintained. In addition to the 114 stimuli from Discrimination training, this stage included 12 novel fee-bee songs (i.e., Probe stimuli), two from each of the six individual females. For True groups, six of these novel songs were categorized as P + and the other six as P-, based on whether they were produced by the same birds as the S+ or the S− training stimuli. For Pseudo groups, the novel songs were not assigned to categories. For both groups, the 12 novel stimuli were neither rewarded (no food access) nor unrewarded (no lights out). The birds completed six 126-trial blocks in which the 114 familiar discrimination stimuli repeated once per block and the 12 probe sequences played once per block. In addition, one of the three noise stimuli conditions (Silence; Low noise, 40 dB SPL; High noise, 75 dB SPL) was played over the song stimuli, and each bird went through three series of Probe with noise (Silence; Low; High) which corresponded with the birds previous Discrimination-85 phase with noise condition. Thus, all birds completed all three Discrimination-85 phases with noise conditions followed by the corresponding Probe with noise conditions, and the order of noise stimuli condition was randomly-selected for each bird. In Probe phases we are interested if subjects can categorize novel stimuli to previously rewarded or unrewarded female birds.Response measuresFor each 342-block trial during training (Discrimination-85 with noise; Probe with noise), proportion response was calculated (R + /(N-I)): R + represents the number of trials in which the bird went to the feeder, N represents the total number of trials, and I represents the number of interrupted trials in which the bird left the perch before the entire stimulus played. For Discrimination training and the Discrimination-85 phase, a discrimination ratio was calculated by dividing the mean proportion response to all S+ stimuli by the mean proportion response to S+ stimuli plus the mean proportion response to S− stimuli. A discrimination ratio = 0.50 specifies equal response to rewarded (S+) and unrewarded (S−) stimuli, a discrimination ratio = 1.00 specifies a perfect discrimination between S+ and S− stimuli. We also collected data regarding the number of blocks and days per stage (Discrimination training; Discrimination-85 training with noise) in order to examine the latency of discrimination learning.Statistical analysesAll statistical analyses were conducted using SPSS (Version 20, Chicago, SPSS Inc.). In order to compare the number of trials needed to reach criterion and the discrimination ratios between True and Pseudo groups for Discrimination Training we conducted an analysis of variance (ANOVA). For Discrimination-85 with noise (Silence, Low noise, High noise), an ANOVA was conducted to compare the number of trials needed to reach criterion and the discrimination ratios between True and Pseudo groups. We also conducted post-hoc tests in order to reveal any sex differences between groups.And for Discrimination-85 with noise and Probes with noise repeated measures ANOVA was conducted to compare proportion response to training stimuli and probe stimuli between True groups and Pseudo groups. Lastly, we conducted post-hoc tests in order to reveal any differences in the number of trials to reach criterion during Discrimination training and to Discrimination-85 with noise.Ethics declarationThroughout the experiment, birds remained in the testing apparatus to minimize the transport and handling of each bird. One male and two female subjects died from natural causes during operant training. Following the experiment, healthy birds were returned to the colony room for use in future experiments.All procedures were conducted in accordance with the Canadian Council on Animal Care (CCAC) Guidelines and Policies with approval from the Animal Care and Use Committee for Biosciences for the University of Alberta (AUP 1937), which is consistent with the Animal Care Committee Guidelines for the Use of Animals in Research. Birds were captured and research was conducted under an Environment Canada Canadian Wildlife Service Scientific permit (#13-AB-SC004), Alberta Fish and Wildlife Capture and Research permits (#56,066 and #56,065), and the City of Edmonton Parks permit. All methods are reported in accordance with ARRIVE guidelines. More

Resistance of H. zea populations to Cry1AcTo measure variation in resistance of H. zea populations across field locations during 2017 and 2018, larval offspring of insects collected from non-Bt maize were subjected to a diet-overlay bioassay containing a diagnostic concentration of Cry1Ac (29 µg/cm2) corresponding to the mean LC95 of four Cry1Ac susceptible H. zea populations. Overall, larval survivorship varied significantly among years (Fig. 1).Figure 1Survival of H. zea larvae collected in 2017 and 2018 following exposure to 29 µg/cm2 Cry1Ac in diet-overlay assay. Dashed line is mean survival of a known susceptible field population. ***Years significantly different F = 25.38; df = 1, 58; P  More

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Isolation of marine sediment-derived actinomycetes from west-central PhilippinesThe geographical sites identified in this study were evaluated to explore the actinomycete distribution in west-central Philippines (Fig. 1a). A total of 16 sediment cores were recovered from the 11 sampling sites and were processed in the laboratory using cultured-dependent actinomycete isolation (Supplementary Table S2). The seawater’s physicochemical conditions in all sampling sites were uniform with pH 7.0 and salinity ranging from 3.1 to 3.2. The characteristics of marine sediments and the distance of the actual collection sites identified using the given criteria varied per sampling location. The sediment characteristics vary from coarse to fine sand with mixture of broken corals and pebbles (Supplementary Table S3). Culture-dependent isolation revealed that actinomycete-like colonies and spores were observed in the minimal marine media after 30 to 60 days of incubation. Actinomycete isolates were repeatedly sub-cultured in enriched marine medium 1 (MM1) to obtain pure cultures as shown in Fig. 1b. Actinomycete growth observed in enriched media was white to gray aerial spores with brown to yellow mycelia or without diffusible pigmentations. Notably, there were strains that produced colonies with no diffusible pigmentations.Figure 1Distribution, abundance, and antibacterial activity of marine sediment-derived actinomycetes in the Philippines. (a) The overall map showing the 11 sampling sites situated within the west-central region in the Philippines. The enlarged map showed the details of the sampling sites and their corresponding actinomycetes abundance. Sampling sites are number-coded as shown inside the circle. The color gradient indicates strain abundance. (b) Actinomycete colonies were sub-cultured repeatedly to obtain pure culture of isolates. (c) A total of 92 out of 2212 actinomycetes strains have confirmed antibacterial activities as verified using microbroth susceptibility assay. The map with Streptomyces abundance plot was generated using ggplot2 package in Rstudio ver. 1.2.5042 (https://www.rstudio.com/).Full size imageIn this work, a total of 2212 pure actinomycete strains were isolated from marine sediments collected in 11 geographically distant sampling sites across the west-central Philippines (Fig. 1a). Actinomycete strains were highly abundant in Negros Occidental with 580 isolates (26%), followed by Southern Antique with 348 isolates (16%) and Southeastern Iloilo with 228 (10%). We recovered least actinomycete strains in Occidental Mindoro and South Central Visayas with only 94 and 81 strains, respectively.Antibacterial activity profile of actinomycete strainsWe assessed the antibacterial activities of actinomycete strains against a multidrug-resistant Gram-positive bacterium (S. aureus ATCC BAA-44) and three Gram-negative bacteria (E. coli ATCC 25922, P. aeruginosa ATCC 27853, and E. aerogenes ATCC 13048) using resazurin agar overlay assay and microbroth susceptibility assay as initial and confirmatory screenings, respectively. A total of 218 (9.9%) out of the 2212 actinomycete isolates have antibacterial activities in the initial screening as indicated by positive results or retained blue resazurin color in wells containing actinomycete overlaid with the test pathogens (Supplementary Fig. S2). The 218 active isolates were fermented to produce biomass for secondary metabolite extraction and to confirm their antibacterial activities by microbroth susceptibility testing. Figure 1c showed the confirmed 92 (4.1%) antibiotic-producing actinomycete strains. The majority of the strains (71 isolates) exhibited activity against Gram-positive S. aureus ATCC BAA-44. Thirty-nine (39) strains (42%) were active against E. coli ATCC 25922. Six strains were active against P. aeruginosa ATCC 27853, while all strains tested were inactive against E. aerogenes ATCC 13048 as indicated with no or less than 50% growth inhibition. Twenty-three (23) active strains targeted 2–3 test pathogens, while 69 active strains were only active against one test pathogen (Supplementary Table S4).Phylogenetic diversity of multiple antibiotic-producing strainsThe 92 active actinomycete strains were further identified and confirmed as Streptomyces species based on genomic analysis of their 16S rRNA and rpoB gene sequences. Comparison of 16S rRNA gene sequences (ranging from 1150 to 1500 nucleotides) and rpoB (700–995 nucleotides) gene sequences with their similar matches in the GenBank verified that the 92 active strains were closely related (97 to 100%) with 19 species under the genus Streptomyces. The nearly complete 16S rRNA and rpoB gene sequences were analyzed in a phylogenetic tree using maximum likelihood algorithms. The 16S rRNA and rpoB gene sequences of active strains reported in the present study were deposited in the GenBank nucleotide database (Supplementary Table S5).Phylogenetic analysis revealed multiple strains with identical 16S rRNA gene sequences which clustered together into 13 major clusters (shown by the colored nodes in the tree) with high bootstrap values ( > 90%) in the phylogenetic tree (Supplementary Fig. S3). Thirty-three strains (36%) were highly similar to S. parvulus presented in red circle. Followed by 12 strains with high similarity to S. enissocaesilis (light blue circle), 11 S. rochei strains (dark pink), six S. mutabilis strains (dark blue), five S. diastaticus strains (light green), four S. kunmingensis strains (green) and three S. geysiriensis strains (light orange). A phylogenetic analysis of rpoB gene sequence was conducted to provide a better resolution of the evolutionary relationship among strains within and between species supporting the taxonomic identity of the phylogenetically identical strains.Similarly, the rpoB gene sequences phylogenetic tree (Fig. 2) showed 13 major clusters that were highly supported with bootstrap replicates  > 90%, except for monophyletic clusters III (Streptomyces sp. strain DSD176) and X (Streptomyces sp. strain DSD1006) with low bootstrap replicates ( 1% abundance) can be recovered in both methods. We also noted that some species were exclusively recovered using a specific method. Eight species were exclusively recovered in DSM compared to 6 species in HSM. All active strains of S. kunmingensis, S. mutabilis, S. sedi and S. olivaceus were only recovered by HSM. Contrary, the active strains of S. carpaticus and S. harbinensis were only isolated using DSM.Carbon source composition of marine minimal media, along with effective isolation techniques, was crucial for the isolation of antibiotic-producing Streptomyces. Among the five minimal marine media used, three carbon sources yielded high isolation rate: glucose, mannitol, and trehalose yielded nine species with 32 strains (35%), nine species with 18 strains (20%), and ten species with 18 strains (20%) respectively. However, only eight species (12 strains, 20%) and two species (12 strains, 20%) were isolated in raffinose, and starch-based media, respectively. In the contrary, high diversity indices were observed in trehalose (2.197), mannitol (2.0), and raffinose (1.979) (Fig. 5a). As expected, the starch-containing media had the lowest isolation rate and diversity as only two species (S. enissocaesilis and S. parvulus) were able to utilize a more complex carbon source.Figure 5Diversity of antibiotic-producing Streptomyces using five different carbon sources. (a) From the five carbon sources in the minimal marine media utilized by Streptomyces strains in this study, mannitol yielded the highest number of active strains, while high diversity was recorded in active strains that utilized glucose (n = 92). (b) Venn diagram of five carbon sources showed that two Streptomyces species can be isolated using all five carbon sources.Full size imageInterestingly, co-isolation of species in different carbon sources was shown in the Venn diagram (Fig. 5b). Eight species can be recovered from at least two different carbon sources, whereas 11 species were exclusively isolated from a specific carbon source. Bioactive S. enissocaesilis and S. parvulus strains were recovered from all of the carbon sources utilized in this study. Active S. rochei were isolated in four media but not in starch-based media. In contrast, more exclusive species were isolated in trehalose with four species (S. harbinensis strains, Streptomyces sp. strain DSD3025, S. pseudogriseolus, and S. xiamenensis). Followed by glucose with three species (Streptomyces sp. strain DSD742, S. carpaticus and S. sedi), and two species each on mannitol- and raffinose-based media (Fig. 5b). The results indicated that diverse Streptomyces species preferred simple sugars-containing one or two sugar molecule as nutrient source compared to complex sugars.
Streptomyces abundance and diversity in geographical sampling locationsBioactive Streptomyces species were widely distributed across the different sampling locations in west-central Philippines (Fig. 6a). Although Southern Antique, Negros Occidental and Negros Oriental have highest number of active strains isolated, we found that Southern Antique, Southern Iloilo, and Western Antique were the most diverse sampling sites (Fig. 6b). We have isolated the greatest number of antibiotic-producing Streptomyces species which were evenly distributed in Southern Antique. This indicates that Southern Antique is stable with many potential niches that can support highly diverse Streptomyces species.Figure 6(a) The abundance profile of 19 antibiotic-producing Streptomyces species in different geographical location across the Philippines revealed that S. parvulus was the most abundant species. (b) Diversity, richness and evenness of antibiotic-producing Streptomyces species in different sites were calculated using vegan package in RStudio ver1.2.5042 (https://www.rstudio.com/). (c) Recovery profile and abundance of antibiotic-producing strain per depth layer, indicated by colored circles and its size, revealed that strains in the bottom sediments were the most diverse as compared to other depth layer and S. parvulus was the most abundant species recovered.Full size imageStreptomyces parvulus emerged as the dominant antibiotic-producing species in this study. Out of the 33 active S. parvulus strains, the majority were isolated in Negros Occidental (11 strains) and Negros Oriental (ten strains). Although Negros Occidental and Negros Oriental have high abundance, its microbial community is highly dominated by one species, S. parvulus, supported by the low species richness, evenness, and diversity (Fig. 6b). This finding implies that these sites may have few potential niches that only a few species dominate. Bioactive S. enissocaesilis strains were recovered in four sampling locations only; specifically, Southern Antique (five strains), Western Antique (three strains), Southeastern Iloilo (three strains), and Northwestern Antique (one strain). Active S. rochei were isolated and evenly distributed in seven sampling locations, but were not present in Western Antique, Northwestern Antique, South Central Visayas, and Tubbataha Reefs. Notably, we observed that no bioactive S. parvulus, S. enissocaesilis, and S. rochei were isolated in Tubbataha Reefs, but antibiotic-producing S. cacaoi, S. psuedogriseolus and S. mutabilis strains were isolated only in Tubbataha Reefs marine sediments. Meanwhile, site-specific species such as S. sedi were recovered only in Occidental Mindoro. The isolation of site-specific species within genus Streptomyces can offer insight on the adaptive capacity of strains to inhibit locally coexisting resource competitors within and among these distinct locations.Distribution of bioactive Streptomyces species at different sediment depthsWe further investigated the distribution of antibiotic-producing Streptomyces strains along with the 110-cm sediment depth in different sampling sites. From the sediments that were partitioned according to depth with five categories at 25-cm increments, heterogeneous distributions of bioactive species were observed at deeper sediment with different dominant species in each depth (Fig. 6c). Although, S. parvulus, S. rochei, and S. enissocaesilis strains were ubiquitous in all depths, several species thrive abundantly in specific depths compared to other species. Streptomyces parvulus was the most dominant species in surface sediment. Meanwhile, Streptomyces rochei and S. enissocaesilis strains were more adapted in surface and sub-bottom sediments, respectively. Depth-specific Streptomyces strains were also identified as follows: Streptomyces sp. strain DSD3025 was isolated in subsurface sediments; S. mutabilis strains were abundant in the middle sediment layer; Streptomyces sp. strain DSD1006 and S. pseudogriseolus strain were recovered from sub-bottom sediments; and Streptomyces strain sp. DSD742, S. albus, S. sedi and S. xiamenensis strains were obtained from bottom sediments. High species diversity was positively correlated with increasing sediment depth, where surface sediments are known to be more prone to dispersal and wash-offs by environmental factors such as deep ocean currents42. Furthermore, the depth-specific species identified largely influenced the species richness in varying sediment depth. More