City Trees

urban-forestry.com 31 Many rural communities also try to replicate the "State of the Urban Forest" reports from large munic- ipalities. While these offer a competitive advantage for grant applications, they are not a well-applied option with opportunistic (budget left-over) funds. Consultants and internal staff need to be clear with the municipality about what will be required to meet different objectives and how collecting more data can help or hinder these processes. Forest Health Surveys Typically, rural forests have stand composition resem- bling their surrounding forests. Consequently, many urban forests of rural communities have reduced species diversity, which can make the forests sus- ceptible to pests. The challenge for many of these communities is that pest management tactics used in forest resource management, such as aerial spraying, are not supported by community residents. Likewise, limited budgets and large numbers of trees can inhibit proactive treatments with pesticides. Our management objectives are dependent upon our understanding of urban forest composition and what pests are impacting our forest. Identification of pests and pathogens can often be done with the assistance of universities and colleges. One of the benefits of being in a continuous forest landscape is that we can employ conventional forest surveys for pathogens. For example, by flying drones over areas of our urban forest, we can look for root diseases and follow up using ground-based surveys. While these surveys should be conducted by skilled practitioners proficient at the identification of signs and symptoms of root diseases, we can use drones to help identify and delineate root disease centers from stand and landscape features, looking for species composition and continuity, and dead trees (Figure 2). I have worked with several communities that allow community members to dispose of brush and woody debris for pickup in the spring and fall. Using these woody debris piles, we have successfully demonstrated that identifying and tracking pest out- breaks and severity is possible across large areas. The woody debris piles are rapidly influenced by changes in forest health, occurring before the impacts are noted by arborists in the area based on calls from homeowners. For this to be successful, it is imperative that we access these debris piles before the piles are congregated or chipped. This lets us track the spatial distribution of different pests at higher resolution and smaller ecological scales. Walking from pile to pile, we go through the woody debris and look for entrance and emergence holes, fruiting bodies, and mycelial fans, as well as any other signs and symptoms of forest pests. While we find some lag time between the arrival of a forest pest in an area and its detection in woody debris piles, there is a significant relationship between the woody debris piles and the state of the stand which spatially correlates to the woody debris pile. Brush pile surveys are an opportunity for biosecu- rity-focused survey methods in rural communities. Because the trees and shrubs removed/pruned are still within the scope of work that can be completed by homeowners, we see the occurrence of pests in the brush piles before we hear about the loca- tion and frequency of pests from local arborists. One reason that we can detect biotic distur- bances in advance of homeowners contacting arborists is that the severity to the stand has not occurred at a large enough extent to result in the homeowner detecting the spread of a pest or pathogen. For example, we observed dwarf mis- tletoe (Arceuthobium spp.) and western pine gall rust (Endocronartium harknessii) in brush piles in the season preceding a spike in tree removal or pruning of the affected trees by local arborists. We have already successfully used this method to detect the presence of Dutch elm disease (Ophiostoma spp.) (DED) in a municipality that had not implemented a DED detection strategy. Consequently, the first known occurrence of DED was found in the brush pile. Five American elm >>

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