An Insect Scale View of the World

Clinton Jenkins, North Carolina State University, clinton.jenkins@gmail.com (Presenter)
Andrea Lucky, North Carolina State University, alucky.ncsu@gmail.com
Beth Gardner, North Carolina State University, lonepinepa@gmail.com
Rob Dunn, North Carolina State University, rob_dunn@ncsu.edu

For large and wide-ranging organisms, the focus of much ecological research, coarse-grained climate or vegetation data are often sufficient for predicting patterns of distribution. However, more than ninety percent of all species on Earth—be they rare or common—are smaller than a bottle cap. At that size, they experience a fine-grained landscape in which regional climate matters, but so too do more local conditions such as forest height, canopy structure, or soil type. The environmental data typically used to model species distributions are also, as often as not, actually surrogates for the variables that directly affect the species. For instance, the amount of precipitation is unlikely to directly affect most animal species, but it does influence the type of vegetation and its productivity, variables likely to have direct influence. We may obtain better results with species distribution models if we use environmental variables more closely aligned with the ecology of the species we attempt to understand.

A challenge is that few environmental datasets are widely available for mapping fine-scale environmental patterns, those of most interest for small and range-restricted species. We address this problem by considering the relative value of climatic, remotely sensed, and other data in predicting the distribution of a well-studied group of Australian ants currently at risk from both deforestation and climate change, the spider ants. Spider ants constitute a small group of large, diurnal ants common in the tropical forests of eastern Australia and they have been collected extensively throughout their range. Furthermore, the limited dispersal capabilities of these ants restrict rapid movements of populations, thus tying the current distributions to recent land use and geologic history. The environmental data we evaluated include traditional climate variables and newly available remote sensing data. While the remotely sensed data are relatively new to the field of species distribution modeling, they are increasingly a standard product of planetary monitoring from satellite.

Presentation Type:  Poster

Session:  Other   (Wed 10:00 AM)

Associated Project(s): 

• Dunn, Rob: Predicting Global Patterns of Ant (and Insect) Diversity and Endemism Using Fine-Grained Remote Sensing Data (ROSES 2008). ...details

Poster Location ID: 155