Modeling urban host tree distributions for invasive forest insects using a two-step approach

Background/Question/Methods

Many alien insect species impacting forested ecosystems in North America first appeared in urban forests. Unfortunately, despite serving as critical gateways for the human-mediated spread of these and other forest pests, urban forests remain less well documented than their “natural” forest counterparts.  Only a small percentage of the communities in the US and Canada have completed any sort of urban forest inventory, and these inventories have commonly been restricted to street trees.  To address this knowledge gap, we devised a two-step approach that utilizes the available local inventory data to model urban host tree distributions at a regional scale. We illustrate the approach for three tree genera – ash (Fraxinus), maple (Acer), and oak (Quercus) – that are associated with high-profile insect pests.  Available inventory data include 60 sample-based inventories of entire cities (i-Tree Eco inventories) and 475 street tree inventories. First, based on existing inventories, we use a suite of explanatory spatial variables to estimate the proportion of the total basal area occupied by each genus in non-inventoried communities. Second, we apply a similar suite of spatial variables to estimate the total basal area of these communities. We then combine these estimates to construct region-wide urban distribution maps for each genus.

Results/Conclusions

We first completed models for the eastern US, where urban data were most abundant.  There the modeling approach worked very well for maple and oak.  Our models explained more than 60% of the variation in those species in our sample data.  However, the model fit less well for ash.  We attribute this to the fact that ash abundance is extremely variable in urban forests and does not follow obvious ecological gradients.  Also, most cities in our dataset contained extremely low levels of ash, making it difficult to achieve a good model fit.  Work continues to refine the model for ash and develop models for Canada and the western US.  Ultimately, by merging our urban host maps with similar data on natural forests (e.g., distribution maps developed from US Forest Service plot data), we will be able to provide a more complete host setting for insect and disease spread modeling efforts