LB 40-337 - Molecular identification of feather spots at renewable energy installations

Alternative energy production will affect how we meet global energy demands and simultaneously reduce the impact of climate change. The development of new renewable energy infrastructures is expected to increase in the future, with a large proportion of this growth dedicated to solar energy production. However, the effects of renewable energy installations on wildlife are still poorly understood and it is important to quantify and understand the dynamics of their impacts on avian populations, in order to better plan and mitigate the potential effects of future utility-scale infrastructure. An important source of information on avian-solar interactions are remnants collected at solar facilities, including those that appear as “feather spots”, known to be of avian origin but not readily assignable to species via morphology (up to 32% at some facilities), that may not otherwise be included in further downstream analyses.To address this limitation, we optimized a molecular approach to identify samples that can be utilized in parallel with morphology-based identification of species in the field. From DNA of unidentified samples, we amplified and sequenced target regions of the mitochondrial CO1 gene. We then search for sequence similarity from an open-access, national genome database (GenBank), to identify samples to species, populations, and in most cases, to individual. Through DNA barcoding analyses, we have been able to identify to the species level over 88% of previously unidentified samples, and assign these individuals to the distinct breeding populations they belong to. Interestingly, the genetically-identified specimens not only added samples to analyses, but actually presented a different composition of the avian community affected by industry activities compared to data obtained exclusively by morphological identification.Our results suggest that this genetic tool can be used to verify, correct, or compliment identifications made in the field and can produce data that will allow more accurate comparisons of avian interactions across facilities, years, or technology types. Most importantly, this genetic approach can be applied to any unidentified samples of avian origin collected at any renewable energy facilities (e.g., wind farms) or other human-impacted areas and can be utilized to better understand avian populations most at risk from further anthropogenic development, therefore contributing to informed management and siting strategies in the future.