The author currently studies biological control agents on invasive weeds; specifically the cinnabar moth and it's interactions with non-host native wetland species Senecio triangularis. Listed below is the review of some references pertaining to this current research. Geographic Information Systems is thought by many to be the next frontier in ecology and ecological modeling. While in entomological research GIS has been historically absent, application of this dynamic tool is increasing as we look at entomology with a broader landscape view.
This list of references was complied to meet the requirements of GEO 565, Geographic Information Systems & Science, Dr. Dawn Wright.
The authors of this article used a spatially explicit simulation model to analyze the effects of matrix habitats on the dispersal and colonization rates of the biological control agent released to control the invasive weed, leafy spurge. While this type of modeling is typical in conservation effort of species of concern, it has not been applied to address the variables within a habitat that may inhibit the establishment and success of biological control agents. Within their model Jonsen et al. included variables describing the insect such as dispersal, and local population growth rates. They addressed the invasive weed parameters by mapping the leafy spurge with Trimble GPS devices and used orthorecitified 1:10000 air photos to input the distribution of the shrubs and grasses within the matrix habitat of interest. While the authors’ model failed to capture all of the complexities within the ecological relationship of this biocontrol agent and its host plant and did not fit the observed data; it does lend a starting point for future research and applications of spatial analysis of dispersment and establishment in the biological control agent field of research.
Real, L. A., and P. McElhany. 1996. Spatial pattern and process in plant-pathogen interactions. Ecology 77 (4): 1011-1025.
The authors in this paper review describe and assess in detail different tools and techniques available for the spatial analysis of the ecology between plant and associated pathogen. This paper is meant to be a tool for future spatial research in the field of plant pathology, offering a variety of pros and cons for each particular type of spatial analysis. In detail the authors discus Join-counts, continuous variogram and correlogram autocorrelation and the Mantel test. While the authors relate each class of technique to a small sampled data set, they describe alternative applications where the technique may be more appropriate if not so with their particular data set. With spatial analysis promising to be an important variable of consideration in most scientific research this paper will provide a strong tool for those in the plant pathology field.
Peterson, A. T. 2003. Predicting the geography of species’ invasions via ecological niche modeling. The Quarterly Review of Biology 78 (4): 419-433.
The author of this article built upon previous research and models to predict what plants would invade where. As humans more readily move across a landscape, they often bring invading non-native species with them. By using point occurrence data the author was able to draw inference on the characteristics of particularly successful plant species ecological niche, and from there project where it could become invasive across the landscape. This approach has the potential to change invasive weed management from reactive to proactive. Peterson’s model successfully predicted the potential invasive geography of several invasive weeds; however, there are limitations to this model. The GIS analysis for this model was very time intensive, making it impracticable at the time of publishing. I am curious, with today’s technological advances if this model could become applicable.
Dark, S. J. 2004. The biogeography of invasive alien plants in
A spatial autocorrelation analysis of the distribution of alien invasive weeds in
Stone, J. and L. Coop. 2006. Developing spatial models for predicting Swiss needle cast distribution and severity. Swiss Needle Cast Cooperative Annual Report.
The authors of this study investigate the effects of microclimate as the main determinate of the diseases distribution. To accomplish this investigation more than a decades worth of aerial survey data were combined in GRASS GIS software package. The authors used a 50x50m raster grid to represent the distribution of Swiss needle cast by a quantitative value of intensity for each year of aerial surveys. PRISM climate data including daily max/min and dew point, as well as elevation data was overlaid on the Swiss needle cast pathology layer. As the authors suspected, there was found to be a strong correlation between degree-days from the previous summer and the outbreak severity of Swiss needle cast. With finer scale climatic data input the authors predict even greater degree of accuracy with their model.
Manuel, J., et al. 2004. Extracting more out of relocation data: building movement models as mixtures of random walks. Ecology 85 (9): 2436-2445.
Manuel, et al. analyzed how GPS locations for individual organisms over landscapes through time can be assessed for behavioral patterns and habitat recognition. Using the radio-collar GPS information for relocated elk, the elk could be characterized to being in an “encamped” or “exploratory” behavioral mode and thus habitat type and favorability could be assigned for those locations. Behavioral modes were distinguished by step lengths and intervals, and turning angles. While this approach is common in behavioral ecology, the authors added complexity by accounting for both turning angles and step lengths which would allow for the application of models as opposed to just classification of movement types. The models and statistics presented in this article allow for greater complexity in both behavioral and habitat analysis through GPS locations.
Weiss, S. B. and A. D. Weiss. 1998. Landscape-level phenology of a threatened butterfly: a GIS-based modeling approach. Ecosystems 1: 299-309.
The authors of this study combined weather data, topography and the distribution of a threatened butterfly larvae to create a model used in predicting Bay checkerspot butterfly emergence phenology across a landscape. This model was applied using Arc Info 7.01, the authors combined the sophisticated Arc software with other such software’s as TOPOGRID and SOLARFLUX to account for the three above stated variables. Within ArcInfo land surface analysis tools such as hill shade, aspect and slope were relied upon for their continuous coverage’s across the study area. This raster based coverage is a great improvement upon previous models that simplified variables such as slope and aspect to the 4 standard slopes. This successful modeling will provide the methods that can be adapted for use on predicting the phenology of many other organisms across complex landscapes.
Coop, L. IPM pest and plant disease models and forecasting; for agricultural, pest management and plant biosecurity decision support in the
This comprehensive and interactive website can be starting and ending point to your search for online weather data and how it may relate to entomology and plant pathology. This website can link you to sophisticated models used to show degree-days for your particular study area. A degree-day is comprised of time which the weather temperature was above and or below a specified temperature, an important component of predicting insect distribution and emergence time & rates. This website will also connect you to online data from a variety of weather stations positioned through out your state and or study area; you can download data directly from this site for use in your personal research. Another interesting component of this interactive website is the plant disease models. The authors of this website utilizes GRASS GIS software to project degree day accumulation as a rater coverage across the landscape, for use in emergence times and severity of several common plant diseases and pests. These are only a few of the available applications of this website, created and co-authored by OSU’s own Dr. Len Coop.
Baker, R. H., et al. 2000. The role of climatic mapping in predicting the potential geographical distribution of non-indigenous pests under current and future climates. Agriculture, ecosystems & environment 82: 57-71.
This article provides a review of climate mapping at three complexity levels; current distribution of pest with related climate effects (1) unknown, (2) known and (3) models based on phenology with or without climatic factoring. In this review, relationships explored at each of the 3 levels were explained and associated GIS software’s application described; some of the software that was utilized includes CLIMEX and a Humid Thermal Index mapped in ArcView, ESRI. Several examples were given at each of the three levels of complexity to further reveal when each application would be most appropriate. This review was followed by a critique of climatic mapping utilizing a Drosophila laboratory experiment. Through the authors review and critique it was concluded that while climate mapping can be successfully utilized in the prediction of insect phenology, additional factors such as host plants and inter / intra-specific species interactions should not be discounted.
Rasmussen, B., S. Brubaker and D. Sharratt. Weedmapper.org. 2004. 10 March 2010. <http://www.weedmapper.org/index.html>
This website is a collaborative effort between both federal and state agencies, providing the general public with a wealth of information about noxious weeds present in our state of