ALBERT

Description: Warming temperatures and increasing CO 2 are likely to have large effects on the amount of carbon stored in soil, but predictions of these effects are poorly constrained. We elevated temperature (canopy: +2.8 °C; soil growing season: +1.8 °C; soil fallow: +2.3 °C) for three years within the 9th-11th years of an elevated CO 2 (+200 ppm) experiment on a maize-soybean agroecosystem, measured respiration by roots and soil microbes, then used a process-based ecosystem model (DayCent) to simulate the decadal effects of warming and CO 2 enrichment on soil C. Both heating and elevated CO 2 increased respiration from soil microbes by ~20%, but heating reduced respiration from roots and rhizosphere by ~25%. The effects were additive, with no heat x CO 2 interactions. Particulate organic matter and total soil C declined over time in all treatments and were lower in elevated CO 2 plots than in ambient plots, but did not differ between heat treatments. We speculate that these declines indicate a priming effect, with increased C inputs under elevated CO 2 fueling a loss of old soil carbon. Model simulations of heated plots agreed with our observations and predicted loss of ~15% of soil organic C after 100 years of heating, but simulations of elevated CO 2 failed to predict the observed C losses and instead predicted a ~4% gain in soil organic C under any heating conditions. Despite model uncertainty, our empirical results suggest that combined, elevated CO 2 and temperature will lead to long term declines in the amount of carbon stored in agricultural soils. This article is protected by copyright. All rights reserved.

Description: We synthesize insights from current understanding of drought impacts at stand-to-biogeographic scales, including management options, and we identify challenges to be addressed with new research. Large stand-level shifts underway in western forests already are showing the importance of interactions involving drought, insects, and fire. Diebacks, changes in composition and structure, and shifting range limits are widely observed. In the eastern US, the effects of increasing drought are becoming better understood at the level of individual trees, but this knowledge cannot yet be confidently translated to predictions of changing structure and diversity of forest stands. While eastern forests have not experienced the types of changes seen in western forests in recent decades, they too are vulnerable to drought and could experience significant changes with increased severity, frequency, or duration in drought. Throughout the continental United States, the combination of projected large climate-induced shifts in suitable habitat from modeling studies and limited potential for the rapid migration of tree populations suggests that changing tree and forest biogeography could substantially lag habitat shifts already underway. Forest management practices can partially ameliorate drought impacts through reductions in stand density, selection of drought-tolerant species and genotypes, artificial regeneration, and the development of multistructured stands. However, silvicultural treatments also could exacerbate drought impacts unless implemented with careful attention to site and stand characteristics. Gaps in our understanding should motivate new research on the effects of interactions involving climate and other species at the stand scale and how interactions and multiple responses are represented in models. This assessment indicates that, without a stronger empirical basis for drought impacts at the stand scale, more complex models may provide limited guidance.

Description: Over the last 120 years, the dry season in the southeastern United States has lengthened by as much as 156 days (130%), with less total precipitation. Less rainfall over a longer dry season, with no apparent change in seasonal thunderstorm patterns, likely increases both the potential for lightning‐ignited wildfires and fire severity. Global climate change could be affecting fire regimes by altering the synchrony of climatic seasonal parameters. Abstract Trends in average annual or seasonal precipitation are insufficient for detecting changes in the climatic fire season, especially in regions where the fire season is defined by wet–dry seasonal cycles and lightning activity. Using an extensive dataset (1897–2017) in the Coastal Plain of the southeastern United States, we examined changes in annual dry season length, total precipitation, and (since 1945) the seasonal distribution of thunder‐days as a correlate of lightning activity. We found that across the entire region, the dry season has lengthened by as much as 156 days (130% over 120 years), both starting earlier and ending later with less total precipitation. Less rainfall over a longer dry season, with no change in seasonal thunderstorm patterns, likely increases both the potential for lightning‐ignited wildfires and fire severity. Global climate change could be having a hitherto undetected influence on fire regimes by altering the synchrony of climatic seasonal parameters.

Description: Quantifying drought at different temporal scales by varying the time length (i.e., grain) and timing (i.e., window) describes different hydrologic attributes which may uniquely influence animal food supplies, vegetation greenness/structure, and other factors affecting populations. For North American Great Plains breeding birds, drought had negative, but species and temporal scale‐dependent effects, supporting a larger pattern of multiple and nuanced wildlife responses to unfavorable weather. Considering drought at different temporal scales may better reveal mechanisms behind drought impacts on birds and other organisms, and improve understanding of the effects of anthropogenic climate change on species and the landscapes they inhabit. Abstract Global climate change is increasing the frequency and intensity of weather extremes, including severe droughts in many regions. Drought can impact organisms by inhibiting reproduction, reducing survival and abundance, and forcing range shifts. For birds, considering temporal scale by averaging drought‐related variables over different time lengths (i.e., temporal grains) captures different hydrologic attributes which may uniquely influence food supplies, vegetation greenness/structure, and other factors affecting populations. However, studies examining drought impacts on birds often assess a single temporal grain without considering that different species have different life histories that likely determine the temporal grain of their drought response. Furthermore, while drought is known to influence bird abundance and drive between‐year range shifts, less understood is whether it causes within‐range changes in species distributions. Our objectives were to (a) determine which temporal grain of drought (if any) is most related to bird presence/absence and whether this response is species specific; and (b) assess whether drought alters bird distributions by quantifying probability of local colonization and extinction as a function of drought intensity. We used North American Breeding Bird Survey data collected over 16 years, generalized linear mixed models, and dynamic occupancy models to meet these objectives. Different bird species responded to drought at different temporal grains, with most showing the strongest signal at annual or near‐annual grains. For all drought‐responsive species, increased drought intensity at any temporal grain always correlated with decreased occupancy. Additionally, colonization/extinction analyses indicated that one species, the dickcissel (Spiza americana), is more likely to colonize novel areas within the southern/core portion of its range during drought. Considering drought at different temporal grains, along with hydrologic attributes captured by each grain, may better reveal mechanisms behind drought impacts on birds and other organisms, and therefore improve understanding of how global climate change impacts species and the landscapes they inhabit.

Description: Abstract Optimizing the remediation of light nonaqueous phase liquids (LNAPLs) to achieve an acceptable endpoint status for a site is not trivial. Recently, Sookhak Lari, Johnston, et al. (2018, https://doi.org/10.1016/j.jhazmat.2017.11.006), Sookhak Lari, Rayner, and Davis (2018, https://doi.org/10.1016/j.jenvman.2018.07.041) conducted three‐dimensional multiphase, multicomponent simulations to address LNAPL remediation endpoints for a single recovery well. However, optimized LNAPL remediation for multiple wells is not addressed in the literature. In the first part of this paper, we establish a matrix of 10 simulation scenarios to show the sensitivity of the remedial endpoint (i.e., what is feasibly achieved) to several parameters including viscosity and partitioning attributes of the LNAPL, heterogeneity of the formation, and the location and number of the recovery wells. While this addresses the variability of LNAPL removal from the subsurface and is valuable in its own right, it does not address the optimal removal of LNAPL. We address this in the second part of the paper by linking a genetic algorithm to TMVOC‐MP to allow, for example, the assessment of the optimal number and location of LNAPL recovery wells in a field‐scale problem. Using supercomputing facilities and within 49 genetic algorithm generations, each including 150 members, highly optimized answers to different objective functions were obtained. For the first time, such a multiphase, multicomponent optimization tool promises the possibility of optimizing LNAPL remediation at field scale to achieve practicable endpoint conditions, within computational affordability.

Description: Arabica coffee (Coffea arabica) is a key crop in many tropical countries and globally provides an export value of over US$13 billion per year. Wild Arabica coffee is of fundamental importance for the global coffee sector and of direct importance within Ethiopia, as a source of harvestable income and planting stock. In this paper we show that under climate change alone, population numbers could reduce by 50% or more (with a few models showing over 80%) by 2088. When applying the International Union for Conservation of Nature’s Red list of Threatened Species (IUCN Red List) criteria, even with a very conservative generation length of 21 years, wild Arabica coffee is assessed as Threatened with extinction (placed in the Endangered category) under a broad range of climate change projections, if no interventions are made. Importantly, if we do not include climate change in our assessment, Arabica coffee is assessed as Least Concern (not threatened) when applying the IUCN Red List criteria. Abstract Arabica coffee (Coffea arabica) is a key crop in many tropical countries and globally provides an export value of over US$13 billion per year. Wild Arabica coffee is of fundamental importance for the global coffee sector and of direct importance within Ethiopia, as a source of harvestable income and planting stock. Published studies show that climate change is projected to have a substantial negative influence on the current suitable growing areas for indigenous Arabica in Ethiopia and South Sudan. Here we use all available future projections for the species based on multiple general circulation models (GCMs), emission scenarios, and migration scenarios, to predict changes in Extent of Occurrence (EOO), Area of Occupancy (AOO), and population numbers for wild Arabica coffee. Under climate change our results show that population numbers could reduce by 50% or more (with a few models showing over 80%) by 2088. EOO and AOO are projected to decline by around 30% in many cases. Furthermore, present‐day models compared to the near future (2038), show a reduction for EOO of over 40% (with a few cases over 50%), although EOO should be treated with caution due to its sensitivity to outlying occurrences. When applying these metrics to extinction risk, we show that the determination of generation length is critical. When applying the International Union for Conservation of Nature's Red list of Threatened Species (IUCN Red List) criteria, even with a very conservative generation length of 21 years, wild Arabica coffee is assessed as Threatened with extinction (placed in the Endangered category) under a broad range of climate change projections, if no interventions are made. Importantly, if we do not include climate change in our assessment, Arabica coffee is assessed as Least Concern (not threatened) when applying the IUCN Red List criteria.

Description: Two fundamental issues in ecology are understanding what influences the distribution and abundance of organisms through space and time. While it is well established that broad scale patterns of abiotic and biotic conditions affect organisms’ distributions and population fluctuations, discrete events may be important drivers of both space use, survival, and persistence. These discrete extreme climatic events can constrain populations and space use at fine scales beyond that which is typically measured in ecological studies. Recently a growing body of literature has identified thermal stress as a potential mechanism in determining space use and survival. We sought to determine how ambient temperature at fine temporal scales affected survival and space use for a ground-nesting quail species ( Colinus virginianus ; northern bobwhite). We modeled space use across an ambient temperature gradient (ranging from -20 °C to 38 °C) through a Maxent algorithm. We also used Andersen-Gill proportional hazard models to assess the influence of ambient temperature-related variables on survival through time. Estimated available useable space ranged from 18.6% to 57.1% of the landscape depending on ambient temperature. The lowest and highest ambient temperature categories (〈-15 °C and 〉35 °C, respectively) were associated with the least amount of estimated useable space (18.6% and 24.6% respectively). Range overlap analysis indicated dissimilarity in areas where Colinus virginianus were restricted during times of thermal extremes (range overlap = 0.38). This suggests that habitat under a given condition is not necessarily habitat under alternative conditions. Further, we found survival was most influenced by weekly minimum ambient temperatures. Our results demonstrate that ecological constraints can occur along a thermal gradient and that understanding the effects of these discrete events and how they change over time may be more important to conservation of organisms than are average and broad scale conditions as typically measured in ecological studies. This article is protected by copyright. All rights reserved.

Description: Accurately estimating basin-wide snow water equivalent (SWE) is the most important unsolved problem in mountain hydrology. Models that rely on remotely sensed inputs are especially needed in ranges with few surface measurements. The NASA Airborne Snow Observatory (ASO) provides estimates of SWE at 50-meter spatial resolution in several basins across the Western US during the melt season. Primarily, water managers use this information to forecast snowmelt runoff into reservoirs; another impactful use of ASO measurements lies in validating and improving satellite-based snow estimates or models that can scale to whole mountain ranges, even those without ground-based measurements. We compare ASO measurements from 2013 to 2015 to four methods that estimate spatially distributed SWE: two versions of a SWE reconstruction method, spatial interpolation from snow pillows and courses, and NOAA's Snow Data Assimilation System (SNODAS). SWE reconstruction downscales energy forcings to compute potential melt, then multiplies those values by satellite-derived estimates of fractional snow-covered area to calculate snowmelt. The snowpack is then built in reverse from the date the snow is observed to disappear. The two SWE reconstruction models tested include one that employs an energy balance calculation of snowmelt, and one that combines net radiation and degree-day approaches to estimate melt. Our full energy balance model, without ground observations, performed slightly better than spatial interpolation from snow pillows, having no systematic bias and 26% mean absolute error when compared to SWE from ASO. Both reconstruction models and interpolation were more accurate than SNODAS. This article is protected by copyright. All rights reserved.

Description: Quantifying drought at different temporal scales by varying the time length (i.e., grain) and timing (i.e., window) describes different hydrologic attributes which may uniquely influence animal food supplies, vegetation greenness/structure, and other factors affecting populations. For North American Great Plains breeding birds, drought had negative, but species and temporal scale‐dependent effects, supporting a larger pattern of multiple and nuanced wildlife responses to unfavorable weather. Considering drought at different temporal scales may better reveal mechanisms behind drought impacts on birds and other organisms, and improve understanding of the effects of anthropogenic climate change on species and the landscapes they inhabit. Abstract Global climate change is increasing the frequency and intensity of weather extremes, including severe droughts in many regions. Drought can impact organisms by inhibiting reproduction, reducing survival and abundance, and forcing range shifts. For birds, considering temporal scale by averaging drought‐related variables over different time lengths (i.e., temporal grains) captures different hydrologic attributes which may uniquely influence food supplies, vegetation greenness/structure, and other factors affecting populations. However, studies examining drought impacts on birds often assess a single temporal grain without considering that different species have different life histories that likely determine the temporal grain of their drought response. Furthermore, while drought is known to influence bird abundance and drive between‐year range shifts, less understood is whether it causes within‐range changes in species distributions. Our objectives were to (a) determine which temporal grain of drought (if any) is most related to bird presence/absence and whether this response is species specific; and (b) assess whether drought alters bird distributions by quantifying probability of local colonization and extinction as a function of drought intensity. We used North American Breeding Bird Survey data collected over 16 years, generalized linear mixed models, and dynamic occupancy models to meet these objectives. Different bird species responded to drought at different temporal grains, with most showing the strongest signal at annual or near‐annual grains. For all drought‐responsive species, increased drought intensity at any temporal grain always correlated with decreased occupancy. Additionally, colonization/extinction analyses indicated that one species, the dickcissel (Spiza americana), is more likely to colonize novel areas within the southern/core portion of its range during drought. Considering drought at different temporal grains, along with hydrologic attributes captured by each grain, may better reveal mechanisms behind drought impacts on birds and other organisms, and therefore improve understanding of how global climate change impacts species and the landscapes they inhabit.

Description: Abstract Understanding streamflow generation and its dependence on catchment characteristics requires large spatial datasets and is often limited by convoluted effects of multiple variables. Here we address this knowledge gap using data‐informed physics‐based hydrologic modelling in two catchments with similar vegetation and climate but different lithology (Shale Hills, SH, Shale, 0.08 km2 and Garner Run, GR, Sandstone, 1.34 km2), which influences catchment topography and soil properties. The sandstone catchment, Garner Run, is characterized by lower drainage density, extensive valley fill, and boulder soils. We tested the hypothesis that the influence of topographic characteristics is more significant than that of soil properties and catchment size. Transferring calibration coefficients from the previously‐calibrated SH model to GR cannot reproduce monthly discharge until after incorporating measured boulder distribution at GR. Model calibration underscored the importance of soil properties (porosity, van Genuchten parameters, and boulder characteristics) in reproducing daily discharge. Virtual experiments were used to swap topography, soil properties, and catchment size one at a time to disentangle their influence. They showed that clayey SH soils led to high nonlinearity and threshold behavior. With the same soil and topography, changing from SH to GR size consistently increased dynamic water storage (Sd) from ~ 0.12 m to ~ 0.17 m. All analyses accentuated the predominant control of soil properties, therefore rejecting the hypothesis. The results illustrate the use of physics‐based modelling for illuminating mechanisms and underscore the importance and challenges for subsurface characterization as we move toward hydrological Prediction in Ungauged Basins (PUB).