## 3 Method
### Calculation of Mean Species Abundance
#### 3.3.1 MSA Impact from Land Use  
\(\mathrm{MSA_{LU}}\) values that relate to intensity of management or human uses were obtained from literature reviews [52]. To ensure consistency, we reclassified the original LU/LC maps [40; 41; 42]. See Supplement 1. In addition, we also conducted field surveys to measure plant diversity to assess the percentage of similarity between different degrees of human use of the LU/LC classes and intact ecosystems (i.e., evergreen forest and deciduous forest) using the Sorensen coefficient [55]. The derived average values used for paddy fields, maize area, cash crops, perennial trees, evergreen forest, deciduous forest, forest plantation, settlement and infrastructure, miscellaneous land uses, water body, and shifting cultivation were 0.1, 0.2, 0.2, 0.4, 1.0, 1.0, 0.3, 0.05, 0.2, 0.0 (not evaluated), 0.2, respectively. \(\mathrm{MSA_{LU}}\) value for water was assigned as 0.0 because we calculated MSA values for terrestrial ecosystems only [51; 52].  
\(\mathrm{MSA_{LU}}\) values incorporate the influence of human management. To distinguish between primary vegetation (more pristine), grazed grasslands, and synthetic pastures (deforested areas used for pasture), we compared the potential vegetation map (SYNMAP) generated by [PERSON] et al. [56] to the 2016 LU/LC classes rather than [PERSON] and [PERSON] [57] who interpreted classes from moderate resolution imaging spectroradiometer (MODIS) data. SYNMAP is a new 1-km resolution global land-cover product with improved characteristics for land-cover model parameterization. The overall advantage of SYNMAP is that all classes are properly defined in terms of plant communities.  
In Nan Province, local people usually raise their cattle (domestic cows and water buffalo) in the forest (open-grazing system). In addition, they keep cattle away from cultivation areas to avoid crop damage [36]. The areas the cattle graze usually consist of patches of herbage and patches of grass found in forest gaps or deciduous forests (personal communication with local stakeholders). Therefore, we used the Normalized Differential Vegetation Index (NDVI) [58] to define pasture coverage because the NDVI is a good predictor of forage abundance [59] where ground-based methods are not practical over extensive geographic areas.  
We extracted a cloud-free sub-scene of Landsat-8 TM (path/row 129/046, 129/047, 130/046, and 130/047 taken in February, March, and April 2018). The calculated NDVI values ranged between \(-\)0.44 to 0.83. Water has an NDVI value less than 0, bare soils between 0 and 0.1, deciduous forests/shrubs between 0.2 and 0.5, and dense vegetation greater than 0.5. We normalized the derived NDVI values between 0 and 1 (see equation below).  
\[\mathrm{Normalized\ NDVI}\ =\ \frac{(ndvi_{i}-ndvi_{\mathrm{min}})}{(ndvi_{ \mathrm{max}}-ndvi_{\mathrm{min}})} \tag{4}\]  
where  
\(\mathrm{ndvi_{i}}\): input ndvi value  
\(\mathrm{ndvi_{min}}\): minimum value of derived ndvi values (\(-\)0.44)  
\(\mathrm{ndvi_{max}}\): maximum value of derived ndvi values (0.83)  
The normalized NDVI subtracted from 1 was masked by the LU/LC grid for natural forest and plantation to determine the pasture area for InVEST GLOBIO. A pixel is defined as \"livestock grazing\" if the cell is greater than 0.2 (equivalent shrubs, deciduous forest, and open woodland). If the grassland pixel is lower than the grazing threshold, it will be defined as non-pasture use or dense vegetation, where young biomass usually occurs, in top canopy.  
Cropland intensification is only calculated in the \(\mathrm{MSA_{LU}}\) and does not affect the configuration of natural forest (\(\mathrm{MSA_{I}}\)) and the fragmentation calculated for \(\mathrm{MSA_{F}}\). The proportion of agriculture intensity values rank between 0 and 1. Higher values are associated with intensive management (e.g., water and fertilizer supply and plowing). We defined paddy fields as \"intensive agriculture\" because they are irrigated and frequently fertilized [60].