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This study comprehensively evaluated the risk associations between cause-specific ambulance services, extreme temperatures, and mass concentrations of PM2.5 and its constituents. The significant cold effects on chest pain and headache/dizziness/vertigo/fainting/syncope and heat effects on coma and unconsciousness and lying at public were observed, while the risk of ambulance services of OHCA was elevated in both extreme heat and cold environments. Ambulance services of respiratory distress, lying at public, and OHCA increased as the PM2.5 concentration increased, and the risk was significant at the PM2.5 concentration of 20–60 60 μg/m3 for ambulance services of lying at public and higher than 60 μg/m3 for respiratory distress. After controlling for effects of daily average temperature and PM2.5 concentration, this study still identified the significant effects of sulfate and EC on ambulance services of lying at public and OC on headache/dizziness/vertigo/fainting/syncope as the concentrations of PM2.5 constituents were at 90th percentile.
Limited studies assessed associations between ambulance calls and ambient environment9,13,19,22,23,24,25,26,27. Studies in Emilia-Romagna in Italy23, Brisbane in Australia26, Taiwan19, and Huainan and Luoyang in China22,24, have indicated the numbers of ambulance calls associated with extreme heat; the risks generally increase as the daily temperature exceeds 27 °C19,23,26. However, no consistent finding for cold threshold was identified19,28. Kaohsiung City has a tropical climate (daily temperature ranging from 13.5 °C to 31.5 °C), but it is cooler than cities located near the equator, e.g., Singapore and Manila. Except for ambulance service of OHCA, we found that the significant risks associated with temperature were only identified in environments with extreme temperatures ( 90th percentiles; Fig. 3).
Fine particulate matter (PM2.5) are characterized with a small diameter ( More

Study area
The study area is a one-hectare (ha) subtropical mixed young forest in the age of 20 to 30 years of Hong Kong. It is located at Shek Kong (22.428774, 114.114968), in between the Kadoorie Farm and Botanic Garden (KFBG) and Kadoorie Institute, The University of Hong Kong (HKU) as shown in Fig. 1. This is a forest dynamic plot under ‘The Center for Tropical Forest Science–Forest Global Earth Observatory (CTFS-ForestGEO) (https://forestgeo.si.edu/). It acts as a research base on the forest dynamics, forest biodiversity, carbon sequestration and more, which also provide opportunity for public involvement in scientific research.
Figure 1

The study area: 1 hectare subtropical moist young forest plot, a full demonstration plot of the Forest Global Earth observatory (ForestGEO) project (https://forestgeo.si.edu/), located in Hong Kong in understanding the long-term forest dynamic. The map is generated by Authors using ArcMap version 10.5 (https://desktop.arcgis.com/en/arcmap/).

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The one-hectare forest plot was demarcated by 25 quadrats in 20 m × 20 m each, which were further sub-divided into sixteen 5 m × 5 m sub-quadrats; delineated with permanent marker poles by the professional surveying team. The forest survey was launched on January 11th, 2012 and completed on September 6th, 2012. A total of 63 species, 10,442 individual trees with 20,888 stems were recorded in the site. The stem locations (UTM WGS84 coordinate system) were recorded in nearest 5 cm, and DBH were measured at 1.3 m at breast height in nearest 1 mm. The dominant species are Litsea rotundifolia, Psychotria asiatica, Ilex asprella and Aporosa dioica, which all are native species and accounted for over 80% of stems with the study area. The forest condition of the study area is shown in Table 2.
Table 2 The forest condition and descriptive statistics.
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Methods
A three-stage methodological framework was outlined for this study as shown in Figs. 2 and 3. In the first stage, parameters including DBH, wood density and stem location were recorded for field-measured AGB computation. DBH was directly obtained from site, wood density (in g cm−3) was obtained from the global wood density database43,44 or World Agroforestry database45 (http://db.worldagroforestry.org//wd), up to species level. If the species was not recorded in the database, the value was replaced by its genus averaged wood density value. Tree height was derived from the LiDAR data with reference to the recorded x,y location of the stems. The second stage is LiDAR AGB model derivation. Five allometric models (Model 1 to Model 5) were used to estimate the AGB of individual trees based on field-measured parameters. The allometric model with the lowest model error was selected to compute the ‘Field-measured AGB’, which would be used as the dependent variable to develop the LiDAR-derived AGB model. The LiDAR plots metrics were generated in various plot-size (i.e., 10 m radius, 5 m radius and 2.5 m radius) within the study area. Stepwise linear regression was used to select the important and significant predictors in each regression model. Three different model forms were tested (Model I, II, III as discussed in “Stage 2: LiDAR AGB model derivation” section), The LiDAR derived AGB regression models would be evaluated by means of assumption tests and bootstrapping; as well as cross-validation (CV) in the last stage of model evaluation and validation.
Figure 2

The workflow of the study. Stage 1: allometric modeling to compute ground-truth AGB. Five allometric models were compared and the best one is selected to calibrate model in the next stage. Stage 2: LiDAR AGB Model Derivation, by comparing LiDAR plot metrics derived in different plot size (i.e. 10 m, 5 m, 2.5 m radius). Stage 3: Model Evaluation & Validation on the best model selected from Stage 2 (Source: Authors).

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Figure 3

The graphical abstract of the study (Source: Authors).

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Stage 1: allometric modeling
20,888 stems from 63 species, were planned to be used to develop the allometric models. Amongst, the 81% of the wood density value were measured up to species level (i.e., 51 species) and 19% were up to genus level (i.e., 12 species). However, as the tree height parameter was retrieved from the LiDAR 1 m Canopy Height Model (CHM), 263 stems found no value and thus the sample size reduced to 20,625 stems. The DBH of stems ranged from 1.0 cm to 57.1 cm, with mean of 2.55 cm and S.D. of 2.38 cm.
The selected five allometric models included two pantropical models by Chave et al.16; two subtropical models by Xu et al.46 and one local model by Nichol and Sarker47 were assessed. The inclusion of the subtropical models from Xu et al.46 is because of the similar forest type (i.e., subtropical moist forest from southern China) with our study area; whereas the pantropical models from Chave et al.16 is due to its large database across the pantropical regions yet not being explored adequately its applicability in subtropical forests.
The five allometric models were compared by One-way ANOVA and the Tukey HSD post-hoc analysis48. Model 1 (Eq. (3)) was the best model proposed by Chave et al.16, comprised of wood density (ρ), diameter-at-breast height (DBH) and height (H) as shown below (AIC = 3130):

$$AGB=0.0673times {left(rho {(DBH)}^{2}Hright)}^{0.976}$$
(3)

However, it is very unlikely to have a precise tree height data in a closed canopy49. Therefore, an alternative allometric equation without height was then proposed by Chave et al.16 and adopted as Model 2 (Eq. (4)) in our study (AIC =  − 4293):to represent the subtropical AGB

$$AGB=expleft[-1.803-0.976E+0.976text{ln}left(rho right)+2.673text{ln}left(DBHright)-0.0299left[{(text{ln}(DBH))}^{2}right]right]$$
(4)

The bioclimatic variable, “E”, which made up of three parameters to account for climatic variations: Temperature seasonality (TS), Long-term Maximum Climatological Water Deficit (CWD) and Precipitation Seasonality (PS). The formula of the bioclimatic variable, “E”, is shown below (Eq. (5)):

$$E={left(0.178 times TS-0.938 times CWD-6.61 times PSright)}^{{10}^{-3}}$$
(5)

The monthly mean temperature (℃), precipitation (mm) and evapotranspiration (mm) data were obtained from the Hong Kong Observatory50 in the period of 1981 to 2010 to compute the three variables. E was then computed as 0.261 and was input into Eq. (4) to derive the ground-truth AGB of individual stems.
The AGB models developed by Xu et al.46 were conducted in the subtropical mixed-species moist forest in southern part of China. Two allometric models from the study were selected to represent the subtropical AGB model. Model 3 (Eq. (6)) assumed tree height is available (by extracting from LiDAR):

$$AGB=text{exp}(-2.334+2.118text{ln}(DBH)+0.5436text{ln}(H)+0.5953text{ln}(rho ))$$
(6)

Meanwhile, an alternative allometric model would be used by assuming tree height was not available, which is the Model 4 (Eq. (7)) of this study:

$$AGB=text{exp}(-1.8226+2.4105text{ln}left(DBHright)+0.5781text{ln}(rho ))$$
(7)

Nichol and Sarker47 developed a local allometric model for Hong Kong by harvesting 75 trees from 15 dominant species of Hong Kong. DBH and H within 50 circular sample plots from a variety of tree stands were measured, which were then used to establish allometric model with field measured AGB. The best allometric model was a model using DBH as the sole parameter and thus selected as the Model 5 (Eq. (8)) of this study:

$$AGB=text{exp}(-1.8226+2.4105text{ln}left(DBHright)+0.5781text{ln}(rho ))$$
(8)

Stage 2: LiDAR AGB model derivation
The airborne LiDAR data used in this study was captured by Optech Gemini ALTM Airborne Laser Terrain Mapper and acquired by The Government of the Hong Kong Special Administrative Region from December 1st, 2010 to January 8th, 2011. A total of 5575 ground truth points were generated, with horizontal accuracy of 0.294 m (95% confidence interval (C.I.)). Vertical accuracy was also assessed against the orthometric heights of Hong Kong Principal Datum (HKPD), the average vertical accuracy was 0.1 m (95% C.I.). Multiple returns were recorded per pulse up to four range measurements (i.e., first, second third and last). The LiDAR acquisition parameters are shown in Table 3.
Table 3 The LiDAR acquisition parameters.
Full size table

Prior to generation of the plot metrics, the LiDAR data was pre-processed by creating the ground TIN by extracting only the ground returns. The extraction and processing were performed in ArcMap 10.5 with LAStools extension (https://rapidlasso.com/lastools/) and the FUSION 3.7 (http://forsys.cfr.washington.edu/fusion/fusionlatest.html). The canopy surface was defined using all non-ground returns with height above 2.0 m. The reason of choosing 2.0 m as the threshold was to avoid canopy returns to be mixed with ground returns51. Moreover, ‘all returns’ were used instead of the ‘first returns’, since the former provided more information on the lower canopies or understory51, while over 50% of returned points in our study area were classified as medium or low vegetation. The ground TIN was subtracted from the canopy surface to compute the normalized height value of each canopy point. The LiDAR metrics were then derived from the normalized height point cloud.
To be compatible with the LiDAR dataset and to facilitate the plot delineation procedure, the entire 1 ha study area was divided into circular plots with three plot sizes: (1) twenty-five 10 m radius plots (16,182 stems), (2) one-hundred 5 m radius plot (15,538 stems) and (3) four-hundred 2.5 m radius plots (15,100 stems) as shown in Fig. 4a–d. Circular plots were considered more favorable than rectangular or square plots, since the periphery-to-area ratio was the smallest and thus minimized the number of edge trees52.
Figure 4

The (a) 1 ha rectangular plot clipped into (b) twenty-five 10 m radius circular plots; (c) one-hundred 5 m radius plots; and (d) four-hundred 2.5 m radius plots respectively; for the LiDAR metrics derivation. The maps are generated by Authors using ArcMap version 10.5 (https://desktop.arcgis.com/en/arcmap/).

Full size image

The 59 plot metrics, under the descriptive, height, intensity and canopy cover categories, were derived by the ‘cloudmetrics’ function in FUSION version 3.7. The LiDAR metrics, and its log-transformed metrics were input into stepwise regression model as independent predictors of AGB. Significant predictors were selected (F  1.0 were removed) into the regression model. Three sets of regression models were generated (Table 4) and the allometric models tended to be linear and normal after logarithmic transformation18,19.
Table 4 The input variables for the three regression models.
Full size table

Observing the normal Q–Q plot (Fig. 5a) of the dependent variable (i.e., AGB) and the Kolmogorov–Smirnov statistic (d = 0.216, p  0.200) indicated a normal distribution (Fig. 5b) and which became the Model II. The further log-transformation into Model III did not indicate significant improvement and thus Model II in various plot sizes were to be further explored.
Figure 5

The normal Q–Q plot, in 10 m radius plot size, of (a) raw AGB and (b) log-AGB. After logarithmic transformation, the AGB (dependent variable) was normalized.

Full size image

Assumption tests including test on normality, homoscedasticity and absence of multi-collinearity were conducted. The Normal P–P plot, scatter plots on residuals, ‘Tolerance’ index and Variance Inflation Factor (VIF) were applied to detect the violation of assumptions. If the ‘Tolerance’ index is smaller than 0.1, or VIF is greater than 10, it indicates a significance chance of collinearity53.
Stage 3: model evaluation and validation
AGB regression models were to be evaluated in this stage. The Model R2, Adjusted R2, Mean-absolute-deviation (MAE) and Root-mean-squared-error (RMSE) were reported to indicate the explanatory power of the model. R2 showed the amount of explained variance by the model, MAE was the average magnitude of error without considering the direction (Eq. (9)). RMSE (Eq. (10)) was the square root of the averaged squared residual and being sensitive to outliers (i.e., larger error) as the errors are squared. The RMSE would be reported in the unit of kg/ha.

$$MAE =frac{sum_{i=1}^{n}left|(widehat{y}-y)right|}{n}$$
(9)

$$RMSE= sqrt[]{frac{{{sum }_{i=1}^{n}(widehat{y}-y)}^{2}}{n}}$$
(10)

(widehat{text{y}}) is the ith estimate for AGB of each plot, y is the ith observation of that plot, divided by the (n), denoting the sample size.
However, as the regression model was built with limited sample plots, it might subject to model overfitting. Bootstrapping was adopted to assess statistical accuracy in terms of the confidence intervals54. Ultimately, it provides a robust estimation of the standard errors, confidence intervals for estimates, including the model regression coefficient, mean and correlation coefficients. The model uncertainty of this study was assessed by 1000 runs of bootstrapping. The 95% “Bias-corrected and accelerated (BCa) confidence interval (C.I.)” on the beta coefficient of model predictors was reported.
Leave-one-out cross validation (LOOCV) was utilized for model validation. The model would be trained by all data points except the one being left out for validation55. The Predicted Residual Error Sum of Squares, PRESS, was the sum of the ‘squared deleted residuals’ (SSDR) of the n−1 observation. Predicted R2 was computed by dividing PRESS by the total sum of square residual (SSTO) expressed in Eq. (11). The RMSE of the CV model was also reported as Eq. (12). The RMSE of the CV model without overfitting shall be approximate to that of the original model.

$${R}_{pred}^{2}=1-frac{PRESS}{SSTO}$$
(11)

$$C{V}_{RMSE}= sqrt[]{frac{PRESS}{d.f.}},$$
(12)

d.f. stands for degree of freedom (i.e., d.f. = 21). These model calibration and validation work were conducted in IBM SPSS Statistics version 24. More

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Experimental design and data collection
The data came from a highly replicated field trial (The Farm Scale Evaluations, FSE) designed to test whether the adoption of genetically modified, herbicide-tolerant (GMHT) crops would have led to significant changes in UK farmland biodiversity by comparison with the conventional crops and herbicide management then used23. The FSE sampled the biodiversity in and around 187 spring sown crop fields across the UK using a standard methodology (Supplementary Fig. S5). Post-hoc analyses demonstrated that the trial design had more than sufficient statistical power to test the null hypothesis of no change in biodiversity31 and indicated that the species nodes were fully sampled32,33. Full details of the experimental design and protocols for data collection can be found in Champion et al. 34 and Bohan et al. 35, which we briefly detail here.
The count data for the carabids, weed seedbank and seed rain, and gastropod molluscs came from 66 spring-sown beet, 55 spring maize and 66 spring oilseed rape fields. The fields were distributed across the UK (Supplementary Fig. S5) and each field was sampled for one cropping year23 between 2000 and 2004. The great majority of fields were below 15 Ha in size, and typically varied between 2 and 10 Ha depending upon the crop34,35. The trial used a half-field design, and each field was divided in two with one half sown with a conventional crop and the other a GMHT variety of the same crop. Data from both treatments are used for the analyses presented in this study, giving a total of 374 half-fields.
The pitfall trapping of soil-surface-active invertebrates employed the method described by Brooks et al. 36. Pitfall traps, spaced at 2, 8 and 32 m points along 4 transects running from the field-edge into the cropped area, were opened in the spring (April ⁄ May) and summer (June ⁄ July) and in late summer (August). The weed seedbank was measured by taking 8 soil samples in each field, at 2 and 32 m points on four transects, prior to the crops being sown and after harvest37. The soil samples in each half-field were bulked, placed in germination trays in a greenhouse and the seeds they contained allowed to germinate over the course of 18 weeks37,38. The viable weed seeds available to the carabids were measured as the seed rain onto the soil surface from the weed plants in the field. Eight seed rain traps, placed at 2 and 32 m along 4 of the transects, sampled the rain of weed seed throughout the growing season37. Gastropods were sampled using baited refuge traps at the same positions used for the pitfall trapping in late April and in early August for spring oilseed rape, and in May and August for maize and beet36. All carabids, molluscs and weed seeds sampled were identified as species and counted. For the carabids and molluscs, counts were then pooled, by summation, to give a year-total estimate for each species in each half-field, from which a relative abundance of each species was calculated by dividing the count of that species by the total count for the group of carabids and molluscs, respectively. Total, monocotyledon and dicotyledon weed species counts were pooled, by summation, to give an estimate of these weed seedbanks before (t0) and after (t1) the crop was sown and harvested, respectively.
Network construction
The species sample data were supplemented with carabid dietary information recovered from the literature. We assumed that where a carabid species, A, was noted to consume a resource species, B, in the literature and both these species were present in a half-field, then this trophic interaction was realised6,39,40,41. To standardise the (trophic interaction) sampling effort across all carabid species and reflect the generalist nature of carabid consumers it was assumed, following Honek et al. 42, that each carabid species would consume the same resources as other carabid species within the same genus39,41. A similar generalisation was made at the resource level: where a particular species of carabid was recorded to feed upon one species of gastropod or weed in the literature, we assumed that this carabid would also consume other resource species of the same genus43. This assumption of generalisation was done to reduce the numbers of artefactually isolated species within each network, and to avoid the bias towards more highly-studied species44,45.
The interaction frequency for each realised link between consumer and resource was calculated as the multiplicative product of the consumer and resource relative abundances, under the assumption that species abundance is a predictor of the strength of interaction between species46. Applying a frequency weighting to the links in this manner integrates a quantitative estimate of the interaction strength between species in each network, enriching the simple binary network structure built from presence/absence data. Future consideration of carabid interaction strength prey may include traits, such as body size and mandible type47. Following construction of the carabid-weed seed (herbivore) and carabid-gastropod (carnivore) layers, each carabid species was assigned to an empirical trophic group based upon their role in each replicate multilayer network. Carabid nodes linked only to gastropods were assigned to the ‘carnivore’ grouping, while those consuming only weeds were ‘herbivores’, and ‘omnivores’ were generalist species linked to both gastropods and weeds. Thus, a particular carabid species might be a carnivore, an herbivore or an omnivore in different replicate multilayer networks. Some species may therefore appear in different classifications across the collection of networks; a particular carabid species may be a weed consuming herbivore in some networks, a gastropod carnivore in others and an omnivore in yet other networks.
Regulation of the weeds in the seedbank
Regulation was calculated from the change in total, monocotyledon and dicotyledon seedbank counts between t0 and t1, as:

$${rm{regulation}} = {mathrm{ln}}left( {frac{{t_1 + 0.5}}{{t_0 + 0.5}}} right)$$
(1)

so that for each half-field three metrics of regulation for the total, dicotyledon and monocotyledon seedbanks were calculated. Negative values of this metric indicate a decline in the size of the weed seedbank from t0 to t1. Following Bohan et al. 14, negative relationships between the metric of regulation and carabid counts are indicative of seedbank regulation by these beetles.
Statistics and reproducibility
All analysis was done in R (R Core Team 2019) using the cheddar48, bipartite49 and vegan50 packages. Network plots were created with the HiveR package51. Interaction frequencies were calculated separately for herbivores, carnivores and omnivores in each network.
Species and link turnover across the collection of multilayer networks were measured using Bray-Curtis dissimilarity in the vegan package50. Each network layer is a realisation of the interactions drawn from the composite network52, with the interactions only being contingent on local species composition and abundances. To assess how species and link turnover changes across the herbivore/carnivore gradient, we used the number of herbivores and carnivores within each network as factor levels with which to categorise the networks (i.e. networks with 1 herbivore, 2 herbivores, 3 herbivores, etc.). We ensured that no single network appeared in more than one group by randomly assigning networks to either their herbivore or carnivore grouping, and then calculated the Bray-Curtis dissimilarity between the herbivore and carnivore groups.
The regression modelling between species and link variables was done using Generalised Linear Mixed-effects Models (GLMM) with Gaussian errors. Each field was treated as a replicate as there was no repeat sampling from any one site23. All analyses were appropriately subjected to tests of normality and behaviour of the model residuals53.
Species richness
GLMMs were used to assess the effect that increasing numbers of gastropod species has on the number of weed species, and the number of herbivorous carabids implicated in each multilayer network. Field identity was nested within crop type (spring-sown beet, spring maize or spring oilseed rape), which was nested within management type (conventional or GMHT) and included as a random effect. The species richness of all groups was log(x+0.5) transformed to conform to normality. The relationship between the number of carabid species acting as herbivores and carnivores in each network was assessed using the same model structure.
Link richness
The relationship between the number of links to plant resources and links to gastropod resources for the omnivorous carabid nodes was assessed using GLMMs, with field identity nested within crop type (spring-sown beet, spring maize or spring oilseed rape), which was nested within management type (conventional or GMHT) and included as a random effect.
Link frequency
Due to the extreme distribution pattern of the carnivore and herbivore predation interaction frequencies, the correlation between these two variables was examined on the log(x + 0.5) scale using LOESS smoothing53.
Weed seed regulation
The relationship between weed seed regulation and the count of herbivores or herbivory interaction frequency was modelled with GLMMs. The count of herbivores and herbivore interaction frequency were log(x+0.5) transformed to attain normality. Field identity was nested within crop type (spring-sown beet, spring maize or spring oilseed rape), which was nested within management type (conventional or GMHT) and included as a random effect.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More

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