4.6 Article

Modeling Rangeland Grasshopper (Orthoptera: Acrididae) Population Density Using a Landscape-Level Predictive Mapping Approach

Journal

JOURNAL OF ECONOMIC ENTOMOLOGY
Volume 114, Issue 4, Pages 1557-1567

Publisher

OXFORD UNIV PRESS INC
DOI: 10.1093/jee/toab119

Keywords

grasshopper; rangeland; forecast model; climate; outbreak

Categories

Ask authors/readers for more resources

Grasshoppers pose a substantial threat to North American rangelands, with potential for significant economic damages. Current grasshopper forecasting efforts in the western United States are solely based on previous year's data. This study in Wyoming found that October precipitation and past grasshopper density were among the best predictors for future grasshopper population density, and environmental factors play a significant role in forecasting efforts.
Since the mid-19th century, grasshoppers have posed a substantial threat to North American rangelands as well as adjacent croplands and have the potential to cost the economy millions of dollars in annual damages. The United States Department of Agriculture (USDA) Animal and Plant Health Inspection Service (APHIS) have gone to great lengths to ensure that rangeland grasshopper populations remain below an economic impact threshold across the western United States. However, current grasshopper forecasting efforts by the USDA are based solely on the previous year's grasshopper density and do not take region-specific environmental factors (e.g., climate and topography) into account. To better understand the effects of climate and landscape heterogeneity on rangeland grasshopper populations, we assessed the relationship between grasshopper density survey data from across 56 sites between 2007 and 2017 for four counties in north central Wyoming with 72 biologically relevant geographic information system (GIS)-based environmental variables. A regression model was developed to predict mean adult grasshopper density from 2012 to 2016, which was then used to forecast grasshopper density in 2017. The best-fit predictive model selected using Akaike's Information Criterion (AICc) explained 34.5% of the variation in mean grasshopper density from 2012 to 2016. October precipitation and past mean grasshopper density from 2007 to 2011 were among the best predictors of mean grasshopper density in 2012-2016. Our results also suggest that rangelands in central Sheridan County, southwest Johnson County, and southeast Washakie County are more prone to grasshopper outbreaks. Most importantly, this study demonstrated that both biotic and abiotic environmental variables influence grasshopper density and should be considered in future forecasting efforts.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available