Where east meets west: Phylogeography of the high Arctic North American brant goose

Genetic variation in Arctic species is often influenced by vicariance during the Pleistocene, as ice sheets fragmented the landscape and displaced populations to low- and high-latitude refugia. The formation of secondary contact or suture zones during periods of ice sheet retraction has important consequences on genetic diversity by facilitating genetic connectivity between formerly isolated populations. Brant geese (Branta bernicla) are a maritime migratory waterfowl (Anseriformes) species that almost exclusively uses coastal habitats. Within North America, brant geese are characterized by two phenotypically distinct subspecies that utilize disjunct breeding and wintering areas in the northern Pacific and Atlantic. In the Western High Arctic of Canada, brant geese consist of individuals with an intermediate phenotype that are rarely observed nesting outside this region. We examined the genetic structure of brant geese populations from each subspecies and areas consisting of intermediate phenotypes using mitochondrial DNA (mtDNA) control region sequence data and microsatellite loci. We found a strong east-west partition in both marker types consistent with refugial populations. Within subspecies, structure was also observed at mtDNA while microsatellite data suggested the presence of only two distinct genetic clusters. The Western High Arctic (WHA) appears to be a secondary contact zone for both Atlantic and Pacific lineages as mtDNA and nuclear genotypes were assigned to both subspecies, and admixed individuals were observed in this region. The mtDNA sequence data outside WHA suggests no or very restricted intermixing between Atlantic and Pacific wintering populations which is consistent with published banding and telemetry data. Our study indicates that, although brant geese in the WHA are not a genetically distinct lineage, this region may act as a reservoir of genetic diversity and may be an area of high conservation value given the potential of low reproductive output in this species.

Marginal value analysis reveals shifting importance of migration habitat for waterfowl under a changing climate

Migratory waterfowl are an important resource for consumptive and non-consumptive users alike and provide tremendous economic value in North America. These birds rely on a complex matrix of public and private land for forage and roosting during migration and wintering periods, and substantial conservation effort focuses on increasing the amount and quality of target habitat. Yet, the value of habitat is a function not only of a site's resources but also of its geographic position and weather. To quantify this value, we used a continental-scale energetics-based model of daily dabbling duck movement to assess the marginal value of lands across the contiguous United States during the non-breeding period (September to May). We examined effects of eliminating each habitat node (32 × 32 km) in both a particularly cold and a particularly warm winter, asking which nodes had the largest effect on survival. The marginal value of habitat nodes for migrating dabbling ducks was a function of forage and roosting habitat but, more importantly, of geography (especially latitude and region). Irrespective of weather, nodes in the Southeast, central East Coast, and California made the largest positive contributions to survival. Conversely, nodes in the Midwest, Northeast, Florida, and the Pacific Northwest had consistent negative effects. Effects (positive and negative) of more northerly nodes occurred in late fall or early spring when climate was often severe and was most variable. Importance and effects of many nodes varied considerably between a cold and a warm winter. Much of the Midwest and central Great Plains benefited duck survival in a warm winter, and projected future warming may improve the value of lands in these regions, including many National Wildlife Refuges, for migrating dabbling ducks. Our results highlight the geographic variability in habitat value, as well as shifts that may occur in these values due to climate change.

Living on the edge: Predicting songbird response to management and environmental changes across an ecotone

Effective wildlife management requires robust information regarding population status, habitat requirements, and likely responses to changing resource conditions. Single-species management may inadequately conserve communities and result in undesired effects to non-target species. Thus, management can benefit from understanding habitat relationships for multiple species. Pinyon pine and juniper (Pinus spp. and Juniperus spp.) are expanding into sagebrush-dominated (Artemisia spp.) ecosystems within North America and mechanical removal of these trees is frequently conducted to restore sagebrush ecosystems and recover Greater Sage-grouse (Centrocercus urophasianus). However, pinyon-juniper removal effects on non-target species are poorly understood, and changing pinyon-juniper woodland dynamics, climate, and anthropogenic development may obscure conservation priorities. To better predict responses to changing resource conditions, evaluate non-target effects of pinyon-juniper removal, prioritize species for conservation, and inform species recovery within pinyon-juniper and sagebrush ecosystems, we modeled population trends and density-habitat relationships for four sagebrush-associated, four pinyon-juniper-associated, and three generalist songbird species with respect to these ecosystems. We fit hierarchical population models to point count data collected throughout the western United States from 2008 to 2020. We found regional population changes for 10 of 11 species investigated; 6 of which increased in the highest elevation region of our study. Our models indicate pinyon-juniper removal will benefit Brewer's Sparrow (Spizella breweri), Green-tailed Towhee (Pipilo chlorurus), and Sage Thrasher (Oreoscoptes montanus) densities. Conversely, we predict largest negative effects of pinyon-juniper removal for species occupying early successional pinyon-juniper woodlands: Bewick's Wren (Thryomanes bewickii), Black-throated Gray Warblers (Setophaga nigrescens), Gray Flycatcher (Empidonax wrightii), and Juniper Titmouse (Baeolophus ridgwayi). Our results highlight the importance of considering effects to non-target species before implementing large-scale habitat manipulations. Our modeling framework can help prioritize species and regions for conservation action, infer effects of management interventions and a changing environment on wildlife, and help land managers balance habitat requirements across ecosystems.

Four conservation challenges and a synthesis

Conservation and management of biological systems involves decision-making over time, with a generic goal of sustaining systems and their capacity to function in the future. We address four persistent and difficult conservation challenges: (1) prediction of future consequences of management, (2) uncertainty about the system's structure, (3) inability to observe ecological systems fully, and (4) nonstationary system dynamics. We describe these challenges in terms of dynamic systems subject to different sources of uncertainty, and we present a basic Markovian framework that can encompass approaches to all four challenges. Finding optimal conservation strategies for each challenge requires issue-specific structural features, including adaptations of state transition models, uncertainty metrics, valuation of accumulated returns, and solution methods. Strategy valuation exhibits not only some remarkable similarities among approaches but also some important operational differences. Technical linkages among the models highlight synergies in solution approaches, as well as possibilities for combining them in particular conservation problems. As methodology and computing software advance, such an integrated conservation framework offers the potential to improve conservation outcomes with strategies to allocate management resources efficiently and avoid negative consequences.

Environmentally sustainable implementations of two-dimensional nanomaterials

Rapid advancement in nanotechnology has led to the development of a myriad of useful nanomaterials that have novel characteristics resulting from their small size and engineered properties. In particular, two-dimensional (2D) materials have become a major focus in material science and chemistry research worldwide with substantial efforts centered on their synthesis, property characterization, and technological, and environmental applications. Environmental applications of these nanomaterials include but are not limited to adsorbents for wastewater and drinking water treatment, membranes for desalination, and coating materials for filtration. However, it is also important to address the environmental interactions and implications of these nanomaterials in order to develop strategies that minimize their environmental and public health risks. Towards this end, this review covers the most recent literature on the environmental implementations of emerging 2D nanomaterials, thereby providing insights into the future of this fast-evolving field including strategies for ensuring sustainable development of 2D nanomaterials.

Optimizing Earthquake Nowcasting With Machine Learning: The Role of Strain Hardening in the Earthquake Cycle

Nowcasting is a term originating from economics, finance, and meteorology. It refers to the process of determining the uncertain state of the economy, markets or the weather at the current time by indirect means. In this paper, we describe a simple two-parameter data analysis that reveals hidden order in otherwise seemingly chaotic earthquake seismicity. One of these parameters relates to a mechanism of seismic quiescence arising from the physics of strain-hardening of the crust prior to major events. We observe an earthquake cycle associated with major earthquakes in California, similar to what has long been postulated. An estimate of the earthquake hazard revealed by this state variable time series can be optimized by the use of machine learning in the form of the Receiver Operating Characteristic skill score. The ROC skill is used here as a loss function in a supervised learning mode. Our analysis is conducted in the region of 5° × 5° in latitude-longitude centered on Los Angeles, a region which we used in previous papers to build similar time series using more involved methods (Rundle & Donnellan, 2020, https://doi.org/10.1029/2020EA001097; Rundle, Donnellan et al., 2021, https://doi.org/10.1029/2021EA001757; Rundle, Stein et al., 2021, https://doi.org/10.1088/1361-6633/abf893). Here we show that not only does the state variable time series have forecast skill, the associated spatial probability densities have skill as well. In addition, use of the standard ROC and Precision (PPV) metrics allow probabilities of current earthquake hazard to be defined in a simple, straightforward, and rigorous way.

Microbiome assembly in thawing permafrost and its feedbacks to climate

The physical and chemical changes that accompany permafrost thaw directly influence the microbial communities that mediate the decomposition of formerly frozen organic matter, leading to uncertainty in permafrost-climate feedbacks. Although changes to microbial metabolism and community structure are documented following thaw, the generality of post-thaw assembly patterns across permafrost soils of the world remains uncertain, limiting our ability to predict biogeochemistry and microbial community responses to climate change. Based on our review of the Arctic microbiome, permafrost microbiology, and community ecology, we propose that Assembly Theory provides a framework to better understand thaw-mediated microbiome changes and the implications for community function and climate feedbacks. This framework posits that the prevalence of deterministic or stochastic processes indicates whether the community is well-suited to thrive in changing environmental conditions. We predict that on a short timescale and following high-disturbance thaw (e.g., thermokarst), stochasticity dominates post-thaw microbiome assembly, suggesting that functional predictions will be aided by detailed information about the microbiome. At a longer timescale and lower-intensity disturbance (e.g., active layer deepening), deterministic processes likely dominate, making environmental parameters sufficient for predicting function. We propose that the contribution of stochastic and deterministic processes to post-thaw microbiome assembly depends on the characteristics of the thaw disturbance, as well as characteristics of the microbial community, such as the ecological and phylogenetic breadth of functional guilds, their functional redundancy, and biotic interactions. These propagate across space and time, potentially providing a means for predicting the microbial forcing of greenhouse gas feedbacks to global climate change.

The past, present, and future of coral reef growth in the Florida Keys

Coral-reef degradation is driving global-scale reductions in reef-building capacity and the ecological, geological, and socioeconomic functions it supports. The persistence of those essential functions will depend on whether coral-reef management is able to rebalance the competing processes of reef accretion and erosion. Here, we reconstructed census-based carbonate budgets of 46 reefs throughout the Florida Keys from 1996 to 2019. We evaluated the environmental and ecological drivers of changing budget states and compared historical trends in reef-accretion potential to millennial-scale baselines of accretion from reef cores and future projections with coral restoration. We found that historically, most reefs had positive carbonate budgets, and many had reef-accretion potential comparable to the ~3 mm year^-1 average accretion rate during the peak of regional reef building ~7000 years ago; however, declines in reef-building Acropora palmata and Orbicella spp. corals following a series of thermal stress events and coral disease outbreaks resulted in a shift from positive to negative budgets for most reefs in the region. By 2019, only ~15% of reefs had positive net carbonate production. Most of those reefs were in inshore, Lower Keys patch-reef habitats with low water clarity, supporting the hypothesis that environments with naturally low irradiance may provide a refugia from thermal stress. We caution that our estimated carbonate budgets are likely overly optimistic; comparison of reef-accretion potential to measured accretion from reef cores suggests that, by not accounting for the role of nonbiological physical and chemical erosion, census-based carbonate budgets may underestimate total erosion by ~1 mm year^-1 (-1.15 kg CaCO₃ m^-2 year^-1 ). Although the present state of Florida's reefs is dire, we demonstrate that the restoration of reef-building corals has the potential to help mitigate declines in reef accretion in some locations, which could allow some key ecosystem functions to be maintained until the threat of global climate change is addressed.

Partial observability and management of ecological systems

The actual state of ecological systems is rarely known with certainty, but management actions must often be taken regardless of imperfect measurement (partial observability). Because of the difficulties in accounting for partial observability, it is usually treated in an ad hoc fashion, or simply ignored altogether. Yet incorporating partial observability into decision processes lends a realism that has the potential to improve ecological outcomes significantly. We review frameworks for dealing with partial observability, focusing specifically on dynamic ecological systems with Markovian transitions, i.e., transitions among system states that are influenced by the current system state and management action over time. Fully observable states are represented in an observable Markov decision process (MDP), whereas obscure or hidden states are represented in a partially observable process (POMDP). POMDPs can be seen as a natural extension of observable MDPs. Management under partial observability generalizes the situation for complete observability, by recognizing uncertainty about the system's state and incorporating sequential observations associated with, but not the same as, the states themselves. Decisions that otherwise would depend on the actual state must be based instead on state probability distributions (“belief states”). Partial observability requires adaptation of the entire decision process, including the use of belief states and Bayesian updates, valuation that includes expectations over observations, and optimal strategy that identifies actions for belief states over a continuous belief space. We compare MDPs and POMDPs and highlight POMDP applications to some common ecological problems. We clarify the structure and operations, approaches for finding solutions, and analytic challenges of POMDPs for practicing ecologists. Both observable and partially observable MDPs can use an inductive approach to identify optimal strategies and values, with a considerable increase in mathematical complexity with POMDPs. Better understanding of POMDPs can help decision makers manage imperfectly measured ecological systems more effectively.

Mapped Predictions of Manganese and Arsenic in an Alluvial Aquifer Using Boosted Regression Trees

Manganese (Mn) concentrations and the probability of arsenic (As) exceeding the drinking-water standard of 10 μg/L were predicted in the Mississippi River Valley alluvial aquifer (MRVA) using boosted regression trees (BRT). BRT, a type of ensemble-tree machine-learning model, were created using predictor variables that affect Mn and As distribution in groundwater. These variables included iron (Fe) concentrations and specific conductance predicted from previously developed BRT models, groundwater flux and age estimates from MODFLOW, and hydrologic characteristics. The models also included results from the first airborne geophysical survey conducted in the United States to target an entire aquifer system. Predictions of high Mn and As occurred where Fe was high. Predicted high Mn concentrations were correlated with fraction of young groundwater (less than 65 years) computed from MODFLOW results. High probabilities of As exceedance were predicted where groundwater was relatively old and airborne electromagnetic resistivity was high, typically proximal to streams. Two-variable partial-dependence plots and sensitivity analysis were used to provide insight into the factors controlling Mn and As distribution in groundwater. The maps of predicted Mn concentrations and As exceedance probabilities can be used to identify areas where these constituents may be high, and that could be targeted for further study. This paper shows that incorporation of a selected set of process-informed data, such as MODFLOW results and airborne geophysics, into a machine-learning model improves model interpretability. Incorporation of process-rich information into machine-learning models will likely be useful for addressing a wide range of problems of interest to groundwater hydrologists.

Assessing dynamic vegetation model parameter uncertainty across Alaskan arctic tundra plant communities

As the Arctic region moves into uncharted territory under a warming climate, it is important to refine the terrestrial biosphere models (TBMs) that help us understand and predict change. One fundamental uncertainty in TBMs relates to model parameters, configuration variables internal to the model whose value can be estimated from data. We incorporate a version of the Terrestrial Ecosystem Model (TEM) developed for arctic ecosystems into the Predictive Ecosystem Analyzer (PEcAn) framework. PEcAn treats model parameters as probability distributions, estimates parameters based on a synthesis of available field data, and then quantifies both model sensitivity and uncertainty to a given parameter or suite of parameters. We examined how variation in 21 parameters in the equation for gross primary production influenced model sensitivity and uncertainty in terms of two carbon fluxes (net primary productivity and heterotrophic respiration) and two carbon (C) pools (vegetation C and soil C). We set up different parameterizations of TEM across a range of tundra types (tussock tundra, heath tundra, wet sedge tundra, and shrub tundra) in northern Alaska, along a latitudinal transect extending from the coastal plain near Utqiaġvik to the southern foothills of the Brooks Range, to the Seward Peninsula. TEM was most sensitive to parameters related to the temperature regulation of photosynthesis. Model uncertainty was mostly due to parameters related to leaf area, temperature regulation of photosynthesis, and the stomatal responses to ambient light conditions. Our analysis also showed that sensitivity and uncertainty to a given parameter varied spatially. At some sites, model sensitivity and uncertainty tended to be connected to a wider range of parameters, underlining the importance of assessing tundra community processes across environmental gradients or geographic locations. Generally, across sites, the flux of net primary productivity (NPP) and pool of vegetation C had about equal uncertainty, while heterotrophic respiration had higher uncertainty than the pool of soil C. Our study illustrates the complexity inherent in evaluating parameter uncertainty across highly heterogeneous arctic tundra plant communities. It also provides a framework for iteratively testing how newly collected field data related to key parameters may result in more effective forecasting of Arctic change.

Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non-forest ecosystems

Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low-stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R ² of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave-one-out cross-validation of 3.9%. Biomass per-unit-of-height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1-10 ha^-1. Photogrammetric approaches could provide much-needed information required to calibrate and validate the vegetation models and satellite-derived biomass products that are essential to understand vulnerable and understudied non-forested ecosystems around the globe.

Genomic and environmental influences on resilience in a cold-water fish near the edge of its range

Small, isolated populations present a challenge for conservation. The dueling effects of selection and drift in a limited pool of genetic diversity make the responses of small populations to environmental perturbations erratic and difficult to predict. This is particularly true at the edge of a species range, where populations often persist at the limits of their environmental tolerances. Populations of cisco, Coregonus artedi, in inland lakes have experienced numerous extirpations along the southern edge of their range in recent decades, which are thought to result from environmental degradation and loss of cold, well-oxygenated habitat as lakes warm. Yet, cisco extirpations do not show a clear latitudinal pattern, suggesting that local environmental factors and potentially local adaptation may influence resilience. Here, we used genomic tools to investigate the nature of this pattern of resilience. We used restriction site-associated DNA capture (Rapture) sequencing to survey genomic diversity and differentiation in southern inland lake cisco populations and compared the frequency of deleterious mutations that potentially influence fitness across lakes. We also examined haplotype diversity in a region of the major histocompatibility complex involved in stress and immune system response. We correlated these metrics to spatial and environmental factors including latitude, lake size, and measures of oxythermal habitat and found significant relationships between genetic metrics and broad and local factors. High levels of genetic differentiation among populations were punctuated by a phylogeographic break and residual patterns of isolation-by-distance. Although the prevalence of deleterious mutations and inbreeding coefficients was significantly correlated with latitude, neutral and non-neutral genetic diversity were most strongly correlated with lake surface area. Notably, differences among lakes in the availability of estimated oxythermal habitat left no clear population genomic signature. Our results shed light on the complex dynamics influencing these isolated populations and provide valuable information for their conservation.

Improving the Usability of Galileo and Voyager Images of Jupiter's Moon Europa

NASA's Voyager 1, Voyager 2, and Galileo spacecraft acquired hundreds of images of Jupiter's moon Europa. These images provide the only moderate- to high-resolution views of the moon's surface and are therefore a critical resource for scientific analysis and future mission planning. Unfortunately, uncertain knowledge of the spacecraft's position and pointing during image acquisition resulted in significant errors in the location of the images on the surface. The result is that adjacent images are poorly aligned, with some images displaced by more than 100 km from their correct location. These errors severely degrade the usability of the Voyager and Galileo imaging data sets. To improve the usability of these data sets, we used the U.S. Geological Survey Integrated Software for Imagers and Spectrometers to build a nearly global image tie-point network with more than 50,000 tie points and 135,000 image measurements on 481 Galileo and 221 Voyager images. A global least-squares bundle adjustment of our final Europa tie-point network calculated latitude, longitude, and radius values for each point by minimizing residuals globally, and resulted in root mean square (RMS) uncertainties of 246.6 m, 307.0 m, and 70.5 m in latitude, longitude, and radius, respectively. The total RMS uncertainty was 0.32 pixels. This work enables direct use of nearly the entire Galileo and Voyager image data sets for Europa. We are providing the community with updated NASA Navigation and Ancillary Information Facility Spacecraft, Planet, Instrument, C-matrix (pointing), and Events kernels, mosaics of Galileo images acquired during each observation sequence, and individual processed and projected level 2 images.

Demographic risk assessment for a harvested species threatened by climate change: polar bears in the Chukchi Sea

Climate change threatens global biodiversity. Many species vulnerable to climate change are important to humans for nutritional, cultural, and economic reasons. Polar bears Ursus maritimus are threatened by sea-ice loss and represent a subsistence resource for Indigenous people. We applied a novel population modeling-management framework that is based on species life history and accounts for habitat loss to evaluate subsistence harvest for the Chukchi Sea (CS) polar bear subpopulation. Harvest strategies followed a state-dependent approach under which new data were used to update the harvest on a predetermined management interval. We found that a harvest strategy with a starting total harvest rate of 2.7% (˜85 bears/yr at current abundance), a 2:1 male-to-female ratio, and a 10-yr management interval would likely maintain subpopulation abundance above maximum net productivity level for the next 35 yr (approximately three polar bear generations), our primary criterion for sustainability. Plausible bounds on starting total harvest rate were 1.7-3.9%, where the range reflects uncertainty due to sampling variation, environmental variation, model selection, and differing levels of risk tolerance. The risk of undesired demographic outcomes (e.g., overharvest) was positively related to harvest rate, management interval, and projected declines in environmental carrying capacity; and negatively related to precision in population data. Results reflect several lines of evidence that the CS subpopulation has been productive in recent years, although it is uncertain how long this will last as sea-ice loss continues. Our methods provide a template for balancing trade-offs among protection, use, research investment, and other factors. Demographic risk assessment and state-dependent management will become increasingly important for harvested species, like polar bears, that exhibit spatiotemporal variation in their response to climate change.

Identifying mismatches between conservation area networks and vulnerable populations using spatial randomization

Grassland birds are among the most globally threatened bird groups due to substantial degradation of native grassland habitats. However, the current network of grassland conservation areas may not be adequate for halting population declines and biodiversity loss. Here, we evaluate a network of grassland conservation areas within Wisconsin, U.S.A., that includes both large Focal Landscapes and smaller targeted conservation areas (e.g., Grassland Bird Conservation Areas, GBCAs) established within them. To date, this conservation network has lacked baseline information to assess whether the current placement of these conservation areas aligns with population hot spots of grassland-dependent taxa. To do so, we fitted data from thousands of avian point-count surveys collected by citizen scientists as part of Wisconsin's Breeding Bird Atlas II with multinomial N-mixture models to estimate habitat-abundance relationships, develop spatially explicit predictions of abundance, and establish ecological baselines within priority conservation areas for a suite of obligate grassland songbirds. Next, we developed spatial randomization tests to evaluate the placement of this conservation network relative to randomly placed conservation networks. Overall, less than 20% of species statewide populations were found within the current grassland conservation network. Spatial tests demonstrated a high representation of this bird assemblage within the entire conservation network, but with a bias toward birds associated with moderately tallgrasses relative to those associated with shortgrasses or tallgrasses. We also found that GBCAs had higher representation at Focal Landscape rather than statewide scales. Here, we demonstrated how combining citizen science data with hierarchical modeling is a powerful tool for estimating ecological baselines and conducting large-scale evaluations of an existing conservation network for multiple grassland birds. Our flexible spatial randomization approach offers the potential to be applied to other protected area networks and serves as a complementary tool for conservation planning efforts globally.

Survival of western Gulf Coast Mottled Ducks (Anas fulvigula) in the path of a Category 4 hurricane

Tropical cyclones are the most powerful storms on earth, causing catastrophic damage to human lives and infrastructure. Hurricanes also cause wildlife mortality when they make landfall, but the severity of these effects is difficult to quantify because data collection is either logistically impossible or deprioritized in the wake of human tragedy. On August 27, 2020, Hurricane Laura made landfall in southwestern Louisiana with maximum sustained winds of 241 kph (150 mph), making it one of the most powerful storms to strike the mainland United States. Hurricane Laura passed directly over the core breeding range of the western Gulf Coast population of Mottled Duck (Anas fulvigula), during a time when many adult birds were undergoing a simultaneous wing feather molt and were flightless. We used GPS-GSM telemetry data to evaluate survival rates of adult female Mottled Ducks in late summer 2020 (bracketing August 27 by one month on either side) relative to the same period in 2018 and 2019. Mortality was lower in 2018 (12 out of 29; 41%) and 2019 (8 out of 28; 29%) than in 2020 (12 out of 18; 67%), and 7 out of 12 mortalities documented in 2020 occurred when Hurricane Laura made landfall. Survival analyses in program MARK confirmed lower survival probability in 2020, but there was overlap in 85% confidence intervals in all years. This single storm resulted in the death of ~40% of all marked birds in our sample, suggesting that hurricanes have the potential to influence population demographics. In addition, Hurricane Laura resulted in widespread habitat loss and degradation that has reduced available nesting habitat in 2021, and possibly for years to come. The acute and chronic effects of hurricanes may exacerbate Mottled Duck population declines, which may worsen in the face of increasingly frequent and more severe tropical storms.

Spring phenology drives range shifts in a migratory Arctic ungulate with key implications for the future

Annual variation in phenology can have profound effects on the behavior of animals. As climate change advances spring phenology in ecosystems around the globe, it is becoming increasingly important to understand how animals respond to variation in the timing of seasonal events and how their responses may shift in the future. We investigated the influence of spring phenology on the behavior of migratory, barren-ground caribou (Rangifer tarandus), a species that has evolved to cope with short Arctic summers. Specifically, we examined the effect of spring snow melt and vegetation growth on the current and potential future space-use patterns of the Porcupine Caribou Herd (PCH), which exhibits large, inter-annual shifts in their calving and post-calving distributions across the U.S.-Canadian border. We quantified PCH selection for snow melt and vegetation phenology using machine learning models, determined how selection resulted in annual shifts in space-use, and then projected future distributions based on climate-driven phenology models. Caribou exhibited strong, scale-dependent selection for both snow melt and vegetation growth. During the calving season, caribou selected areas at finer scales where the snow had melted and vegetation was greening, but within broader landscapes that were still brown or snow covered. During the post-calving season, they selected vegetation with intermediate biomass expected to have high forage quality. Annual variation in spring phenology predicted major shifts in PCH space-use. In years with early spring phenology, PCH predominately used habitat in Alaska, while in years with late phenology, they spent more time in Yukon. Future climate conditions were projected to advance spring phenology, shifting PCH calving and post-calving distributions further west into Alaska. Our results demonstrate that caribou selection for habitat in specific phenological stages drive dramatic shifts in annual space-use patterns, and will likely affect future distributions, underscoring the importance of maintaining sufficient suitable habitat to allow for behavioral plasticity.

Migration stopover ecology of Cinnamon Teal in western North America

Identifying migration routes and fall stopover sites of Cinnamon Teal (Spatula cyanoptera septentrionalium) can provide a spatial guide to management and conservation efforts, and address vulnerabilities in wetland networks that support migratory waterbirds. Using high spatiotemporal resolution GPS-GSM transmitters, we analyzed 61 fall migration tracks across western North America during our three-year study (2017-2019). We marked Cinnamon Teal primarily during spring/summer in important breeding and molting regions across seven states (California, Oregon, Washington, Idaho, Utah, Colorado, and Nevada). We assessed fall migration routes and timing, detected 186 fall stopover sites, and identified specific North American ecoregions where sites were located. We classified underlying land cover for each stopover site and measured habitat selection for 12 land cover types within each ecoregion. Cinnamon Teal selected a variety of flooded habitats including natural, riparian, tidal, and managed wetlands; wet agriculture (including irrigation ditches, flooded fields, and stock ponds); wastewater sites; and golf and urban ponds. Wet agriculture was the most used habitat type (29.8% of stopover locations), and over 72% of stopover locations were on private land. Relatively scarce habitats such as wastewater ponds, tidal marsh, and golf and urban ponds were highly selected in specific ecoregions. In contrast, dry non-habitat across all ecoregions, and dry agriculture in the Cold Deserts and Mediterranean California ecoregions, was consistently avoided. Resources used by Cinnamon Teal often reflected wetland availability across the west and emphasize their adaptability to dynamic resource conditions in arid landscapes. Our results provide much needed information on spatial and temporal resource use by Cinnamon Teal during migration and indicate important wetland habitats for migrating waterfowl in the western United States.

Direct and Delayed Mortality of Ceriodaphnia dubia and Rainbow Trout Following Time-Varying Acute Exposures to Zinc

The potential for delayed mortality following short-term episodic pollution events was evaluated by exposing cladocerans (Ceriodaphnia dubia) and rainbow trout (Oncorhynchus mykiss) to zinc (Zn) in various 1- to 48-h and 1- to 96-h exposures, respectively, followed by transferring the exposed organisms to clean water for up to 47 h for C. dubia and up to 95 h for trout for additional observation. For C. dubia, 1-h exposures of up to 3790 µg Zn/L never resulted in mortality during the actual Zn exposures, but by 48 h, a 1-h exposure to 114 µg/L, a concentration similar to the present US national water quality acute criterion for the test water conditions, ultimately killed 70% of C. dubia. With C. dubia, the speed of action of Zn toxicity was faster for intermediate concentrations than for the highest concentrations tested. For rainbow trout, pronounced delayed mortalities by 96 h only occurred following ≥8-h exposures. For both species, ultimate mortalities from Zn exposures ≤8 h mostly presented as delayed mortalities, whereas for exposures ≥24 h, almost all ultimate mortalities presented during the actual exposure periods. With Zn, risks of delayed mortality following exposures to all concentrations tested were much greater for the more sensitive, small-bodied invertebrate (C. dubia) than for the less sensitive, larger-bodied fish (rainbow trout). These results, along with previous studies, show that delayed mortality is an important consideration in evaluating risks to aquatic organisms from brief, episodic exposures to some substances. Environ Toxicol Chem 2021;40:2484-2498. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Is the grass always greener? Land surface phenology reveals differences in peak and season-long vegetation productivity responses to climate and management

Vegetation phenology-the seasonal timing and duration of vegetative phases-is controlled by spatiotemporally variable contributions of climatic and environmental factors plus additional potential influence from human management. We used land surface phenology derived from the Advanced Very High Resolution Radiometer and climate data to examine variability in vegetation productivity and phenological dates from 1989 to 2014 in the U.S. Northwestern Plains, a region with notable spatial heterogeneity in climate, vegetation, and land use. We first analyzed interannual trends in six phenological measures as a baseline. We then demonstrated how including annual-resolution predictors can provide more nuanced insights into measures of phenology between plant communities and across the ecoregion. Across the study area, higher annual precipitation increased both peak and season-long productivity. In contrast, higher mean annual temperatures tended to increase peak productivity but for the majority of the study area decreased season-long productivity. Annual precipitation and temperature had strong explanatory power for productivity-related phenology measures but predicted date-based measures poorly. We found that relationships between climate and phenology varied across the region and among plant communities and that factors such as recovery from disturbance and anthropogenic management also contributed in certain regions. In sum, phenological measures did not respond ubiquitously nor covary in their responses. Nonclimatic dynamics can decouple phenology from climate; therefore, analyses including only interannual trends should not assume climate alone drives patterns. For example, models of areas exhibiting greening or browning should account for climate, anthropogenic influence, and natural disturbances. Investigating multiple aspects of phenology to describe growing-season dynamics provides a richer understanding of spatiotemporal patterns that can be used for predicting ecosystem responses to future climates and land-use change. Such understanding allows for clearer interpretation of results for conservation, wildlife, and land management.

Buried Aseismic Slip and Off-Fault Deformation on the Southernmost San Andreas Fault Triggered by the 2010 El Mayor Cucapah Earthquake Revealed by UAVSAR

We use UAVSAR interferograms to characterize fault slip, triggered by the Mw 7.2 El Mayor-Cucapah earthquake on the 1 San Andreas Fault in the Coachella Valley providing comprehensive maps of short-term geodetic surface deformation that complement in situ measurements. Creepmeters and geological mapping of fault offsets on Durmid Hill recorded 4 and 8 mm of average triggered slip respectively on the fault, in contrast to radar views that reveal significant off-fault dextral deformation averaging 20 mm. Unlike slip in previous triggered slip events on the southernmost San Andreas fault, dextral shear in 2010 is not confined to transpressional hills in the Coachella valley. Edge detection and gradient estimation applied to the 50-m-sampled interferogram data identify the location (to 20 m) and local strike (to <4°) of secondary surface ruptures. Transverse curve fitting applied to these local detections provides local estimates of the radar-projected dextral slip and a parameter indicating the transverse width of the slip, which we equate with the depth of subsurface shear. These estimates are partially validated by fault-transverse interferogram profiles generated using the GeoGateway UAVSAR tool, and appear consistent for radar-projected slip greater than about 5 mm. An unexpected finding is that creep and triggered slip on the San Andreas fault terminate in the shallow subsurface below a surface shear zone that resists the simple expression of aseismic fault slip. We introduce the notion of a surface locking depth above which fault slip is manifest as distributed shear, and evaluate its depth as 6-27 m.

Black Scoter habitat use along the southeastern coast of the United States

While the Atlantic Coast of the United States and Canada is a major wintering area for sea ducks, knowledge about their wintering habitat use is relatively limited. Black Scoters have a broad wintering distribution and are the only open water species of sea duck that is abundant along the southeastern coast of the United States. Our study identified variables that affected Black Scoter (Melanitta americana) distribution and abundance in the Atlantic Ocean along the southeastern coast of the United States. We used aerial survey data from 2009 to 2012 provided by the United States Fish and Wildlife Service to identify variables that influenced Black Scoter distribution. We used indicator variable selection to evaluate relationships between Black Scoter habitat use and a variety of broad- and fine-scale oceanographic and weather variables. Average time between waves, ocean floor slope, and the interaction of bathymetry and distance to shore had the strongest association with southeastern Black Scoter distribution.