Leaf out time correlates with wood anatomy across large geographic scales and within local communities.

There is a long-standing idea that the timing of leaf production in seasonally cold climates is linked to xylem anatomy, specifically vessel diameter because of the hydraulic requirements of expanding leaves. We tested for a relationship between the timing of leaf out and vessel diameter in 220 plants in three common gardens accounting for species' phylogenetic relationships. We investigated how vessel diameter related to wood porosity, plant height and leaf length. We also used dye perfusion tests to determine whether plants relied on xylem produced during the previous growing season at the time of leaf out. In all three gardens, there was later leaf out in species with wider vessels. Ring-porous species had the widest vessels, exhibited latest leaf out and relied less on xylem made during the previous growing season than diffuse-porous species. Wood anatomy and leaf phenology did not exhibit a phylogenetic signal. The timing of leaf out is correlated with wood anatomy across species regardless of species' geographic origin and phylogenetic relationships. This correlation could be a result of developmental and physiological links between leaves and wood or tied to a larger safety efficiency trade-off.

Effects of the COVID-19 pandemic on noise pollution in three protected areas in metropolitan Boston (USA).

Noise pollution can reduce the ability of urban protected areas to provide a refuge for people and habitat for wildlife. Amidst an unprecedented global pandemic, it is unknown if the changes in human activity have significantly impacted noise pollution in metropolitan parks. We tested the hypothesis that reduced human activity associated with the COVID-19 pandemic lockdowns would lead to reduced sound levels in protected areas compared with non-pandemic times. We measured sound levels in three urban protected areas in metropolitan Boston, MA (USA) at three time periods: in the fall and summer before the pandemic, immediately after the government-imposed lockdown in March 2020 when the trees were leafless, and during the beginning of reopening in early June 2020 when the trees had leaves. At all time periods, sound levels were highest near major roads and demonstrated a logarithmic decrease further from roads. At the two protected areas closest to the city center, sound levels averaged 1-3 dB lower during the time of the pandemic lockdown. In contrast, at the third protected area, which is transected by a major highway, sound levels were 4-6 dB higher during the time of the pandemic, likely because reduced traffic allowed vehicles to travel faster and create more noise. This study demonstrates that altered human levels of activity, in this case associated with the COVID-19 pandemic, can have major, and in some cases unexpected, effects on the levels of noise pollution in protected areas.

Citizen scientists and university students monitor noise pollution in cities and protected areas with smartphones.

Noise pollution can cause increased stress, cognitive impairment and illness in humans and decreased fitness and altered behavior in wildlife. Maps of noise pollution are used to visualize the distribution of noise across a landscape. These maps are typically created by taking a relatively small number of sound measurements or simulated on the basis of theoretical models. However, smartphones with inexpensive sound measuring apps can be used to monitor noise and create dense maps of real-world noise measurements. Public concern with noise can make monitoring noise pollution with smartphones an engaging and educational citizen science activity. We demonstrate a method utilizing single-day citizen science noise mapping events and a university lab to collect noise data in urban environments and protected areas. Using this approach, we collected hundreds of noise measurements with participants that we used to create noise maps. We found this method was successful in engaging volunteers and students and producing usable noise data. The described methodology has potential applications for biological research, citizen science engagement, and teaching.