Number of positive trap catch hours as a function of time since sunrise in hourly bins for <i>H</i>. <i>ligniperda</i> and <i>H</i>. <i>ater</i> and for time since sunset for <i>A</i>. <i>ferus</i>.

<p>Because day length varied as a function of the day of the year during the study a range is provided that encompasses the period when sunrise or sunset occurred. Dashed lines indicate the period where sunset occurred as a function of time since sunrise for <i>H</i>. <i>ligniperda</i> and <i>H</i>. <i>ater</i>. Similarly for the nocturnal <i>A</i>. <i>ferus</i> these dashed lines indicate the period when sunrise occurred as a function of time since sunset.</p

False positive (Type I error) rates as a function of the threshold required for the model to predict ‘Yes’, and false negative (Type II error) rates as a function of the threshold required for the model to predict ‘No’ for <i>H</i>. <i>ligniperda</i>, <i>H</i>. <i>ater</i>, and <i>A</i>. <i>ferus</i>.

<p>Red lines indicate the relationship for the calibration dataset (i.e., full casefile), blue shading indicates the range of the first standard deviation for 100 runs of 4-fold cross validation, with the inner white line denoting mean outcomes. Yellow indicates the maximum and minimum values observed during those 100 runs. The green curve represents the number of standard deviations between the calibration and the average of the 100 runs of 4-fold cross validation.</p

Predicting forest insect flight activity: A Bayesian network approach

<div><p>Daily flight activity patterns of forest insects are influenced by temporal and meteorological conditions. Temperature and time of day are frequently cited as key drivers of activity; however, complex interactions between multiple contributing factors have also been proposed. Here, we report individual Bayesian network models to assess the probability of flight activity of three exotic insects, <i>Hylurgus ligniperda</i>, <i>Hylastes ater</i>, and <i>Arhopalus ferus</i> in a managed plantation forest context. Models were built from 7,144 individual hours of insect sampling, temperature, wind speed, relative humidity, photon flux density, and temporal data. Discretized meteorological and temporal variables were used to build naïve Bayes tree augmented networks. Calibration results suggested that the <i>H</i>. <i>ater</i> and <i>A</i>. <i>ferus</i> Bayesian network models had the best fit for low Type I and overall errors, and <i>H</i>. <i>ligniperda</i> had the best fit for low Type II errors. Maximum hourly temperature and time since sunrise had the largest influence on <i>H</i>. <i>ligniperda</i> flight activity predictions, whereas time of day and year had the greatest influence on <i>H</i>. <i>ater</i> and <i>A</i>. <i>ferus</i> activity. Type II model errors for the prediction of no flight activity is improved by increasing the model’s predictive threshold. Improvements in model performance can be made by further sampling, increasing the sensitivity of the flight intercept traps, and replicating sampling in other regions. Predicting insect flight informs an assessment of the potential phytosanitary risks of wood exports. Quantifying this risk allows mitigation treatments to be targeted to prevent the spread of invasive species via international trade pathways.</p></div

Model influence runs.

<p>Presented as tornado diagrams [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183464#pone.0183464.ref048" target="_blank">48</a>] that have a grey and a black portion, to the left and to the right, of the expected ("normative") outcome where all other covariates are set to their prior probability (expected, normative) distributions. Gray portions of the bars show the variables potential range influence on reducing flight probability; black portions show its potential range of influence on increasing flight probability. Variables are sorted in order of decreasing overall influence for each model.</p

Results from the final General Additive Models (GAMs) for the flight activity of <i>H</i>. <i>ligniperda</i>.

<p>GAMs have a parametric component and a smoothing part, hence the distinction between parametric coefficients and the smoothing terms. s() = smooth term for a continuous variable, <i>SE</i> = standard error of the estimate, <i>t</i> = <i>t</i>-statistic, <i>P</i> = <i>P</i>-value, <i>edf</i> = estimated degrees of freedom, <i>F</i> = <i>F</i>-statistic. Wdspd = Wind speed, PAR = Photon flux density, and RH = Relative humidity. Significant values are denoted with P <0.05 = *, P <0.01 = **, P <0.001 = ***.</p

Summary of BN model performance.

<p>Model performance is assessed at different predictive thresholds with both calibration (entire dataset) and validation (4-fold cross validation) results presented.</p

Hourly catch per trap averaged across all traps at the four study sites.

<p>Separate panels indicate the three discontinuous time periods when sampling was undertaken.</p

Description of the best model for each species.

<p>Description of the best model for each species.</p

Sensitivity analysis of the Bayesian network models (Fig 3), showing degree of sensitivity of insect flight probability to each predictor variable.

<p>Sensitivity analysis of the Bayesian network models (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183464#pone.0183464.g003" target="_blank">Fig 3</a>), showing degree of sensitivity of insect flight probability to each predictor variable.</p

Bayesian networks models of flight activity of three forest insect species.

<p>Bayesian networks models of flight activity of three forest insect species.</p