## 2 Materials and Methods
### Environmental Predictors of Virtual Pests  
Multispectral images captured via UAS were used to generate three outputs: one multispectral orthomosaic (blue, green, and red edge bands), one digital surface model (DSM), and one digital terrain model (DTM). These products were created using Agisoft Photoscan (V1.2 for Debian Linux distributions), an image analysis software widely used in PA that facilitates the creation of maps from UAS imagery [23; 24]. The native resolution of such outputs was re-scaled from 20 cm\({}^{2}\)/pixel to 1 m\({}^{2}\)/pixel to avoid computationally heavy processes and still achieve very high spatial resolution in our results. Four sub-products were obtained from the manipulation of spectral data. Red edge, green and blue bands of the orthomosaic were used to calculate a vegetation index highly correlated to plant metabolism and stress (i.e., single-band normalized differences vegetation index or SI-NDVI [21]). SI-NDVI (NDVI from now on) values facilitated the estimation of fractional vegetation cover (FVC) based on methods described by [PERSON], [PERSON], and French [25]. This measurement of plant area per surface unit is closely related to leaf area index and evapotranspiration [26; 27]. The available DTM was used to compute maps of flow accumulation and topographic roughness index (TRI) using algorithms implemented in functions \"r.terraflow\" [28] and \"r.tri\" [29] of GRASS GIS 7 [30]. Within-field patterns of crop cover and terrain features are known environmental drivers of different agricultural pest species [27; 31].  
Microclimatic data logs were summarized into averages, maxima, and minima of every sampled variable (i.e., ambient temperature, relative humidity). Using logger coordinates and the inverse distance weights algorithm (IDW) implemented in the function \"v.surf\" of GRASS GIS 7, three raster maps were created to represent the experimental orchard in terms of average temperature, mean relative humidity, and vapor pressure deficit (VPD). This last variable is closely related to evapotranspiration and ecosystem function [32] and was calculated by implementing [PERSON]'s equation based on temperature and humidity data [33]. IDW is a statistical interpolation technique historically used to generate surface models of climatic variables and within-field pest distributions [34; 35]. Although methods based on semivariograms (e.g., kriging) are a more standard approach to interpolate point-based data in PA [11; 36; 37], IDW is much simpler to implement, is less demanding in computing power, and is readily available in practically every GIS software today. The resulting interpolated maps were not accurate representations of the environment associated with the experimental orchard but reductionist models that simplified the evaluation of complex environment-pest interactions in geographic space. The influence exerted by climatic conditions over the distributional patterns of pests is well known [38; 39; 40].  
Soil EC, soil pH and presence-absence data collected from citrus trees were also spatially interpolated using the IDW algorithm as in [PERSON] and Lesch [41] and [PERSON] et al. [8]. Soil pH and EC are relevant variables for agriculture due to their close relationship to crop productivity and soil physical properties, respectively [42; 43]. Citrus foot rot and brown rot are different manifestations of _Plytophthora_ sp. infections that affect crop productivity and facilitate the establishment of secondary diseases [44].  
Twelve digital maps of environmental features were generated (in TIFF format). Variable names, codes, and methods used to compute them are presented in Table 1.