Modelling the distribution of chickens, ducks, and geese in China

Abstract

Global concerns over the emergence of zoonotic pandemics emphasize the need for high-resolution population distribution mapping and spatial modelling. Ongoing efforts to model disease risk in China have been hindered by a lack of available species level distribution maps for poultry. The goal of this study was to develop 1 km resolution population density models for China's chickens, ducks, and geese. We used an information theoretic approach to predict poultry densities based on statistical relationships between poultry census data and high-resolution agro-ecological predictor variables. Model predictions were validated by comparing goodness of fit measures (root mean square error and correlation coefficient) for observed and predicted values for ¼ of the sample data which was not used for model training. Final output included mean and coefficient of variation maps for each species. We tested the quality of models produced using three predictor datasets and 4 regional stratification methods. For predictor variables, a combination of traditional predictors for livestock mapping and land use predictors produced the best goodness of fit scores. Comparison of regional stratifications indicated that for chickens and ducks, a stratification based on livestock production systems produced the best results; for geese, an agro-ecological stratification produced best results. However, for all species, each method of regional stratification produced significantly better goodness of fit scores than the global model. Here we provide descriptive methods, analytical comparisons, and model output for China's first high resolution, species level poultry distribution maps. Output will be made available to the scientific and public community for use in a wide range of applications from epidemiological studies to livestock policy and management initiatives.

Figure 1

(a) Methods used for filling data gaps in total poultry across China, (b) methodology for modeling chicken, duck, and goose distributions for China. RESID = residual poultry at end of year, SOLD = number poultry sold, NSB = National Statistics Bureau, AHB = Animal Husbandry Bureau (see Supplemental Fig. S1 for NSB and AHB relationships).

Figure 2

(a) Observed densities, (b) model predictions, and (c) coefficient of variation, for chickens, ducks, and geese across China. Mean densities and coefficient of variation(standard deviation divided by mean) represent 25 bootstrapped models. Model output shown for the GLW+LU predictors and LPS (chickens, ducks) or CAR stratification (geese) method (defined by goodness of fit scores).

Figure 3

Violin plots of (a) Root Mean Square Error (RMSE) and (b) correlation coefficient between predicted and observed chicken, duck, and goose densities (log transformed) for 3 predictor datasets: GLW (traditional Gridded Livestock of the World predictors), LU (landuse and anthropogenic predictors), and GLW+LU (combination of GLW and LU predictors). ANOVA main effects (P<0.001) and Tukey’s Pairwise Comparisons (all P<0.005) indicate significant differences among all 3 predictor sets with GLW+LU having lowest mean RMSE and highest mean Correlation between observed and predicted values.

Figure 4

Boxplots of Root Mean Square Error (RMSE) and correlation coefficient between predicted and observed chicken, duck, and goose densities (log transformed) for 4 stratification schemes: All.BestRSE (uses prediction from stratification (BestEZ, CAR, or LPS) with the best goodness of fit score on a pixel by pixel basis), EZ.BestRSE (uses prediction from data driven classifications (EZ5, EZ12, EZ25, EZ50) with best goodness of fit score on a pixel by pixel basis), CAR (China Agro-Ecological Regions), and LPS (global livestock production systems). Main effects ANOVA significance values in lower left of each panel; means represented by black circles; Tukey’s pairwise comparisons (p<0.05) denoted by letters; grey boxplots represent statification with best mean GOF, LPS for chickens and ducks and CAR for geese. Although strong differences among stratifications were not evident, all stratifications examined performed better than the global model (i.e., no stratification; P<0.001 see Fig S4).

Figure 5

(a) Root Mean Square Error (RMSE) and (b) correlation coefficients for ducks (log densities) comparing predictions with and without data from surrounding countries. Data are presented as violin plots, a combination of box and kernel density plots (see Hintze 1998). Higher RMSE and lower correlation coefficients for analyses using data from surrounding countries suggest relationships between poultry densities and predictor variables within China are different from surrounding countries and such additional analyses do not improve predictions within China.