Insect migratory flight differs fundamentally from most other kinds of flight behavior, in that it is non-appetitive. The adult is not searching for anything, and migratory flight is not terminated by encounters with ...Insect migratory flight differs fundamentally from most other kinds of flight behavior, in that it is non-appetitive. The adult is not searching for anything, and migratory flight is not terminated by encounters with potential resources. Many insect pests of agricultural crops are long-distance migrants, moving from lower latitudes where they overwinter to higher latitudes in the spring to exploit superabundant, but seasonally ephemeral, host crops. The migratory nature of these pests is somewhat easy to recognize because of their sudden appearance in areas where they had been absent only a day or two earlier. Many other serious pests survive hostile winter conditions by diapausing, and therefore do not require migration to move between overwintering and breeding ranges. Yet there is evidence of migratory behavior engaged in by several pest species that inhabit high latitudes year-round. In these cases, the consequences of migratory flight are not immediately noticeable at the population level, because migration takes place for the most part within their larger year-round distribution. Nevertheless, the potential population-level consequences can be quite important in the contexts of pest management and insect resistance management. As a case study, I review the evidence for migratory flight behavior by individual European corn borer adults, and discuss the importance of understanding it. The kind of migratory behavior posited for pest species inhabiting a permanent distribution may be more common than we realize.展开更多
Multi-level multi-scale resource selection models using machine learning were compared and contrasted for generating predictive maps of jaguar habitat (Panthera onca) in the Brazilian Pantanal. Multiple spatial scales...Multi-level multi-scale resource selection models using machine learning were compared and contrasted for generating predictive maps of jaguar habitat (Panthera onca) in the Brazilian Pantanal. Multiple spatial scales and temporal movement levels were run within several analytical modeling frameworks for comparison. Included in the analysis were multi-scale raster grains (30 m, 90 m, 180 m, 360 m, 720 m, 1440 m) and GPS collaring temporal movement levels (point, path, and step). Various analytical methods were used for comparison of models that could accommodate data structural levels (group, individual, case-control). Models compared included conditional logistic regression, generalized additive modeling (GAM), and classification regression trees, such as random forests (RF) and gradient boosted regression tree (GBM). The goals of the study were to discuss the potential and limitations for machine learning methods using GPS collaring data to produce predictive habitat suitability mapping using the various scales and levels available. Results indicated that choosing the appropriate temporal level and raster scale improved model outputs. Overall, larger level analytical modeling frameworks and those that used multi-scale raster grains showed the best model evaluation with the inherent condition that they predict a broader scale and subset of data. The identification of the appropriate spatial scale, temporal scale and statistical model need careful consideration in predictive mapping efforts.展开更多
基金funded by the Special Fund for Agro-scientific Research in the Public Interest of China (201403031)
文摘Insect migratory flight differs fundamentally from most other kinds of flight behavior, in that it is non-appetitive. The adult is not searching for anything, and migratory flight is not terminated by encounters with potential resources. Many insect pests of agricultural crops are long-distance migrants, moving from lower latitudes where they overwinter to higher latitudes in the spring to exploit superabundant, but seasonally ephemeral, host crops. The migratory nature of these pests is somewhat easy to recognize because of their sudden appearance in areas where they had been absent only a day or two earlier. Many other serious pests survive hostile winter conditions by diapausing, and therefore do not require migration to move between overwintering and breeding ranges. Yet there is evidence of migratory behavior engaged in by several pest species that inhabit high latitudes year-round. In these cases, the consequences of migratory flight are not immediately noticeable at the population level, because migration takes place for the most part within their larger year-round distribution. Nevertheless, the potential population-level consequences can be quite important in the contexts of pest management and insect resistance management. As a case study, I review the evidence for migratory flight behavior by individual European corn borer adults, and discuss the importance of understanding it. The kind of migratory behavior posited for pest species inhabiting a permanent distribution may be more common than we realize.
文摘Multi-level multi-scale resource selection models using machine learning were compared and contrasted for generating predictive maps of jaguar habitat (Panthera onca) in the Brazilian Pantanal. Multiple spatial scales and temporal movement levels were run within several analytical modeling frameworks for comparison. Included in the analysis were multi-scale raster grains (30 m, 90 m, 180 m, 360 m, 720 m, 1440 m) and GPS collaring temporal movement levels (point, path, and step). Various analytical methods were used for comparison of models that could accommodate data structural levels (group, individual, case-control). Models compared included conditional logistic regression, generalized additive modeling (GAM), and classification regression trees, such as random forests (RF) and gradient boosted regression tree (GBM). The goals of the study were to discuss the potential and limitations for machine learning methods using GPS collaring data to produce predictive habitat suitability mapping using the various scales and levels available. Results indicated that choosing the appropriate temporal level and raster scale improved model outputs. Overall, larger level analytical modeling frameworks and those that used multi-scale raster grains showed the best model evaluation with the inherent condition that they predict a broader scale and subset of data. The identification of the appropriate spatial scale, temporal scale and statistical model need careful consideration in predictive mapping efforts.
