## 3 Results
### The Influence of Human Factors, Geography, Climate, and Vegetation on Captures  
The parameters of the univariable Poisson models fitted to the data are shown in Table 2. It is, however, important to underscore that the validity of these models applies only between the intervals observed for the minimum and maximum values of the independent variables (consult Table 3), as these are the values contained by the sample. Any other extrapolation for values outside the sampling values cannot be validated. \begin{table}
\begin{tabular}{c|c c c c|c c c} \hline
**IV in the Model** & \multicolumn{4}{c|}{**Primary Workers**} & \multicolumn{4}{c}{**Secondary Workers**} \\ \hline \multirow{3}{*}{Ner of beehives} & \multirow{2}{*}{\(p\)**-Value**} & **AIC** & \(\beta\) & **Exp(\(\beta\))** & \(p\)**-Value** & **AIC** & \(\beta\) & **Exp(\(\beta\))** \\  & \textless{}0.001 & 11,399 & 0.058 *** & 1.060 &  
The first impression taken from the models is that those fitted to the DV count of captures for primary workers (PWs) and those fitted to the DV count of captures for secondary workers (SWs) reiterate each other. The parameters of the models are all highly significant, with \(p\) < 0.001, all have the same direction (all positive), and as the number of captures for secondary workers is higher, the values of the parameters are higher as well in the respective models. From the models we can infer that:  
As the number of beehives in the apiary increases, so do the captures, at a rate of 7.5% per beehive. Albedo also positively affects captures, with rates increasing by 0.3% and 0.4% per unit for the Primary Workers Model (PWM) and the Secondary Workers Model (SWM), respectively. The number of captures increases with the aridity index, and thus with increased humidity or decreased evapotranspiration, by 344.4% for the PWM and 464.9% for the SWM. These high values are explained by the low variation in the sampled values observed, 1.091 and 1.420, respectively, for the minimum and maximum. Therefore, a variation of a centresilant part of the unit in the index results in increased capture rates of 3.44% and 4.649% for the PWM and SWM, respectively. A greater distance from a main river leads to an increase in captures, with captures increasing by 50.9% and 71.5% per kilometre for the PWM and SWM, respectively. The number of captures also increases with elevation and slope, at rates of 0.08% and 0.1% per meter (for the PWM and SWM, respectively) for the former, and 29.4% and 38.6% per degree of slope angle (for the PWM and SWM, respectively) for the latter. Captures increase with precipitation at rates of 0.03% and 0.04% (for the PWM and SWM, respectively) per accumulated millimetre. Higher precipitation seasonality also results in a greater number of captures, with rates of 109.4% and 113% for the PWM and SWM, respectively. Again, these high rates are due to the small variation in precipitation seasonality values in the samples (with a minimum of 47.962 and a maximum of 51.773). Captures also increase with temperature, at rates of 3.4% and 4.6% (for the PWM and SWM, respectively) per accumulated degree. Higher temperature seasonality also results in an increased number of captures, with rates of 0.01% for both the PWM and SWM. The Normalized Difference Vegetation Index (NDVI) positively influences the number of captures in both the PWM and the SWM, with rates increasing by 2.5% and 3.4% per index unit, respectively. The number of captures also increases with population density, at rates of 0.9% and 1.2% for the PWM and SWM, respectively, per person increase in population density. Finally, captures increase with mean wind speed, at rates of 209.1% and 267.4% for the PWM and SWM, respectively. These values should be interpreted