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Abstract Forbs (“wildflowers”) are important contributors to grassland biodiversity but are vulnerable to environmental changes. In a factorial experiment at 94 sites on 6 continents, we test the global generality of several broad predictions: (1) Forb cover and richness decline under nutrient enrichment, particularly nitrogen enrichment. (2) Forb cover and richness increase under herbivory by large mammals. (3) Forb richness and cover are less affected by nutrient enrichment and herbivory in more arid climates, because water limitation reduces the impacts of competition with grasses. (4) Forb families will respond differently to nutrient enrichment and mammalian herbivory due to differences in nutrient requirements. We find strong evidence for the first, partial support for the second, no support for the third, and support for the fourth prediction. Our results underscore that anthropogenic nitrogen addition is a major threat to grassland forbs, but grazing under high herbivore intensity can offset these nutrient effects. 

Abstract Plants respond to their environment with both short‐term, within‐generation trait plasticity, and long‐term, between‐generation evolutionary changes. However, the relative magnitude of plant responses to short‐ and long‐term changes in the environment remains poorly understood. Shifts in phenological traits can serve as harbingers for responses to environmental change, and both a plant's current and source (i.e., genotype origin) environment can affect plant phenology via plasticity and local adaptation, respectively. To assess the role of current and source environments in explaining variation in flowering phenology ofBromus tectorum, an invasive annual grass, we conducted a replicated common garden experiment using 92 genotypes collected across western North America. Replicates of each genotype were planted in two densities (low = 100 seeds/1 m², high = 100 seeds/0.04 m²) under two different temperature treatments (low = white gravel; high = black gravel; 2.1°C average difference) in a factorial design, replicated across four common garden locations in Idaho and Wyoming, USA. We tested for the effect of current environment (i.e., density treatment, temperature treatment, and common garden location), source environment (i.e., genotype source climate), and their interaction on each plant's flowering phenology. Flowering timing was strongly influenced by a plant's current environment, with plants that experienced warmer current climates and higher densities flowering earlier than those that experienced cooler current climates and lower densities. Genotypes from hot and dry source climates flowered consistently earlier than those from cool and wet source climates, even after accounting for genotype relatedness, suggesting that this genetically based climate cline is a product of natural selection. We found minimal evidence of interactions between current and source environments or genotype‐by‐environment interactions. Phenology was more sensitive to variation in the current climate than to variation in source climate. These results indicate that cheatgrass phenology reflects high levels of plasticity as well as rapid local adaptation. Both processes likely contribute to its current success as a biological invader and its capacity to respond to future environmental change. 

Abstract Selecting among competing statistical models is a core challenge in science. However, the many possible approaches and techniques for model selection, and the conflicting recommendations for their use, can be confusing. We contend that much confusion surrounding statistical model selection results from failing to first clearly specify the purpose of the analysis. We argue that there are three distinct goals for statistical modeling in ecology: data exploration, inference, and prediction. Once the modeling goal is clearly articulated, an appropriate model selection procedure is easier to identify. We review model selection approaches and highlight their strengths and weaknesses relative to each of the three modeling goals. We then present examples of modeling for exploration, inference, and prediction using a time series of butterfly population counts. These show how a model selection approach flows naturally from the modeling goal, leading to different models selected for different purposes, even with exactly the same data set. This review illustrates best practices for ecologists and should serve as a reminder that statistical recipes cannot substitute for critical thinking or for the use of independent data to test hypotheses and validate predictions. 

Abstract Although natural resource managers are concerned about climate change, many are unable to adequately incorporate climate change science into their adaptation strategies or management plans, and are not always aware of or do not always employ the most current scientific knowledge. One of the most prominent natural resource management agencies in the United States is the Bureau of Land Management (BLM), which is tasked with managing over 248 million acres (>1 million km²) of public lands for multiple, often conflicting, uses. Climate change will affect the sustainability of many of these land uses and could further increase conflicts between them. As such, the purpose of our study was to determine the extent to which climate change will affect public land uses, and whether the BLM is managing for such predicted effects. To do so, we first conducted a systematic review of peer‐reviewed literature that discussed potential impacts of climate change on the multiple land uses the BLM manages in the Intermountain West, USA, and then expanded these results with a synthesis of projected vegetation changes. Finally, we conducted a content analysis of BLM Resource Management Plans in order to determine how climate change is explicitly addressed by BLM managers, and whether such plans reflect changes predicted by the scientific literature. We found that active resource use generally threatens intrinsic values such as conservation and ecosystem services on BLM land, and climate change is expected to exacerbate these threats in numerous ways. Additionally, our synthesis of vegetation modeling suggests substantial changes in vegetation due to climate change. However, BLM plans rarely referred to climate change explicitly and did not reflect the results of the literature review or vegetation model synthesis. Our results suggest there is a disconnect between management of BLM lands and the best available science on climate change. We recommend that the BLM actively integrates such research into on‐the‐ground management plans and activities, and that researchers studying the effects of climate change make a more robust effort to understand the practices and policies of public land management in order to effectively communicate the management significance of their findings.