Philippa Kaur

StatementsWe report our study in accordance with ARRIVE guidelines.Structure of artificial turf of ICUA schematic illustration of a ground plan of the artificial turf sports field of the ICU is shown in Fig. 1. This artificial turf was installed in 2013 by Japanese company B. The field is surrounded by ditches, and there are three drains that connect to sewer pipes. The artificial turf field of TGU was installed in 2011 by Japanese company C.Characterization of rubber tips of artificial turf field of ICU and TGURT were collected from the artificial fields of ICU and TGU. RT for the artificial turf field of ICU were made of residual part of rubber for making tires, window frames and windshields of automobiles. RT of ICU consists of a mixture of EPDM (ethylene-propylene-diene) and SBR (styrene-butadiene rubber) (personal communication from a Japanese company B). The RT of TGU was made of rubber of the residual part of rubber for making tires, window frames, etc. (personal communication from a Japanese company C). Information on raw material of the RT was not manifested.RT collected from the fields (ICU and TGU) was sieved to estimate the particle sizes. The RT of the ICU varied from 0.053 to 3.35 mm, and that of TGU varied from 0.212 to 3.35 mm. The specific gravity of the RT was obtained as follows: A certain amount of RT was weighed and poured into a 10 ml graduated cylinder containing some water. The total volume of the RT was obtained by measuring the rise in the meniscus of the water. The specific gravities of the tested RT were 1.28 (ICU) and 1.28 (TGU). Elemental analyses of RT (ICU and TGU) were conducted using micro-PIXE line analysis47, and calcium, sulfur, zinc, and iron were detected, but lead was under the detection limit from the RT of both ICU and TGU.Sampling of sediments in the ditches of the fieldTo examine the migration of RT from the field to the ditches, approximately 200 g of sediments in the ditches was sampled at four different sites, D1–D4 (Fig. 1), in the ICU. The ditch surrounding the field is made by connecting U-shaped concrete blocks and concrete lids. The inner width, length, and depth of the block are 24, 60, and 24 cm, respectively. The size of the lid is 33 × 60 × 4.5 cm with 1.5 × 9.0 cm snicks at short sides, which make an opening of the ditch of 3.0 × 9.0 cm size between two lids.Each sample was weighed (wet weight) and washed with water using a fine sieve to remove the soil. After the removal of the soil, the sediment was dried, and RT was collected manually. The collected RT was weighed, and the percentages of RT in the sediments were calculated (weight/weight).Goldfish and crucian carpA common variety of goldfish C. auratus of different sizes were obtained from a fish merchant in Saitama Prefecture and from a pet shop in Tokyo and then kept in the ICU. Approximately 200 fish of four different sizes (large, body weight (BW) ~ 100 g; medium, BW, ~ 30 g; small-medium, BW, ~ 15 g; small, BW ~ 4.0 g) were kept in three 800-L stock tanks maintained at 20 °C under a 16-h light/8-h dark (16 L/8 D) photoperiod (lights on at 06:00). Small body size fish were kept in a floating cage in one of the stock tanks. The fish were fed commercial floating goldfish feed (Itosui) once a day ad libitum. The fish stock tanks had circulation filtration systems equipped with sand filters. The filter was cleaned every week to maintain the water quality. The health condition of the fish was judged by their appetite. All the experimental fish (mixed sex) in the present study were kept in stock tanks for over two weeks before they were used for experiments. A total of 127 goldfish were used for the present study. The sample size of each experiment was determined by the results of preliminary experiments. Our preliminary survey confirmed that the fish feed we used did not contain RT-like substances. Therefore, the sample sizes of the control groups (goldfish) were smaller than those of the experimental groups to sacrifice fewer fish. All goldfish and crucian carp experiments were conducted in the ICU.Approximately 30 wild juvenile crucian carp C. auratus subsp. 2 weighing 1.4–4.6 g were obtained from a fish merchant in Saitama Prefecture and kept in an 800-L stock tank in the same conditions as that for goldfish. A total of 16 crucian carp were used for the present study.For the experiments, fish were transferred from the stock tanks to experimental 60-L glass aquaria, which were maintained at 20 °C under a 16-h light/8-h dark (16 L/8 D) photoperiod (lights on at 06:00). The experimental aquaria had a running water system, and dechlorinated tap water was added at 20 ml/min. Plastic box filters were also set to each experimental aquarium to maintain water quality. When stock fish were transferred to experimental aquaria, fish were randomly allocated to the aquaria. All the methods for using goldfish and crucian carp were performed in accordance with the guidelines of the Animal Experimentation Committee of International Christian University. The conduct of the present study was approved by the Animal Experimentation Committee of International Christian University.Co-ingestion of feed and RT by goldfish of three different body sizesWe examined whether RT are ingested by goldfish with feed and whether the body size of fish affects the ingestion of RT using three different body sizes of fish, large, medium, and small. First, we conducted an experiment using large body size fish (N = 24; BW, 91.9 ± 21.6 g, mean and SD). Three goldfish of large body size were transferred from the stock tank to the experimental 60-L glass aquarium and kept for three days for acclimation of the environment and sinking fish feed. Fish were fed 3.0 g of large-size feed (Japan Pet Design Co. Ltd.) once a day. On the fourth day, fish were fed a mixture of RT collected from the field (ICU, 300 mg) and large feed (3.0 g). Control fish were fed only fish feed. At 90 min after feeding, the fish were transferred to a pail containing 0.05% 2-phenoxyethanol solution and deeply anesthetized. After body weight measurement, fish were dissected. We observed the intestine to determine whether RT was ingested. When RT was observed in the intestine, we collected the tips and counted the number of tips in each fish. The experimental tests were repeated eight times, and the data were combined.Second, we conducted an experiment using medium body size fish (N = 24; BW, 30.4 ± 12.4 g). Three goldfish of medium body size were transferred from the stock tank to the experimental 60-L glass aquarium and kept for three days for acclimation. Fish were fed 1.0 g of medium-size feed (Kyorin) once a day. On the fourth day, fish were fed a mixture of RT (ICU, 300 mg) and medium feed (1.0 g). Control fish were fed only fish feed. At 90 min after feeding, fish were anesthetized and dissected, and then the intestine was observed as described above. The experimental tests were repeated eight times, and the data were combined.Third, we conducted an experiment using small body size fish (N = 40; BW, 4.4 ± 1.5 g). Four goldfish of small body size were transferred from the stock tank to the experimental 60-L glass aquarium and kept for three days for acclimation. Fish were fed 0.5 g of small-size feed (Kyorin) once a day. On the fourth day, fish were fed a mixture of RT (ICU, 300 mg) and small feed (0.5 g). Because of the small size of fish, RT of small size particles (212–500 µm) were collected with sieves and used for the tests. Control fish were fed only fish feed. At 90 min after feeding, fish were anesthetized and dissected, and then the intestine was observed as described above. The experimental tests were repeated ten times, and the data were combined.In the first three experiments, all three control groups showed no ingestion of RT. From the results of the three experiments, it was clear that our experimental system was not contaminated with RT. Therefore, we omitted making control groups for further experiments to decrease the number of fish sacrificed from the standpoint of fish welfare.Fourth, we examined whether RT collected from TGU was ingested by goldfish. We conducted an experiment using large body size fish (N = 12; BW, 140.3 ± 27.0 g). Three goldfish of large body size were transferred from the stock tank to the experimental 60-L glass aquarium and kept for three days for acclimation. Fish were fed 3.0 g of large-size feed once a day. On the fourth day, fish were fed a mixture of RT (TGU, 300 mg) and large feed (3.0 g). At 90 min after feeding, fish were anesthetized and dissected, and then the intestine was observed as described above. The experimental tests were repeated four times, and the data were combined.We conducted an additional experiment with a similar design to those of the four experiments to take photographs of the fish and RT using fish of small-medium body size (N = 9; BW, 12.8 ± 2.7 g). Three fish were transferred from the stock tank to the experimental 60-L glass aquarium and kept for two days for acclimation. Fish were given 0.5 g of medium-size feed once a day. On the third day, fish were given a mixture of RT of ICU (30 pieces; size 0.5–1.0 mm) and medium feed (0.5 g). At 60 min after feeding, fish were anesthetized and dissected, and photographs of RT in the intestine were taken. The experimental tests were repeated three times, and the data were combined.Active ingestion of RT by goldfishWe examined whether goldfish actively ingest RT when RT are given without fish feed using large body size fish (N = 9; BW, 122.4 ± 20.8 g). Three fish were transferred from the stock tank to the experimental 60-L glass aquarium and kept for three days for acclimation. Fish were fed 3.0 g of large-size feed once a day. On the fourth day, fish were given 300 mg of RT (ICU) on the bottom of the aquarium. At 90 min after the placement of RT, fish were anesthetized and dissected, and then the intestine was observed as described above. The experimental tests were repeated three times, and the data were combined.Retention and elimination of ingested RT in the intestine of goldfishWe examined how long RT was retained in the intestine using large body size goldfish (N = 9; BW, 101.6 ± 11.4 g). Three goldfish were transferred from the stock tank to the experimental 60-L glass aquarium and kept for three days for acclimation. Fish were fed 3.0 g of large-size feed once a day. On Day 4, fish were given 1.0 g of RT (ICU). At 90 min after the placement of RT, each fish was individually transferred to three experimental 60-L glass aquaria. Then, each fish was fed 1.0 g of the feed. At 24 and 48 h (Day 5 and Day 6) after the transfer, we collected feces from fish and some water from the bottom of the aquaria. We observed whether RT was eliminated from the fish into the aquaria. When RT was observed in the feces and the bottom of the aquarium, we collected the RT and counted the number of RT. On Day 5, after the RT observation, each fish was fed 1.0 g of the feed. On Day 6, after RT observation in feces and water, the fish were anesthetized and dissected. We observed whether the intestine retained RT. The experimental tests were repeated three times, and the data were combined.Ingestion of RT by wild crucian carpWe examined whether wild Japanese crucian carp ingest RT. The experiment was conducted using juvenile crucian carp (N = 16, BW, 2.8 ± 0.9 g). Sixteen fish were transferred from the stock tank to three experimental 60-L glass aquaria (5 or 6 fish per aquarium) and kept for six days for acclimation. Fish were fed with 0.2 g of small-size feed once a day. On the seventh day, fish were fed a mixture of RT (ICU, 30 mg) and the small feed (0.2 g) or RT alone (30 mg). Because of the small size of fish, RT of small size particles (212–500 µm) were collected with sieves and used for the test. Control fish were fed only fish feed (0.2 g). At 6 h after feeding, fish were anesthetized and dissected, and then the intestine was observed as described above. More

Our study is focussed on population trends of large carnivores; a culturally important group32, essential for regulating ecosystem function33. Large carnivores represent an important study group as their population status is unclear, with reports of devastating declines33 contrasted with remarkable recoveries23. Further, as a well-studied taxa with abundant trend and trait datasets, large carnivores present a good system to evaluate important drivers of trends without being impacted by poor inference from missing data34. Finally, as large carnivores are considered indicator species of the overall status of biodiversity within an area35, our inference may provide insight beyond our focal taxa.Population trendsWe sourced population (defined by the authors of the original studies, who reported on population trends for one or more studied groups of individuals) trend information for species in the families Canidae, Felidae, Hyaenidae, and Ursidae of the order Carnivora from two large trend datasets: CaPTrends12 and the Living Planet Database13. The CaPTrends database is the product of a semi-systematic literature search for population trends of large carnivore species (from the families listed above); the dataset possesses trend information for 50 species from locations around the world, and trends are reported in a variety of ways. The Living Planet Database contains population abundance time-series for vertebrates from thousands of sites around the world and is one of the larger population trend datasets. Combined, these datasets produce a cumulative 1123 trends (after removing duplicates and records we deemed unreliable or unsuitable), derived from >10,000 individual population estimates. In the Living Planet Database, and for most records in CaPTrends, trends are reported as a time-series of abundance (or density) estimates. We modelled these time-series with log-linear regressions, where abundance (the response) was loge transformed, and year of abundance estimates was selected as the predictor. We included a continuous Ornstein-Uhlenbeck (OU) autoregressive process to control for temporal autocorrelation in these models. The OU process estimates covariance between abundance values, under the assumption that abundances in time point 1 will be more similar to abundances in time point 2, than time-point 3, 4, 5, etc. Accounting for covariance resolves non-independence within time-series. We extracted the slope coefficient which represents the annual instantaneous rate of change, sometimes called the population growth rate (rt). Alongside the abundance time-series, CaPTrends also has three other quantitative datatypes, all of which we converted into an annual instantaneous rate of change (rt): (1) a mean finite rate of change; (2) estimates of percentage abundance change between two points in time; and (3) time-series’ of population change estimates (e.g. in year 1 the population doubled and in year 2 it halved). We converted all annual instantaneous rates of change into an annual rate of change percentage to improve interpretability. These annual rates of change ranged from −75 to 68%, but the majority of values fell within −10 to 10% (Supplementary Fig. 1a).Alongside the quantitative records, 138 populations in the CaPTrends dataset were only described qualitatively with categories: Increase, Stable, and Decrease. These qualitative records were more common for populations located in traditionally poorer-sampled countries (e.g. with lower human development), so whilst they are less informative (only describing the direction and not the magnitude), we deem them important to reduce bias (Fig. 1). As a result, we used a combination of percentage annual rates of change (N = 985) and qualitative categories (N = 138) as our response in our model (see below), representing 50 large carnivore species.CovariatesFor each population, we extracted sixteen covariates (each z-transformed) that fell into four categories: land-use, climate, governance, and traits. Our covariates were designed to cover a diverse array of factors that could influence population trends in large carnivores (Supplementary Table 1). Each covariate is described briefly in Fig. 2 with full descriptions of how variables were derived in the Supplementary material: Covariates.One of the challenges in identifying how covariates—which can vary in space and time—impact population trends is matching the spatial and temporal scale of the covariate with the population i.e. how much of the population is affected by the covariate at a given point in time. To tackle the spatial element of this problem, we used data on the area of extent of each population (e.g. how large is the spatial extent of the population or monitoring zone) to generate a circular distribution zone around the population’s coordinate centroid. We refer to this as the ‘population area’ hereafter. We then sampled covariate values within each population area, with more sampling points in larger areas (range: 13–295 sampling points, Supplementary Fig. 2b). For covariates which varied over time, we extracted the covariates across the ‘population monitoring period’, which refers to the period (from start to end year) the population was monitored for. However, as evidence suggests there can be a lag period between impact or change and any detectable changes in population abundance3, we tested how 0-, 5-, and 10-year lags in covariates changed model fits and effect sizes. We implemented these lags by extending the start of the population monitoring period backwards for each given lag e.g. for a 10-year lag, a normal population monitoring period of 1990–2000, would then capture covariates between 1980–2000. Sensitivity analysis showed a 10-year lag had the greatest balance of improved model fit, with high taxonomic and spatial coverage (see Supplementary: Sensitivity analysis).ModellingAt its core, our model is a linear mixed effects model, regressing annual rates of change against a combined 23 covariates and interactions, using random intercepts to account for phylogenetic and spatial nesting. The model was written in BUGS language and implemented in JAGS 4.3.036 via R 4.0.337. The model structure is summarised below, with a full description in Supplementary: Modelling.ResponseWe modelled our annual rate of change response with a normal error prior. However, to allow the two different types of population trend data (quantitative rates of change and qualitative descriptions of change) to be included in the same model, we treated the qualitative records as partially known. Specifically, we censored the qualitative records to indicate that the true value is unknown, but it occurs within a specified range, with annual rate changes ranging from −50 to 0%, −5 to 5%, and 0 to 50% within the decrease, stable and increase categories, respectively. The overlapping nature of these thresholds is by design, as we wanted to acknowledge that there is likely a grey area between the different categories. For instance, in one study, a 2% trend could be called stable, whilst a different study would consider this as increasing, our overlapping thresholds address this grey area. Admittedly, our category thresholds were arbitrarily selected—this is as a consequence of there being no strict rules on what population change is needed to be assigned a given category. However, despite being arbitrary, they were still carefully selected. For instance, our censoring range thresholds are similar to the range of the observed change (−75 to 68%). Further, whilst we don’t have a clear definition for what an increasing or decreasing population looks like (is it 1% or 10%), we can be confident that increasing and decreasing populations will fall above and below 0%, respectively. The stable category is most vulnerable to subjectivity, and so without clear definitions, we set a large range e.g. the maximum and minimum value we considered could be plausibly called stable was 5% and −5%, respectively.Many of the qualitative and short-term (brief monitoring period) quantitative records address known data biases as they occur in less-well represented regions, species, and time-periods (Fig. 1). However, these lower quality records can be more prone to error. As a result, we developed a weighting term within the model to inflate uncertainty around trends derived over a short timeframe, with few abundance observations, and less robust methods—see Supplementary: Modelling—Weighted error.CovariatesPrior to modelling, we identified missing values in some covariates (e.g. some species were missing Maximum longevity values), which can be problematic for inference if ignored34. We used imputation approaches38,39 to predict these missing values and recorded the associated imputation uncertainty alongside these predictions. Within our model, we accounted for uncertainty in the imputed estimates by treating imputed values of the covariates as distributions instead of point estimates. Specifically, for each imputed value we assigned a normal distribution defined by the mean and standard deviation of the imputed estimates. This approach allowed us to capture imputation uncertainty and improve inference robustness.With 16 covariates and a further seven interactive effects (23 effects in total), we were conscious of overparameterizing the model. As a result, we split these parameters into three groups: (1) core parameters—which included main effects that were considered likely drivers of population change; (2) optional parameters—which included main effects we considered interesting but with little evidence to-date of any influence on trends; and (3) interaction parameters—which includes the seven proposed interaction terms. We included our core parameters (Change in human density, Primary land loss, Population area, Body mass, Change in extreme heat, Governance, and Protected area coverage) in every model, but used Kuo and Mallick variable selection40 to identify parameters from the optional and interaction groups that improve model fit whilst balancing the risk of overfitting.Random interceptsWe used a hierarchical model structure to account for phylogenetic and spatial non-independence in the data, including species as a random intercept nested with genus, and country as a random intercept nested within sub-regions, as defined by the United Nations (https://www.un.org/about-us/member-states).Model runningWe ran the full model through three chains, each with 150,000 iterations. The first 50,000 iterations in each chain were discarded, and we only stored every 10th iteration along the chain (thinning factor of 10). We opted for a large chain and burn-in due to the model complexity, and to allow a broad selection of parameter combinations to be tested under variable selection. We assessed convergence of the full model on all parameters monitored in the sensitivity analysis, as well as the model intercept, and all 23 main and interactive effect slope coefficients. We checked the standard assumptions of a mixed effect linear model (normal residuals and heterogeneity of variance), and tested the residuals to ensure there was no spatial (Moran’s test) or phylogenetic (Pagel’s lambda) autocorrelation. We also conducted posterior predictive checks to ensure independently simulated values were broadly reminiscent of model predicted values.We report the median slope coefficient and associated credible intervals for each of the main and interactive effects, and produce marginal effect plots for a selection of important parameters. These marginal effects hold all other covariates at zero (which is the equivalent of the mean, as covariates were z-transformed).LimitationsDeveloping macro-scale models of population change is challenging as response data are biased41 and hard to summarise42, and response-covariate relationships are likely complex and numerous2. Within our workflow, we attempted to address these challenges, and overall, this allowed us to achieve a moderate model fit (conditional R2 ~ 0.4). We minimised biases in the trend data by integrating qualitative trends with quantitative estimates, which allowed us to increase the taxonomic and spatial scale of the work. However, biases are likely still present to some extent. For instance, whilst we have population trend data covering the full parameter space of our most influential variable (change in human development), we have more population trends in high human development countries (Supplementary Fig. 20)—given these biases, caution should be used when interpreting results. While we could not avoid some biases, we found inference was similar across different fragments of the data and model structures (Supplementary results: Sensitivity analysis). We also attempted to capture complexity by covering a more comprehensive array of covariates than many previous analyses, but we still lack data on likely important aspects that are cryptic and difficult to measure (e.g. poaching, persecution, culling, and the conservation benefits of being flagship species). Further, there are temporal lags between disturbance-events and observable changes in the population10 and we tested several to incorporate the lag that maximised model fit. However, it is possible that responses to different types of disturbance (e.g. habitat loss and climate change) have different lags, although this has not been quantified. Long lags (the maximum lag we explored was 10-years) may also occur and be associated with slow recoveries, but an absence of longer temporal extents in the response and covariate data largely prohibits this analysis at global scales (long temporal extent data is less available outside of the global north).Counterfactual scenariosTo explore how observed changes in land-use, climate and human development have influenced population trends, we developed three counterfactual scenarios, where we compared observed population change to predicted population change if habitat, climate, and human development remained static. For instance, in the climate change counterfactual scenario, we predicted each population trend using the global model (all covariate parameters) with available covariate data (e.g. land-use, governance and trait covariates), as well as taxa and location data (to provide sensitivity to the models varying random intercepts), but set the climate change covariate data to zero (in this case, change in extreme heat and change in drought). We then subtracted these counterfactual predictions from the observed trends to define ‘Difference in annual rate of change (%)’, whereby a positive value indicates carnivore populations would be in better shape (fewer declines) under the counterfactual scenario, and vice-versa. We summarise counterfactual scenarios by reporting the median Difference in annual rate of change and 95% quantiles across the observed 1123 populations.Socioeconomic development and non-linearity in carnivore trendsGiven the large effect of human development change on carnivore population trends within our counterfactual scenarios, we further explore the potential impacts of human development change (i.e. changes in the socioeconomic standards of society) on the dynamics of potential carnivore abundance change. Specifically, we test how changing the rate of human development growth of a hypothetical low human development country could impact carnivore abundances. We test this by simulating time series of human development change between the years 1960 and 2020 along three common development pathways for low human development countries, each given: (1) a mean rate of change in human development (%) defined as Slow (1.25%), Moderate (1.5%) and Fast (1.75%); (2) a shared deceleration rate set to −0.02% per year—a key feature of the human development data is that as human development grows, its growth rate decreases; and (3) a shared initial human development value which we set as 0.2 (a hypothetical low human development country) at year 1960 (Fig. 4a). All our selected parameter values are representative of the human development data (Supplementary Fig. 2), with the Moderate pathway being largely typical for a country with an initial human development value of 0.2, while Slow and Fast represent plausible extremes.We then used our fitted model (Fig. 2) to evaluate how the three pathways of Change in Human development would affect annual abundance of a hypothetical carnivore. This involved predicting the annual rate of change in abundance using the Change in human development pathways and the marginal effect of the Change in human development parameter from the fitted model—setting all other covariates in the model to zero, which in our z-transformed variables represents the mean. We then used the predicted annual rates of change in abundance to project carnivore abundance up to the year 2020, from an arbitrary baseline abundance of 100 in the year 1960 (Fig. 4c). These projections capture the 95% credible intervals around the human development change model coefficient, and assume constant and average values for all other effects (e.g. primary habitat loss or climate change).Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

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