Philippa Kaur

Experimental site and test cultivars
A field experiment of cover crop planting in a winter fallow field was conducted in Changsha (28° 11′ N, 113° 04′ E), Hunan Province, China, from 2014–2015. The soil in the experimental field was tidal clay, with 1.16% organic carbon, 0.17% total N, and a pH of 6.15.
Experimental design and field management
A randomized block experiment was established with 3 different treatments, including cover crops (Lolium spp. and Astragalus sinicus) with chicken grazing (+ C), cover crops without chicken grazing (− C), and a bare, fallow field (CK). Each field plot covers 140 m2, and there were three replications. To prevent the movement of water between adjacent plots, ridges were covered with a plastic sheet inserted into the soil to a depth of 0.5 m.
Ryegrass and milk vetch were planted on October 10th, 2014, at seed densities of 23 and 40 kg ha−1, respectively. Thirty-day-old yellow chickens were introduced into the field on November 25th. To ensure the homogeneity of the chicken manure inputs, a 3 m × 3 m cage was used during the process of chicken grazing. There were 30 chickens in each cage. Five kilograms of corn flour was fed to the chickens in each cage daily. The corn flour was 1.8% nitrogen. The cage was moved every 7 days in the chicken-grass plot until February 2, 2015. The quantity of in situ chicken manure input into the system within the symbiotic period (69 days) in these plots was estimated to be 96.3 t ha−1 by collecting the chicken waste in an underground container. The underground container was a square with a side length of 50 cm and a height of 10 cm. There were 3 symbiotic periods in these plots, and the chicken waste samples were collected every 12 h for three days. On March 27th, 2015, the average aboveground biomass of the cover crops was 11.7 t ha−1 in the + C plot and 14.4 t ha−1 in the − C plot. All the procedures used in this experiment were conducted in accordance with the Chinese Guidelines for Animal Welfare. The experimental procedures performed in the current study were approved by the Hunan Agricultural University Institutional Animal Care and Use Committee (Changsha, China). Furthermore, all the experimental protocols, including animal handling, were performed humanly, and animal welfare was specially considered. We further confirmed that no animals were harmed or stressed during the experimental period.
The cover crops were incorporated into the soil on March 27th, and all the plots were used to grow double-season rice. The early rice cultivar ‘Zhongjiazao 17’ and the late rice cultivar ‘Xiangwanxian 12’ were used in the experiment, and their growth durations were 109 days and 115 days, respectively. Rice seedlings were transplanted on May 5th and harvested on July 12th for the early-season rice, followed by the late-season rice, which was transplanted on July 25th and harvested on October 30th. The seedlings were 35 and 25 days old in the early and late seasons, respectively. The transplantation density was 30 hills m−2 for the early rice season and 25 hills m−2 for the late rice season.
We supplied nitrogen (N) in the form of urea, calcium superphosphate for phosphorus pentoxide (P2O5), and potassium chloride for potassium oxide (K2O) in the rice growing season. The quantity of N supplied was 74 kg ha−1 in the early rice season and 102 kg ha−1 in the late rice season. Urea was applied three times during the rice season; the ratio of tillering fertilizer to panicle fertilizer (grain fertilizer) was 70:30 in the early rice season and 50:50 in the late rice season. The quantity of P2O5 and K2O supplied was 60 kg ha−1, and the same quantity was applied in both seasons. Potassium chloride was applied twice during the rice season, 50% as basal fertilizer and 50% as tillering fertilizer. The calcium superphosphate was applied as a basal fertilizer before transplantation. Water management was performed according to the technology used for double rice cropping systems (local high-yield cultivation) (Table 4).
Table 4 Experimental design16.
Full size table

Soil chemical properties
Soil samples from the 0–20 cm soil layer were used to determine the soil chemical properties. The samples were collected during cover crop harvesting, early rice harvesting and late rice harvesting. The soil samples were air dried and the soil organic matter was determined using K2Cr2O7 and concentrated H2SO4 and heating. The soil total N was determined with the Kjeldahl method, which involved two steps: (1) the digestion of the samples to convert organic N into ({text{NH}}_{4}^{ + })–N and (2) the determination of ({text{NH}}_{4}^{ + })–N in the digest. The soil C:N ratio was calculated by dividing the SOC concentration by the TN concentration. Soil ammonium N was analyzed using indophenol blue colorimetry. Soil nitrate–N was analyzed using ultraviolet spectrophotometry.
In situ CH4 and CO2 flux measurements
During the rice growing season, in situ CH4 and CO2 flux were measured with a static chamber by circulating the gas within the chamber and pipes of an ultraportable greenhouse gas analyzer (CH4/CO2/H2O Analyzer; Los Gatos Research Corp., USA). The static chamber was a square with a side length of 50 cm and a height of 120 cm. A fluted base consistent with the static chamber was inserted in the soil in advance. On the sampling dates, daytime samples were collected from 9:00–11:00 a.m. and 15:00–17:00 p.m., and nighttime samples were collected from 19:00–21.00 p.m. The testing time in each plot was 5 min. The sampling dates were 170, 185, 199, 215, 230, 252, 268, 291, 304, 322, and 347 days after the chickens were introduced into the field. The samples were collected at intervals of 14 days, plus or minus one day if the weather forecast for a sampling date was rainy.
The temperature inside the static chamber needs to be accurately recorded at a soil depth of 3 cm. Plants (excluding the border plants) were sampled from a 0.24 m2 area of each plot on the sampling date. The plant samples were manually separated into leaf and straw and/or grains. The volume of the plant samples was measured with drainage. The effective volume in the chamber was reduced to subtract the internal plant volume from the chamber. The leaf area was determined with a leaf area meter (LI-3000A, LICOR, Lincoln, NE, USA). Lastly, the plant samples were oven-dried at 70 °C to constant weight to determine the aboveground biomass.
The CO2 (F, g m−2 day−1) and CH4 (F, mg m−2 day−1) fluxes were calculated using the following formula (Eq. 1):

$$ {text{F}} = frac{{{text{P}} times {text{V}}}}{{{text{R}} times {text{A}} times left( {{text{T}} + 273.15} right)}} times frac{{{text{dc}}}}{{{text{dt}}}}, $$
(1)

where P is the atmospheric pressure under standard conditions (101.2237 × 103 Pa); V is the effective volume in the chamber (m3), the difference between the volume of the static chamber and the volume of the plant, fan and temperature recorder; R is a gas constant (8.3144 J⋅mol−1 K−1); A is the area of the chamber cover (m2); T is the average temperature at testing time inside the chamber (°C); and dc/dt is the rate of change in the concentration of CO2 and CH4.
To accurately calculate the CO2 and CH4 fluxes in the paddy field, the daytime and nighttime CO2 and CH4 fluxes on the sampling dates were calculated using the following formulas (Eq. 2–4):

$$ {text{F}}_{{{text{daytime}}}} = {text{ S}}_{{{text{daytime}}}} times {text{M}} times left( {{text{F}}_{{1}} + {text{F}}_{{2}} } right)/{2,} $$
(2)

$$ {text{F}}_{{{text{night}}}} = {text{ F}}_{{3}} times {text{S}}_{{{text{night}}}} times {text{M,}} $$
(3)

$$ {text{F}}_{{{text{day}}}} = {text{ F}}_{{{text{daytime}}}} + {text{F}}_{{{text{night}}}} , $$
(4)

where F1, F2 and F3 represent the values at 9:00–11:00 a.m. and 15:00–17:00 p.m. on sunny days and 19:00–21:00 p.m., respectively; S is the day length (s day−1) on the sampling date; and M is the relative molecular mass of CO2 or CH4 (g mol−1).
Seasonal emissions in CO2 and CH4 were calculated using the following formula (Eq. 5):

$$ {text{T }} = {text{a}} times {1}0 times left( {mathop sum limits_{{{text{i}} = 1}}^{{text{n}}} [frac{{{text{F}}_{{text{i}}} + {text{F}}_{{{text{i}} + 1}} }}{2}left( {{text{t}}_{{{text{i}} + 1}} – {text{t}}_{{text{i}}} } right)] + frac{{{text{F}}_{{text{i}}} + {text{F}}_{{text{n}}} }}{2}} right), $$
(5)

where T (g m−2) is the total seasonal emissions, Fi and Fi+1 are the measured fluxes on two consecutive sampling days, ti+1 − ti is the number of days between the two sampling dates, 10 is the conversion coefficient from g m−2 to kg ha−1, and a is the conversion coefficient of the rice growth period (86/61 in the early season and 132/96 in the late season).
In addition, the period from early rice harvesting to late rice transplanting is 13 days. The emissions were calculated using the following formula (Eq. 6):

$$ {text{T}}_{{{text{ER}} – {text{LR}}}} = {text{ T}}_{{{text{ER}}}} /{86} times {6}.{5} + {text{T}}_{{{text{LR}}}} /{132} times {6}.{5,} $$
(6)

where TER-LR (g m−2) is the total emissions from early rice harvesting to late rice transplanting, TER and TLR are the total seasonal emissions in the early rice season and late rice season, respectively, and 86 and 132 are the number of days from sowing to harvesting in the early rice season and late rice season, respectively.
Soil microbe and dissolved carbon and nitrogen measurements
In 2014, soil was sampled from the 0–20 cm soil layer, and the sampling dates were 10, 28, 56, 74, 120, 170, 183, 199, 215, 234, 252, 268, 294, 301, 322, and 347 days after chicken grazing. Fresh soil samples were taken to determine the soil microbial carbon and nitrogen contents by chloroform fumigation-incubation and K2SO4 extraction. Soil microbial carbon (SMC, mg kg−1) = EC/0.38 and soil microbial nitrogen (SMN, mg kg−1) = EN × 0.45, where 0.33 and 0.45 are the conversion coefficients of SMC and SMN, respectively. EC and EN are the differences in organic carbon and nitrogen between fumigation and nonfumigation based K2SO4 extraction. In addition, other fresh soil samples were used to determine the soil dissolved carbon and nitrogen by K2SO4 extraction.
Yield and its components
When the rice was mature, 10 hills were sampled randomly from a 5 m2 harvest area to determine the yield components. Panicle number was counted on each hill to determine the panicle number per m2. The panicles were hand-threshed, and the filled spikelets were separated from the unfilled spikelets by submerging them in tap water. Three subsamples of 30 g of filled spikelets and 3 g of unfilled spikelets were taken to count the number of spikelets. Based on the spikelets per panicle, the grain-filling percentage (100 × filled spikelet number/total spikelet number) was determined. The grain yield was determined from a 5 m2 area in each plot and adjusted to the standard moisture content of 0.14 g H2O g−1.
Data analysis
The global warming potential (GWP) was the overall GWP of CH4 and N2O emissions per unit rice field (ha). The 100-year radiative forcing potential coefficients relative to CO2 were 25 and 298 for CH4 and N2O, respectively (IPCC, 2007). The net ecosystem exchange (NEE) was the value of Fdaytime, ecosystem respiration (Reco) was the value of Fnighttime, and gross primary production (GPP) was the sum of the NEE and Reco. The means of the indexes were organized in Excel 2016. The SD (standard deviation) of the indexes were determined by descriptive statistics with a 95% confidence interval. Analysis of variance (ANOVA) and multiple comparisons were performed using Statistix ver. 8.0 (2004) to evaluate the effects of planting cover crops and chicken grazing on the SOC, STN, C:N ratio, DOC, DON, SMN, SMC, and grain yield and its components. More

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We hand-collected adult toads (n = 8 individuals per site) from four sites across the toads’ tropical range within Australia, from Townsville, Qld in the east (GPS coordinates: − 19.26, 146.82, 14 m altitude) to Richmond, Qld (− 20.73, 143.14, 218 m altitude), Middle Point, NT (− 12.56, 131.33, 12 m altitude) and Kununurra, WA (− 15.78, 128.74, 49 m altitude) in the west. That transect spans the toads’ 80-year invasion history. Although both temperatures and precipitation exhibit a general east–west cline, the greatest disparities in the duration of hot dry conditions per year lie between the easternmost site (Townsville) and the three other sites (Fig. 1). We recorded toad mass (after gently squeezing the animal in a standardized manner to induce it to empty its bladder) and snout-vent length (SVL) immediately before conducting the trials.
Figure 1

Data from Australian Bureau of Meteorology7.

Mean climatic conditions in the four sites from which we collected cane toads (Rhinella marina) for use in laboratory trials. The red line connects mean monthly maximum air temperatures, the green line shows mean monthly air temperatures, and the blue line shows mean monthly minimum air temperatures. Histograms show mean monthly rainfall.

Full size image

Toads were not fed for three days prior to experiments, to ensure they would not defecate during the experiment and minimize variability in mass due to stomach contents. Toads from all four populations were housed in a room kept at 18 °C, then moved concurrently to a temperature-controlled room set at 37 °C. All toads were in separate containers (ventilated plastic boxes of 1-L capacity), half of which had dry paper towel as substrate whereas the other half had 40 mL of water, enough to keep the ventral portion of the body moist but not the rest of the body.
We measured toad body temperatures at the beginning of the trial, and after 20 min and 40 min, using an infrared thermometer (Digitech QM7215) held ~ 10 cm from the toad’s dorsal surface. At the beginning and end of the experiment we measured internal temperatures with a cloacal probe (Digitech QM7215 with probe attachment), to check that our measurements of external body temperature offer robust estimates of internal temperature also. Cloacal temperatures were taken within 10 s of each toad’s removal from the container. After a trial, toads were kept at a temperature of 25 °C, allowed to fully hydrate and monitored for wellbeing during recovery. No adverse effects of the trials were evident.
We used mixed model repeated measures analysis to identify factors affecting body temperatures of cane toads during the 40-min heating trials. Sex and body mass were used as covariates in the analysis with climate at each collection site (# consecutive months per year with average maximum temperature  > 30 °C and with  More

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Experimental design
Following five focus groups with SERNAPESCA’s head of enforcement and other personnel, we designed and implemented an online survey that targeted fisheries enforcement officers who are responsible for monitoring IUU activities in Chile. The survey was structured to capture expert knowledge on various aspects of illegal activities, as well as the relative experience of the officers. The survey defined illegal fishing as a fishing activity carried out in national jurisdiction waters by national or international boats that is in violation of the national fishing law, conducted without a legal permit, or activities that involve unreported or misreported captures to the authorities. The Director of SERNAPESCA delivered the survey via email to all SERNAPESCA enforcement officers. The list of officers was constructed by the Director (n = 86). The survey was anonymous in that the officers were not asked to report their name nor any information that could be used for identification (e.g., email). Answers to questions were not mandatory; that is, respondents could opt-out of answering particular questions and continue with the survey. The survey was available online for ten weeks, over which five reminder emails were sent to officers requesting them to complete the survey.
The survey, in Spanish, consisted of two sections. First, we asked respondents to rank the magnitude of illegal activity for twenty fisheries on a nominal scale (1–5), along with their relative experience with each fishery (nominal scale, 1–5). The twenty fisheries were selected a priori based on our focus groups and known information about illegal activity. All fisheries were single species, with the exception of four that included multiple species: skates (2 species, Zearaja chilensis and Bathyraja macloviana), kelp (4 species: Lessonia spicate, L. berteroana, L. traberculata, Macrocystis pyrifera), red algae (3 species: Sarcothalia crispate, Gigartina skottsbergii, Mazzaella laminarioides), and crabs (10 species excluding southern king crab: Cancer edwardsi, C. porter, C. setosus, C. coronatus, Homalaspis plana, Ovalipes trimaculatus, Taliepus dentatus, T. marginatus, Mursia gaudichaudi, Hemigrapsus crenulatus). In the second part of the survey, we asked respondents additional questions for four focal fisheries: South Pacific hake (Merluccius gayi gayi), southern hake (M. australis), loco or Chilean abalone (Concholepas concholepas), and kelp. For each fishery, we asked respondents to score on a nominal scale (1–5),

The frequency of six specific illegal activities in the industrial sector: size, gear, season, area, transshipment, and port.

The frequency of six specific illegal activities in the small-scale sector: size, gear, season, area, transshipment, and port.

The participation of illegal activity for six different stakeholders along the supply chain: fisher, purchaser, processor, wholesaler, exporter, and restaurateur.

The utilization of seven infrastructure types in illegal activities: fishing boats, refrigeration trucks, processing plants, markets, transshipment boats, export vehicles, and restaurants.

This study was approved by the Advanced Conservation Strategies and Pontificia Universidad Católica ethics institutional review boards and followed guidelines established by their ethics committees, which complies with national and international standards. The surveys included a written informed consent approved by all interviewees, which acknowledged research objectives and established that the survey was anonymous and that interviewees were free to choose to not answer questions. While all species have common names in Chile (which were used in the survey), we use Fishbase and Sealifebase as the taxonomic authority and for the common names reported here to facilitate comparisions34,35.
Statistical analysis
For both sections of the survey, we used a Bayesian cumulative multinomial logit model to predict illegal estimates. First, we fitted a model for illegal estimates for each of the twenty fisheries jointly. Second, we fitted models for illegal estimates for various aspects of the four focal fisheries (i.e., activities, stakeholders, and infrastructure) in a single analysis for each aspect. In both models, we included a random intercept term for respondent, along with a fixed effect for fishery. We evaluated the role of experience, as self-reported by the respondents, by comparing the difference between the illegal score by a respondent for a fishery and the model prediction for that fishery across respondents. If higher levels of expertise increased or decreased the value of a respondent’s scoring, there would be a relationship between the size of the differences and the level of experience reported for a fishery. Experience may also affect the difference in mean responses (i.e., bias), potentially due to more personal experience over a longer period of time, which would lead to a correlation between expertise and mean illegality scores. Depending on the patterns observed in the data, there are several ways to control for a respondent’s experience in illegality estimates. In our case, we used experience scores as a covariate in the model.
For the twenty fisheries, we used the following model,

$$Prleft{{S}_{ij}=kright}=phi left({tau }_{k}-left({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}+{{varvec{z}}}_{{varvec{j}}}{{varvec{V}}}_{{varvec{i}}}right)right)-phi left({tau }_{k-1}-left({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}+{{varvec{z}}}_{{varvec{j}}}{{varvec{V}}}_{{varvec{j}}}right)right)$$
(1)

in which the probability that the score for the level of illegal landings ({S}_{ij}) for the ith species by the jth respondent is equal to category k, can be represented as a latent continuous variable which is divided into K categories, by K − 1 thresholds at ({tau }_{k}). This latent continuous variable is represented by the cumulative normal distribution, (phi). For a given observation, the regression equation is composed of coefficients multiplied times predictor variables ({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}) plus a design matrix for the random effect, multiplied times the error term for the jth respondent, ({{varvec{z}}}_{{varvec{j}}}{{varvec{V}}}_{{varvec{i}}}) . The probability of that observation falling in category k, (Prleft{{S}_{ij}=kright}), is thus the probability of it being in a category equal to or smaller than k, (phi left({tau }_{k}-left({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}+{{varvec{z}}}_{{varvec{j}}}{{varvec{V}}}_{{varvec{i}}}right)right)), less the probability of the observation being in a category smaller than k, (phi left({tau }_{k-1}-left({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}+{{varvec{z}}}_{{varvec{j}}}{{varvec{V}}}_{{varvec{j}}}right)right)). Implemented in the R statistical language, using the brms package36, the call to fit this model looks like the following:

$${text{Score}}; , sim ;{text{Species}} + {text{Experience }} + left( {{1}|{text{Respondent}}} right),;{text{ data}} = {text{SurveyData}},;{text{family}} = {text{cumulative}}),$$

where Score is ({S}_{ij}) in (1) above, the fixed effects, ({varvec{beta}}{{varvec{x}}}_{{varvec{i}}}) are the experience of the respondent and the species that was scored, and (1|Respondent) denotes a random intercept model, where each has a different intercept term, drawn from a shared error distribution. For more information on the application of this model to ordinal response data, see Burkner and Vuorre37.
For the estimates for the various aspects of the four focal fisheries, we used the following model,

$${text{Response}}; sim ;{text{Species}} + {text{Experience}} + left( {{1}|{text{Respondent}}} right),;{text{data}} = {text{SurveyData}},;{text{family}} = {text{cumulative}}),$$

which is structured as per (1) above, but with the responses to the various focal species questions (i.e., activities per sector, stakeholders, and infrastructure) substituted for the species scores as in (1).
We compared both models with simpler models, including a single-term null model using leave-one-out cross-validation. We did so in the R statistical language using the loo packages36,38,39. Prior distributions for all regression terms were improper flat priors over the real numbers, the default in the brms package for population parameters. The priors on the intercept and the random effects were student t3,0,10 distributions, as per the default for uninformative priors in the brms package.
We carried out a Principal Components Analysis (PCA) with the four focal fisheries as categorical variables and the illegal activity, stakeholder, and infrastructure estimates from the Bayesian cumulative multinomial logit model. For each fishery, we used 10,000 estimates from the model, along with a qualitative variable that represented the different factors (e.g., restaurateur). The latter has no influence on the principal components of the analysis but helps to interpret the dimensions of variability. Principal Components Analysis is especially powerful as an approach to visualize patterns, such as clusters, clines, and outliers in a dataset40. In our case, we sought to visualize whether there were common illegal factors with similar set of scores and whether there was any association between high or low scores of illegal factors and the focal fisheries. We used the FactoMineR package in the R statistical language41. More

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