A Simple Model for Predicting Annual Numbers of Southern Pine Beetle Infestations in East Texas

Eleven climatic variables, recorded during the period 1966-76, were used to develop a multiple linear regression model for predicting potential number of southern pine beetle (SPB) infestations for east Texas. Four climatic variables were significantly (P < 0.05) related to numbers of SPB infestations. These were (1) mean temperature for February of current year, (2) total rainfall for previous summer, (3) total rain fall for previous fall, and (4) total rainfall for previous spring. The regression analysis accounted for 90.7 percent of the variation in yearly numbers of SPB infestations. The southern pine beetle (Dendroctonus frontalis Zimm.) is the single most destructive agent of southern pine forests. Existing control efforts, although often effective on individual infestations, have seemed to be unsuccessful in reducing general population levels from one year to the next (Coster 1977). In an effort to reduce the severity and cost of damage by this insect, a southwide research and applications program (Southern Pine Beetle Research and Applications Program or SPBRAP) was initiated. Application of control measures by land managers will depend heavily upon their ability to anticipate timber losses due to pine beetle activity. Hence, a simple model to predict annual numbers of SPB infestations would be of considerable value. Several researchers have attempted to delineate environmental factors which directly or indirectly affect southern pine beetle (SPB) populations and activity. Limiting climatic factors examined include moisture availability, moisture abundance, and temperature (Coster 1977). King (1972) compared epidemic and endemic years in order to assess importance of rainfall to SPB activity. Epidemic years were characterized as having (1) low summer rainfall in Georgia, (2) high winter rainfall in Texas, and (3) high spring rainfall and low early summer rainfall in North and South Carolina. In Delaware, Maryland, and Virginia, SPB outbreaks have been associated with prolonged periods of low rainfall (Hansen et al. 1973). Kalkstein (1974) noted that, in Texas and Louisiana, SPB activity was directly related to increased moisture and late winter potential evapotranspiration. In a later study, Kalkstein (1976) developed a regression model designed to predict SPB activity, the most important variable to the regression being evapotranspiration three months prior to the evaluated beetle activity. Sixty-four percent of the variability in SPB activity (measured as increased or decreased number of spots about a mean) was explained by the regression model. Kalkstein's regression model relies heavily on current year's data and makes use of evapotranspiration which may not be readily available to the forest manager. It is apparent that a reliable method of SPB activity over a broad geographic area has not been developed to date. Development of such a method has been limited because: (1) extent of SPB activity is difficult to determine using current technology, (2) number of SPB infestations detected may or may not be an indicator of current activity or population levels, and (3) contradictory environmental conditions have been associated with beetle activity in different geographical areas. Infested locations, large enough to be detected by air, may be actively expanding in size. These areas may also have declining populations or even be inactive at the time of surveilance. TABLE 1. Climatic data for forested east TexasJ Location Temperature Means oF Precipitation (in.) Jan July or Aug Annual Annual Greenville (north) 44 84 64 43 Houston (south) 54 83 69 46 Kirbyville (east) 52 82 67 54 Palestine (west) 49 83 67 41 Mean 48 83 67 46 Nacogdoches (central) 48 82 66 48 • Temperature rounded to nearest whole degree; precipitation rounded to nearest inch. 62 SOUTHERN JOURNAL OF APPLIED FORESTRY There is a need for a simple technique to predict potential timber losses and manpower needs resulting from beetle activity. We present here a regression model which hopefully allows the forest manager to make such predictions in east Texas prior to peak beetle activity.