No abstract

Big sagebrush (Artemisia tridentata) is one of the most widespread and abundant plant species in the intermountain regions of western North America. This species occupies an extremely wide ecological niche ranging from the semi-arid basins to the subalpine. Within this large niche, three widespread subspecies are recognized. Montane ecoregions are occupied by subspecies vaseyana, while subspecies wyomingensis and tridentata occupy basin ecoregions. In cases of wide-ranging species with multiple subspecies, it can be more practical from the scientific and management perspective to assess the climate profiles at the subspecies level. We focus bioclimatic model efforts on subspecies wyomingensis, which is the most widespread and abundant of the subspecies and critical habitat to wildlife including sage-grouse and pygmy rabbits. Using absence points from species with allopatric ranges to Wyoming big sagebrush (i.e., targeted groups absences) and randomly sampled points from specific ecoregions, we modeled the climatic envelope for subspecies wyomingensis using Random Forests multiple-regression tree for contemporary and future climates (decade 2050). Overall model error was low, at 4.5%, with the vast majority accounted for by errors in commission (>99.9%). Comparison of the contemporary and decade 2050 models shows a predicted 39% loss of suitable climate. Much of this loss will occur in the Great Basin where impacts from increasing fire frequency and encroaching weeds have been eroding the A. tridentata landscape dominance and ecological functions. Our goal of the A. tridentata subsp. wyomingensis bioclimatic model is to provide a management tool to promote successful restoration by predicting the geographic areas where climate is suitable for this subspecies. This model can also be used as a restoration-planning tool to assess vulnerability of climatic extirpation over the next few decades.

No abstract

Rising temperatures have begun to shift flowering time, but it is unclear whether phenotypic plasticity can accommodate projected temperature change for this century. Evaluating clines in phenological traits and the extent and variation in plasticity can provide key information on assessing risk of maladaptation and developing strategies to mitigate climate change. In this study, flower phenology was examined in 52 populations of big sagebrush (Artemisia tridentata) growing in three common gardens. Flowering date (anthesis) varied 91 days from late July to late November among gardens. Mixed-effects modeling explained 79% of variation in flowering date, of which 46% could be assigned to plasticity and genetic variation in plasticity and 33% to genetics (conditional R = 0.79, marginal R = 0.33). Two environmental variables that explained the genetic variation were photoperiod and the onset of spring, the Julian date of accumulating degree-days >5 °C reaching 100. The genetic variation was mapped for contemporary and future climates (decades 2060 and 2090), showing flower date change varies considerably across the landscape. Plasticity was estimated to accommodate, on average, a ±13-day change in flowering date. However, the examination of genetic variation in plasticity suggests that the magnitude of plasticity could be affected by variation in the sensitivity to photoperiod and temperature. In a warmer common garden, lower-latitude populations have greater plasticity (+16 days) compared to higher-latitude populations (+10 days). Mapped climatypes of flowering date for contemporary and future climates illustrate the wide breadth of plasticity and large geographic overlap. Our research highlights the importance of integrating information on genetic variation, phenotypic plasticity and climatic niche modeling to evaluate plant responses and elucidate vulnerabilities to climate change.

We conclude that nocturnal pollination of Oenothera harringtonii via hawkmoths is robust to the negative impacts of land-use change.

Conserving migratory birds is made especially difficult because of movement among spatially disparate locations across the annual cycle. In light of challenges presented by the scale and ecology of migratory birds, successful conservation requires integrating objectives, management, and monitoring across scales, from local management units to ecoregional and flyway administrative boundaries. We present an integrated approach using a spatially explicit energetic-based mechanistic bird migration model useful to conservation decision-making across disparate scales and locations. This model moves a Mallard-like bird (Anas platyrhynchos), through spring and fall migration as a function of caloric gains and losses across a continental-scale energy landscape. We predicted with this model that fall migration, where birds moved from breeding to wintering habitat, took a mean of 27.5 d of flight with a mean seasonal survivorship of 90.5% (95% Cl = 89.2%, 91.9%), whereas spring migration took a mean of 23.5 d of flight with mean seasonal survivorship of 93.6% (95% CI = 92.5%, 94.7%). Sensitivity analyses suggested that survival during migration was sensitive to flight speed, flight cost, the amount of energy the animal could carry, and the spatial pattern of energy availability, but generally insensitive to total energy availability per se. Nevertheless, continental patterns in the bird-use days occurred principally in relation to wetland cover and agricultural habitat in the fall. Bird-use days were highest in both spring and fall in the Mississippi Alluvial Valley and along the coast and near-shore environments of South Carolina. Spatial sensitivity analyses suggested that locations nearer to migratory endpoints were less important to survivorship; for instance, removing energy from a 1036 km2 stopover site at a time from the Atlantic Flyway suggested coastal areas between New Jersey and North Carolina, including the Chesapeake Bay and the North Carolina piedmont, are essential locations for efficient migration and increasing survivorship during spring migration but not locations in Ontario and Massachusetts. This sort of spatially explicit information may allow decision-makers to prioritize their conservation actions toward locations most influential to migratory success. Thus, this mechanistic model of avian migration provides a decision-analytic medium integrating the potential consequences of local actions to flyway-scale phenomena.

No abstract

The challenges of restoration in dryland ecosystems are growing due to a rise in anthropogenic disturbance and increasing aridity. Plant functional traits are often used to predict plant performance and can offer a window into potential outcomes of restoration efforts across environmental gradients. We analyzed a database including 15 yr of seeding outcomes across 150 sites on the Colorado Plateau, a cold desert ecoregion in the western United States, and analyzed the independent and interactive effects of functional traits (seed mass, height, and specific leaf area) and local biologically relevant climate variables on seeding success. We predicted that the best models would include an interaction between plant traits and climate, indicating a need to match the right trait value to the right climate conditions to maximize seeding success. Indeed, we found that both plant height and seed size significantly interacted with temperature seasonality, with larger seeds and taller plants performing better in more seasonal environments. We also determined that these trait-environment patterns are not influenced by whether a species is native or nonnative. Our results inform the selection of seed mixes for restoring areas with specific climatic conditions, while also demonstrating the strong influence of temperature seasonality on seeding success in the Colorado Plateau region.