PARENT SESSION
Tuesday, August 8, 8:00-11:30 am
COS 18 - Biodiversity II: environmental gradients and habitat factors, Part II
Ballroom E, Ballroom Level, Cook Convention Center
Presiders: J Bossart
Mapping species distributions: spatial inference from environmental data.
Franklin, Janet*,1, 1 San Diego State University, San Diego, CA
ABSTRACT- Ecologists increasingly need to use local measurements to assess change at landscape and regional scales, and statistical or simulation models are often used to extrapolate data in space. Specifically, maps of actual or potential species distributions or habitat suitability are required for many aspects of resource management and conservation planning including biodiversity assessment, habitat management and restoration, single- and multiple species and habitat conservation plans, population viability analysis, modeling community and ecosystem dynamics, and predicting the effects of climate change on species and ecosystems. Predictive mapping assumes that species distributions can be predicted from the spatial distribution of environmental variables that control them. Maps of those environmental variables, or their surrogates, must be available, or easier to map that the species distributions themselves, in order for predictive mapping to be practical or useful for forecasting future scenarios. Mike Austin's framework for spatial prediction of species distributions via statistical modeling has three parts, the ecological model, data model and statistical model. The data model "consists of decision regarding how the data are collected, and measured or estimated" and spatial aspects of the data model are important. This paper discusses aspects of the environmental data model generally used for predictively mapping species distributions. Two paramount issues are the myth of ubiquitous environmental maps, and appropriately linking the spatial scales of the species and environmental data. These challenges are not insurmountable, but they are not trivial.
Key words: species niche, geographic information science, spatial ecology