Defining a spectrum of integrative trait-based vegetation canopy structural types.

Vegetation canopy structure is a fundamental characteristic of terrestrial ecosystems that defines vegetation types and drives ecosystem functioning. We use the multivariate structural trait composition of vegetation canopies to classify ecosystems within a global canopy structure spectrum. Across the temperate forest sub-set of this spectrum, we assess gradients in canopy structural traits, characterise canopy structural types (CST) and evaluate drivers and functional consequences of canopy structural variation. We derive CSTs from multivariate canopy structure data, illustrating variation along three primary structural axes and resolution into six largely distinct and functionally relevant CSTs. Our results illustrate that within-ecosystem successional processes and disturbance legacies can produce variation in canopy structure similar to that associated with sub-continental variation in forest types and eco-climatic zones. The potential to classify ecosystems into CSTs based on suites of structural traits represents an important advance in understanding and modelling structure-function relationships in vegetated ecosystems.

Developing and deploying an integrated workshop curriculum teaching computational skills for reproducible research.

Inspired by well-established material and pedagogy provided by The Carpentries (Wilson, 2016), we developed a two-day workshop curriculum that teaches introductory R programming for managing, analyzing, plotting and reporting data using packages from the tidyverse (Wickham et al., 2019), the Unix shell, version control with git, and GitHub. While the official Software Carpentry curriculum is comprehensive, we found that it contains too much content for a two-day workshop. We also felt that the independent nature of the lessons left learners confused about how to integrate the newly acquired programming skills in their own work. Thus, we developed a new curriculum that aims to teach novices how to implement reproducible research principles in their own data analysis. The curriculum integrates live coding lessons with individual-level and group-based practice exercises, and also serves as a succinct resource that learners can reference both during and after the workshop. Moreover, it lowers the entry barrier for new instructors as they do not have to develop their own teaching materials or sift through extensive content. We developed this curriculum during a two-day sprint, successfully used it to host a two-day virtual workshop with almost 40 participants, and updated the material based on instructor and learner feedback. We hope that our new curriculum will prove useful to future instructors interested in teaching workshops with similar learning objectives.

Mechanical Harvesting Effectively Controls Young Typha spp. Invasion and Unmanned Aerial Vehicle Data Enhances Post-treatment Monitoring.

The ecological impacts of invasive plants increase dramatically with time since invasion. Targeting young populations for treatment is therefore an economically and ecologically effective management approach, especially when linked to post-treatment monitoring to evaluate the efficacy of management. However, collecting detailed field-based post-treatment data is prohibitively expensive, typically resulting in inadequate documentation of the ecological effects of invasive plant management. Alternative approaches, such as remote detection with unmanned aerial vehicles (UAV), provide an opportunity to advance the science and practice of restoration ecology. In this study, we sought to determine the plant community response to different mechanical removal treatments to a dominant invasive wetland macrophyte (Typha spp.) along an age-gradient within a Great Lakes coastal wetland. We assessed the post-treatment responses with both intensive field vegetation and UAV data. Prior to treatment, the oldest Typha stands had the lowest plant diversity, lowest native sedge (Carex spp.) cover, and the greatest Typha cover. Following treatment, plots that were mechanically harvested below the surface of the water differed from unharvested control and above-water harvested plots for several plant community measures, including lower Typha dominance, lower native plant cover, and greater floating and submerged aquatic species cover. Repeated-measures analysis revealed that above-water cutting increased plant diversity and aquatic species cover across all ages, and maintained native Carex spp. cover in the youngest portions of Typha stands. UAV data revealed significant post-treatment differences in normalized difference vegetation index (NDVI) scores, blue band reflectance, and vegetation height, and these remotely collected measures corresponded to field observations. Our findings suggest that both mechanically harvesting the above-water biomass of young Typha stands and harvesting older stands below-water will promote overall native community resilience, and increase the abundance of the floating and submerged aquatic plant guilds, which are the most vulnerable to invasions by large macrophytes. UAV's provided fast and spatially expansive data compared to field monitoring, and effectively measured plant community structural responses to different treatments. Study results suggest pairing UAV flights with targeted field data collection to maximize the quality of post-restoration vegetation monitoring.

Detecting Precontact Anthropogenic Microtopographic Features in a Forested Landscape with Lidar: A Case Study from the Upper Great Lakes Region, AD 1000-1600.

Forested settings present challenges for understanding the full extent of past human landscape modifications. Field-based archaeological reconnaissance in forests is low-efficiency and most remote sensing techniques are of limited utility, and together, this means many past sites and features in forests are unknown. Archaeologists have increasingly used light detection and ranging (lidar), a remote sensing tool that uses pulses of light to measure reflecting surfaces at high spatial resolution, to address these limitations. Archaeology studies using lidar have made significant progress identifying permanent structures built by large-scale complex agriculturalist societies. Largely unaccounted for, however, are numerous small and more practical modifications of landscapes by smaller-scale societies. Here we show these may also be detectable with lidar by identifying remnants of food storage pits (cache pits) created by mobile hunter-gatherers in the upper Great Lakes during Late Precontact (ca. AD 1000-1600) that now only exist as subtle microtopographic features. Years of intensive field survey identified 69 cache pit groups between two inland lakes in northern Michigan, almost all of which were located within ~500 m of a lakeshore. Applying a novel series of image processing techniques and statistical analyses to a high spatial resolution DTM we created from commercial-grade lidar, our detection routine identified 139 high potential cache pit clusters. These included most of the previously known clusters as well as several unknown clusters located >1500 m from either lakeshore, much further from lakeshores than all previously identified cultural sites. Food storage is understood to have emerged regionally as a risk-buffering strategy after AD 1000 but our results indicate the current record of hunter-gatherer cache pit food storage is markedly incomplete and this practice and its associated impact on the landscape may be greater than anticipated. Our study also demonstrates the potential of harnessing commercial-grade lidar for other fine-grained archaeology applications.