# Spatial Dynamics of Olive Fruit Fly Adults in the Framework of a Monitoring Trap Network
## 2 Materials and Methods
### Data Analysis  
Based on the data collected, the number of adults (males and females) captured per trap per day and the proportion of average BT over UT catches per trap were calculated  
Figure 1: Spatial grid of sampling points according to the different experimental sites in Greece (**a**), Italy (**b**), Lebanon (**c**), Spain (**d**) and Tunisia (**e**). Red squares indicate baited traps and black circles indicate unbaited traps. N and E coordinates are reported in the UTM geographical system. Base maps of the olive groves were obtained from Google Earth Pro version 7.3 (Google LLC, Mountain View, CA, USA). per each date and site. ANOVA was performed to assess the influence of the device (baited or not), sex, country and interactions on the captured individuals per trap. The correlation between BT and UT captures per sampling date was calculated at each site and with combined data, using the Pearson coefficient to examine whether the temporal variation in catches in the two types of traps followed a similar trend. The coefficient r, number of observations (N) and \(p\) values are reported in the results, where N refers to the single dates of trap checking. Correlations were considered strong, moderate or weak for \(p\) values corresponding to \(\leq\)0.0001, \(\leq\)0.001 and \(\leq\)0.05, respectively. Statistical analyses were conducted using SPSS Statistics version 26. The characterisation of the spatial distribution was carried out with SADIE (Spatial Analysis by Distance Indices) software (SADIE Shell Version 2.0). This software is specifically designed for analysing counts of individuals at known locations [19]. The technique seeks to identify areas of clustering of two forms--patch and gap clusters--and quantify the spatial pattern in a sampled population by measuring the total effort of individuals in the observed sample to move to extreme arrangements such as uniform patterns [20]. The degree of non-randomness within a set of data is quantified by comparing the observed spatial pattern with rearrangements in which counts are redistributed among the units by permutation, which is called distance to regularity, from which an index of aggregation was obtained:  
\[Ia=D/Ea, \tag{1}\]  
where D is the value of the distance to regularity for the observed data; \(Ea\) = the mean distance to regularity over the randomisations [21]. Values of \(Ia<1\) indicate regularity; values \(>1\) indicate aggregation. A specific test, with the null hypothesis of a spatially random distribution of counts, was also completed. \(Ia\) was calculated for each sampling date and the whole sampling period for the catches of adults, males and females. A stepwise regression procedure in determining relationships between \(Ia\) and field catches was used to select the variables with slopes different from zero (\(p<0.05\)). Variables included in the analysis were the captures obtained from all traps, both baited and unbaited, expressed as a mean number of individuals per trap per day, and the proportion of baited over unbaited trap catches. Counting data were log-transformed prior to the analysis. Data coming from different countries were analysed together. This analysis was conducted using SPSS software version 26. The SADIE methodology carried out by using the software SADIE N_AShell Version 1.0 was used to measure the spatial association (\(\chi_{\text{k}}\)) between two counts of data by overlaying the cluster maps of the male and female distributions that were previously calculated [20]. The positive value of \(\chi_{\text{k}}\) arises from coincidences of patches or of gaps in both populations, whereas negative values emerge from opposite cluster types. An overall index of spatial association (X) is calculated as the mean of local values. The significance of X was tested by randomisations [22].