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Abstract Many recent studies have explored remote sensing approaches to facilitate non‐destructive sampling of aboveground biomass (AGB). Lidar platforms (e.g., iPhone and iPad PRO models) have recently made remote sensing technologies widely available and present an alternative to traditional approaches for estimating AGB. Lidar approaches can be completed within a fraction of the time required by many analog methods. However, it is unknown if handheld sensors are capable of accurately predicting AGB or how different modeling techniques affect prediction accuracy. Here, we collected AGB from 0.25‐m²plots (N = 45) from three sites along an elevational gradient within rangelands surrounding Flagstaff, Arizona, USA. Each plot was scanned with a mobile laser scanner (MLS) and iPad before plants were clipped, dried, and weighed. We compared the capability of iPad and MLS sensors to estimate AGB via minimization of model normalized root mean square error (NRMSE). This process was performed on predictor subsets describing structural, spectral, and field‐based characteristics across a suite of modeling approaches including simple linear, stepwise, lasso, and random forest regression. We found that models developed from MLS and iPad data were equally capable of predicting AGB (NRMSE 26.6% and 29.3%, respectively) regardless of the variable subsets considered. We also found that stepwise regression regularly resulted in the lowest NRMSE. Structural variables were consistently selected during each modeling approach, while spectral variables were rarely included. Field‐based variables were important in linear regression models but were not included after variable selection within random forest models. These findings support the notion that remote sensing techniques offer a valid alternative to analog field‐based data collection methods. Together, our results demonstrate that data collected using a more widely available platform will perform similarly to a more costly option and outline a workflow for modeling AGB using remote sensing systems alone. 

Identifying the mechanisms underlying the persistence of rare species has long been a motivating question for ecologists. Classical theory implies that community dynamics should be driven by common species, and that natural selection should not allow small populations of rare species to persist. Yet, a majority of the species found on Earth are rare. Consequently, several mechanisms have been proposed to explain their persistence, including negative density dependence, demographic compensation, vital rate buffering, asynchronous responses of subpopulations to environmental heterogeneity, and fine‐scale source‐sink dynamics. Persistence of seeds in a seed bank, which is often ignored in models of population dynamics, can also buffer small populations against collapse. We used integral projection models (IPMs) to examine the population dynamics ofOenothera coloradensis, a rare, monocarpic perennial forb, and determine whether any of five proposed demographic mechanisms for rare species persistence contribute to the long‐term viability of two populations. We also evaluate how including a discrete seed bank stage changes these population models. Including a seed bank stage in population models had a significantly increased modeledO. coloradensispopulation growth rate. Using this structured population model, we found that negative density‐dependence was the only supported mechanism for the persistence of this rare species. We propose that high micro‐site abundances within a spatially heterogeneous environment enables this species to persist, allowing it to sidestep the demographic and genetic challenges of small population size that rare species typically face. The five mechanisms of persistence explored in our study have been demonstrated as effective strategies in other species, and the fact that only one of them had strong support here supports the idea that globally rare species can employ distinct persistence strategies. This reinforces the need for customized management and conservation strategies that mirror the diversity of mechanisms that allow rare species persistence. 

Tree species appear to prefer distinct climatic conditions, but the true nature of these preferences is obscured by species interactions and dispersal, which limit species’ ranges. We quantified realized and potential thermal niches of 188 North American tree species to conduct a continental-scale test of the architecture of niches. We found strong and consistent evidence that species occurring at thermal extremes occupy less than three-quarters of their potential niches, and species’ potential niches overlap at a mean annual temperature of ~12°C. These results clarify the breadth of thermal tolerances of temperate tree species and support the centrifugal organization of thermal niches. Accounting for the nonrealized components of ecological niches will advance theory and prediction in global change ecology. 

The Global Vegetation Project (http://gveg.wyobiodiversity.org) is a new initiative to host an online database of open-access, georeferenced vegetation photos. The mission of the Global Vegetation Project is ‘to inspire and empower people of all ages to learn about the diversity of vegetation on our planet and to provide educators with a resource for teaching ecology online’. The beta release includes two R-Shiny web applications that allow users to 1) submit photos of plant communities through a user-friendly online portal and 2) explore submissions made by others through an interactive global map. The spatial coordinates of each photo are used to extract information about the location including long-term and recent climate data to create Walter and Leith climate diagrams for each photo. User submitted photos can be filtered by biome, temperature, precipitation, and elevation on the map. The Global Vegetation Project will evolve to match the needs of vegetation scientists and ecology educators. We intend to enhance the educational value of the mapping application by incorporating additional search features, global data layers, and the publication of curricula geared towards primary, secondary, and post-secondary education. We encourage the global community of vegetation scientists to use this resource in their classrooms and to contribute photos of vegetation to grow this valuable resource for the world. 

Generalizing the effect of traits on performance across species may be achievable if traits explain variation in population fitness. However, testing relationships between traits and vital rates to infer effects on fitness can be misleading. Demographic trade-offs can generate variation in vital rates that yield equal population growth rates, thereby obscuring the net effect of traits on fitness. To address this problem, we describe a diversity of approaches to quantify intrinsic growth rates of plant populations, including experiments beyond range boundaries, density-dependent population models built from long-term demographic data, theoretical models, and methods that leverage widely available monitoring data. Linking plant traits directly to intrinsic growth rates is a fundamental step toward rigorous predictions of population dynamics and community assembly.