Natural Resource Ecology Laboratory

Research Findings

  • Pine beetle (Dendroctonus spp.) and ips beetle (Ips pini) infestations in the American Rocky Mountains are expected shift geographically under predicted climate change (Evangelista et al. 2011). The southern Rocky Mountains are likely to have a reduced risk, while the northern latitudes will exhibit greater susceptibility. We used ten years of beetle data with 19 bioclimatic variables to model current infestations. Our results were used to define climatic parameters that were conducive to beetle outbreaks, and were projected to multiple future climate change scenarios.
  • The use of new geospatial models are typically used to predict the occurrence or distribution of a plant species using environmental variables available in a GIS. Evangelista et al. (2009) found that these same models can be used with remote sensing data to map actual distributions as well as or better than traditional methods. The same study also found that a time-series collection of satellite scenes across the growing season can help spectrally distinguish some species (e.g. Tamarix ramosissima) from other plants with similar spectral signatures.
  • Geospatial models can be used to identify range and new populations of rare and cryptic wildlife species. This was demonstrated with the mountain nyala (Tragelaphus buxtoni), an endangered antelope endemic to the Ethiopian highlands (Evangelista et al. 2008a). As a result of this work, four newly discovered populations were identified nearly doubling the estimated extent of habitat and total number of the species in the wild.
  • Invasions of non-native generalist species are more challenging to spatially predict than specialist species. Evangelista et al. (2008b) found that poor spatial model performances for a generalist species (i.e. Bromus tectorum) may indicate a loosely defined niche, thus increasing the probability and risk of a prolific invader.