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Developing and Testing the Framework
Empirical studies on CNH system change and disease emergence depend on the assembly of a diverse set of independently generated neighborhoodand landscapelevel data accurately matched with spatially aggregated or 'point' level data. In order to make such analyses possible we need an extensive spatial database that includes vector layers representing landscape characteristics (e.g., ecoregions, geology, soils, protected area boundaries, human settlements, road infrastructure) from available hardcopy maps, digital data, aerial surveys, global positioning system (GPS) data, and satellite image feature extraction, e.g., urban features (Zhang et al. 2002) and paddy fields (Xiao et al. 2005, 2006). We have also acquired national census data and other economic, demographic, institutional and cultural databases that describe socioeconomic variables at the household and commune level and that have been linked to biophysical data via a geographic information system (GIS) (Epprecht and Heinimann 2004; Epprecht and Robinson 2007). These databases can also provide information on the location and size of villages, roads, streams, and agricultural fields. Information collected on specific landscapes through interviews with farmers and key informants can be keyed to these databases using handheld GPS devices.
We can use these multivariate databases to test models of general relationships hypothesized between HPAI and urbanization, agricultural change (both crop land and poultry), and habitat alteration at the national level using commune level data. For instance, we can test an a priori specific structural equation model (SEM) based on extant literature. In Fig. 5.2, we present a model in which we hypothesize that outbreaks of HPAI in poultry are associated with variations in three latent constructs: (1) Urbanization, measured in terms of changes in quality of housing and drinking water supplies (Spencer 2013); proximity to cities (Pfeiffer et al. 2007) and major roads (Fang et al. 2008), and human population (Gilbert et al. 2008b); (2) Habitat Alteration, measured in terms of changes in wetlands (Fang et al. 2008; Gilbert et al. 2007), amount and diversity of natural water sources (Fang et al. 2008);
Fig. 5.2 Structural equation models (SEMs) of hypothesized relationships between HPAI outbreak and urbanization, habitat alteration, and agricultural change (both crop land and poultry) at the national level. Model 1 (solid lines) proposes an a priori SEM based on existing literature; model 2 (dashed lines) proposes an explanatory higher-order factor
changes in proximity to wetlands (Fang et al. 2008); and (3) Agricultural Change, measured in terms changes in terms of number and intensity of paddy fields (Gilbert et al. 2007, 2008b; Pfeiffer et al. 2007), and number of ducks and chickens (Gilbert et al. 2006, 2007, 2008b; Pfeiffer et al. 2007).
The second model shown in Fig. 5.2 (dashed lines) tests whether the relations among urbanization, habitat alteration, and agricultural change are attributable to a common higher order influence. While the first model acknowledges the existence of relations among the three latent constructs, it does not explicitly represent cause of covariation. The second model postulates that correlations among the latent constructs can be explained by a higher-order factor. We can thus examine direct and indirect pathways between the latent constructs and HPAI outbreak. The dashed lines in Fig. 5.2 represent the indirect pathways and higher-order factor, which we call CNH system Transition.
Evaluating these models is quantitatively challenging because the concepts of urbanization, agricultural change, and habitat alteration represent a complex multivariate response. Multiple regression analysis of these types of problems are subject to problems of interpretation that include covariances among interacting explanatory variables and an inability to assign unique explanatory capacity to individual factors (Grace and Bollen 2005; Laughlin and Abella 2007). To avoid these problems, techniques such as structural equation modeling (SEM) may be most useful. SEM allows researchers to theorize about why explanatory variables are correlated and to build directional relationships into their models of systems. Explanatory variables are often correlated because they have a common cause or because one factor influences the other (Laughlin and Abella 2007; Shipley 2000). These situations are common in observation studies of complex systems. Consequently a systems approach to the analysis and interpretation of composition in this CNH system may be optimal for explaining where driving forces interact to produce observed patterns of bird deaths across the landscape.
We can not only explore the relationship between HPAI and urbanization, agricultural change, and habitat alteration at the national level, but also examine whether this relationship exists at commune and household scales using focus groups, interviews, and a structured household survey. This is necessary because as numerous researchers have shown, complexity is scale sensitive (Fox 1992; Phillips 1999; Walsh et al. 1999). Processes that operate at one scale may not occur at other scales or resolutions.
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