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At least 739 records · Page 41Linked to original sources

USGS2024

Assessing the sustainability of Pacific walrus harvest in a changing environment

Harvest sustainability is a primary goal of wildlife management and conservation, and in a changing world it is increasingly important to consider environmental drivers of population dynamics alongside harvest in cohesive management plans. This is particularly pertinent for harvested species that are acutely experiencing effects of climate change. The Pacific walrus ( Odobenus rosmarus divergens ), a critical traditional subsistence resource for indigenous communities, is simultaneously subject to rapid habitat loss associated with diminishing sea ice and an increasing anthropogenic footprint in the Arctic. We developed a theta-logistic population modeling-management framework to evaluate various harvest scenarios combined with four potential climate/disturbance scenarios (ranging from optimistic–pessimistic) which simulates Pacific walrus population dynamics to the end of the 21 st century. We considered two types of harvest strategies: (1) adaptive harvest scenarios wherein harvest is calculated as a percentage of the population and annual harvests are updated at set intervals as the population is reassessed, and (2) non-adaptive harvest scenarios wherein annual harvest remains constant. All climate/disturbance scenarios indicated declines of varying severity in Pacific walrus abundance to the end of the 21 st century, even in the absence of harvest. However, we found that an adaptive annual harvest of 1.23% of the independent-aged female subset of the population (e.g., 1,280 independent-aged females harvested in 2020, representing contemporary harvest levels) met our criterion for sustainability (>70% probability of maintaining population abundance above maximum net productivity level) under all climate/disturbance scenarios, accepting a medium risk tolerance level of 25%. This suggests that the present rate of Pacific walrus harvest is sustainable and will continue to be—provided the harvest adapts to match changes in population dynamics. Our simulations suggest that a sustainable non-adaptive harvest is also possible, but only at low levels if the population declines as expected. Applying a constant annual harvest of 1,280 independent-aged females (equivalent to contemporary harvest levels of 1.23) exceeded our criterion for sustainability and resulted in a >5% chance of quasi-extinction by the end of the 21 st century under three of the four climate/disturbance scenarios we evaluated. Our results highlight the importance of adaptive co-management strategies, and we suggest such modeling frameworks are useful for managing for harvest sustainability in a changing climate.

BioRxiv↗

USGS2017

Topographic, edaphic, and vegetative controls on plant-available water

Soil moisture varies within landscapes in response to vegetative, physiographic, and climatic drivers, which makes quantifying soil moisture over time and space difficult. Nevertheless, understanding soil moisture dynamics for different ecosystems is critical, as the amount of water in a soil determines a myriad ecosystem services and processes such as net primary productivity, runoff, microbial decomposition, and soil fertility. We investigated the patterns and variability in in situ soil moisture measurements converted to plant-available water across time and space under different vegetative cover types and topographic positions at the Marcell Experimental Forest (Minnesota, USA). From 0 – 228.6 cm soil depth, plant-available water was significantly higher under the hardwoods (12%), followed by the aspen (8%) and red pine (5%) cover types. Across the same soil depth, toeslopes were wetter (mean plant-available water = 10%) than ridges and backslopes (mean plant-available water was 8%), although these differences were not statistically significant ( p < 0.05). Using a mixed model of fixed and random effects, we found that cover type, soil texture, and time were related to plant-available water and that topography was not significantly related to plant-available water within this low-relief landscape. Additionally, during the three-year monitoring period, red pine and quaking aspen sites experienced plant-available water levels that may be considered limiting to plant growth and function. Given that increasing temperatures and more erratic precipitation patterns associated with climate change may result in decreased soil moisture in this region, these species may be sensitive and vulnerable to future shifts in climate.

Ecohydrology↗

USGS2020

Storm impacts on phytoplankton community dynamics in lakes

In many regions across the globe, extreme weather events such as storms have increased in frequency, intensity, and duration due to climate change. Ecological theory predicts that such extreme events should have large impacts on ecosystem structure and function. High winds and precipitation associated with storms can affect lakes via short‐term runoff events from watersheds and physical mixing of the water column. In addition, lakes connected to rivers and streams will also experience flushing due to high flow rates. Although we have a well‐developed understanding of how wind and precipitation events can alter lake physical processes and some aspects of biogeochemical cycling, our mechanistic understanding of the emergent responses of phytoplankton communities is poor. Here we provide a comprehensive synthesis that identifies how storms interact with lake and watershed attributes and their antecedent conditions to generate changes in lake physical and chemical environments. Such changes can restructure phytoplankton communities and their dynamics, as well as result in altered ecological function (e.g., carbon, nutrient and energy cycling) in the short‐ and long‐term. We summarize the current understanding of storm‐induced phytoplankton dynamics, identify knowledge gaps with a systematic review of the literature, and suggest future research directions across a gradient of lake types and environmental conditions.

Global Change Biology↗

USGS2012

Numerical models of salt marsh evolution: ecological, geomorphic, and climatic factors

Salt marshes are delicate landforms at the boundary between the sea and land. These ecosystems support a diverse biota that modifies the erosive characteristics of the substrate and mediates sediment transport processes. Here we present a broad overview of recent numerical models that quantify the formation and evolution of salt marshes under different physical and ecological drivers. In particular, we focus on the coupling between geomorphological and ecological processes and on how these feedbacks are included in predictive models of landform evolution. We describe in detail models that simulate fluxes of water, organic matter, and sediments in salt marshes. The interplay between biological and morphological processes often produces a distinct scarp between salt marshes and tidal flats. Numerical models can capture the dynamics of this boundary and the progradation or regression of the marsh in time. Tidal channels are also key features of the marsh landscape, flooding and draining the marsh platform and providing a source of sediments and nutrients to the marsh ecosystem. In recent years, several numerical models have been developed to describe the morphogenesis and long-term dynamics of salt marsh channels. Finally, salt marshes are highly sensitive to the effects of long-term climatic change. We therefore discuss in detail how numerical models have been used to determine salt marsh survival under different scenarios of sea level rise.

Reviews of Geophysics↗

USGS2018

The GFDL global atmosphere and land model AM4.0/LM4.0: 1. Simulation characteristics with prescribed SSTs

In this two‐part paper, a description is provided of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). This version, with roughly 100 km horizontal resolution and 33 levels in the vertical, contains an aerosol model that generates aerosol fields from emissions and a “light” chemistry mechanism designed to support the aerosol model but with prescribed ozone. In Part 1, the quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode—with prescribed sea surface temperatures (SSTs) and sea‐ice distribution—is described and compared with previous GFDL models and with the CMIP5 archive of AMIP simulations. The model's Cess sensitivity (response in the top‐of‐atmosphere radiative flux to uniform warming of SSTs) and effective radiative forcing are also presented. In Part 2, the model formulation is described more fully and key sensitivities to aspects of the model formulation are discussed, along with the approach to model tuning.

Journal of Advances in Modeling Earth Systems↗

USGS2018

The GFDL global atmosphere and land model AM4.0/LM4.0: 2. Model description, sensitivity studies, and tuning strategies

In Part 2 of this two‐part paper, documentation is provided of key aspects of a version of the AM4.0/LM4.0 atmosphere/land model that will serve as a base for a new set of climate and Earth system models (CM4 and ESM4) under development at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL). The quality of the simulation in AMIP (Atmospheric Model Intercomparison Project) mode has been provided in Part 1. Part 2 provides documentation of key components and some sensitivities to choices of model formulation and values of parameters, highlighting the convection parameterization and orographic gravity wave drag. The approach taken to tune the model's clouds to observations is a particular focal point. Care is taken to describe the extent to which aerosol effective forcing and Cess sensitivity have been tuned through the model development process, both of which are relevant to the ability of the model to simulate the evolution of temperatures over the last century when coupled to an ocean model.

Journal of Advances in Modeling Earth Systems↗

USGS2020

Co-producing knowledge: The Integrated Ecosystem Model for resource management in Arctic Alaska

Assessments of climate-change effects on ecosystem processes and services in high-latitude regions are hindered by a lack of decision-support tools capable of forecasting possible future landscapes. We describe a collaborative effort to develop and apply the Integrated Ecosystem Model (IEM) for Alaska and northwestern Canada to explore how climate change influences interactions among disturbance regimes, permafrost integrity, hydrology, and vegetation, and how these dynamics in turn influence resource management decisions. This process emphasizes co-production of knowledge among decision makers, scientists, major funders, partners, and stakeholders. We highlight research findings based on IEM applications in Arctic Alaska, as well as successes and challenges of the co-production process. The overall framework and lessons from our work with the IEM are relevant to other collaborative efforts outside the Arctic that aim to develop a decision-support tool or an undertaking of equivalent scope.

Alaska↗

USGS2023

Fluvial delivery and wave resuspension of sediment in a sheltered, urbanized Pacific Northwest estuary

The sequence and timing of sediment delivery and redistribution in coastal systems is important for shoreline stability, ecosystem services, and remediation planning. In temperate estuaries, understanding the role of fluvial sediment delivery and dispersal relative to wind and wave remobilization processes is particularly important to address the fate of contaminants, many of which adsorb to fine particles, and to assess changes in coastal systems under projected changes in climate. Here we present an integrated analysis of observations at multiple timescales to evaluate sediment dynamics and the sedimentary coupling between fluvial and oceanographic processes within Bellingham Bay, Washington, USA, an urban estuary. Time-series data of currents, waves, and turbidity at four moorings along with geochemical data from grab samples and cores of seabed sediment from across the bay are contrasted with the dynamics of the Nooksack River, its fluvial sediment source. Even during large (5-yr return interval) river-flood events, water-column suspended-sediment concentration (SSC) near the bed on the outer delta topset was not correlated with Nooksack River runoff and was instead closely correlated with local wind-wave height. In contrast, near-surface SSC was strongly correlated with fluvial discharge, suggesting intense water-column suspended-sediment stratification during flood events. Grain-size and geochemical ( 7 "> 7 Be and excess 210 "> 210 Pb) results from seabed-sediment samples and historical bathymetric measurements of the subaqueous Nooksack River delta reveal spatial gradients of fluvial and wave influence and sediment-accumulation rates. Analysis of historical bathymetric surveys of the Nooksack River delta combined with the geochemical data reveal that about 75% of the fluvial sediment load can be accounted for in the Bellingham Bay receiving system. Studies of this type in urbanized coastal settings can help address ecological and geological questions regarding the risks from contaminants associated with fine-grained sediment, predict longer-term delta morphological evolution, and inform managers planning future coastal restoration efforts

Washington↗

USGS2022

Mechanisms of forest resilience

Ecosystems are dynamic systems with complex responses to environmental variation. In response to pervasive stressors of changing climate and disturbance regimes, many ecosystems are realigning rapidly across spatial scales, in many cases moving outside of their observed historical range of variation into alternative ecological states. In some cases, these new states are transitory and represent successional stages that may ultimately revert to the pre-disturbance condition; in other cases, alternative states are persistent and potentially self-reinforcing, especially under conditions of altered climate, disturbance regimes, and influences of non-native species. These reorganized states may appear novel, but reorganization is a characteristic ecosystem response to environmental variation that has been expressed and documented throughout the paleoecological record. Resilience, the ability of an ecosystem to recover or adapt following disturbance, is an emergent property that results from the expression of multiple mechanisms operating across levels of organism, population, and community. We outline a unifying framework of ecological resilience based on ecological mechanisms that lead to outcomes of persistence, recovery, and reorganization. Persistence is the ability of individuals to tolerate exposure to environmental stress, disturbance, or competitive interactions. As a direct expression of life history evolution and adaptation to environmental variation and stress, persistence is manifested most directly in survivorship and continued growth and reproduction of established individuals. When persistence has been overcome (e.g., following mortality from stress, disturbance, or both), populations must recover by reproduction. Recovery requires the establishment of new individuals from seed or other propagules following dispersal from the parent plant. When recovery fails to re-establish the pre-disturbance community, the ecosystem will assemble into a new state. Reorganization occurs along a gradient of magnitude, from changes in the relative dominance of species present in a community, to individual species replacements within an essentially intact community, to complete species turnover and shift to dominance by plants of different functional types, e.g. transition from forest to shrub or grass dominance. When this latter outcome is persistent and involves reinforcing mechanisms, the resulting state represents a vegetation type conversion (VTC), which in this framework represents an end member of reorganization processes. We explore reorganization in greater detail as this phase is increasingly observed but the least understood of the resilience responses. This resilience framework provides a direct and actionable basis for ecosystem management in a rapidly changing world, by targeting specific components of ecological response and managing for sustainable change.

Forest Ecology and Management↗

USGS2003

Monitoring vegetation phenology using MODIS

Accurate measurements of regional to global scale vegetation dynamics (phenology) are required to improve models and understanding of inter-annual variability in terrestrial ecosystem carbon exchange and climate–biosphere interactions. Since the mid-1980s, satellite data have been used to study these processes. In this paper, a new methodology to monitor global vegetation phenology from time series of satellite data is presented. The method uses series of piecewise logistic functions, which are fit to remotely sensed vegetation index (VI) data, to represent intra-annual vegetation dynamics. Using this approach, transition dates for vegetation activity within annual time series of VI data can be determined from satellite data. The method allows vegetation dynamics to be monitored at large scales in a fashion that it is ecologically meaningful and does not require pre-smoothing of data or the use of user-defined thresholds. Preliminary results based on an annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS) data for the northeastern United States demonstrate that the method is able to monitor vegetation phenology with good success.

Remote Sensing of Environment↗

USGS2020

The influence of climate variability on the accuracy of NHD perennial and non-perennial stream classifications

National Hydrography Dataset (NHD) stream permanence classifications (SPC; perennial, intermittent, and ephemeral) are widely used for data visualization and applied science, and have implications for resource policy and management. NHD SPC were assigned using a combination of topographic field surveys and interviews with local residents. However, previous studies indicate that non‐NHD, in situ streamflow observations (NNO) frequently disagree with NHD SPC. We hypothesized that differences in annual climate conditions between map creation years and the years NNO were collected contributed to disagreement between NNO and NHD SPC. We compared NHD SPC to 10,055 NNO (classified as “wet” or “dry”) collected in the Pacific Northwest between 1977 and 2015. Annual climate conditions were described with the Palmer Drought Severity Index (PDSI). Stream order was added as a covariate to account for different effects along the stream network. NHD SPC agreed with 80.5% of NNO. “Dry” NNO were five times more likely to disagree with NHD than “wet” NNO ( p < 0.0001). Disagreement was greatest on first‐order streams. When NHD SPC were collected during a wetter period than NNO the probability of disagreement increased by a factor of 1.17 ( p < 0.0001) per unit difference in PDSI. The influence of climate on disagreements between NNO and NHD SPC provides support for the continued development of dynamic models representing SPC as opposed to static NHD classifications.

Journal of the American Water Resources Associatio↗

USGS2020

Impacts of environmental conditions on fleas in black-tailed prairie dog burrows

Sylvatic plague, caused by the bacterium Yersinia pestis and transmitted by fleas, occurs in prairie dogs of the western United States. Outbreaks can devastate prairie dog communities, often causing nearly 100% mortality. Three competent flea vectors, prairie dog specialists Oropsylla hirsuta and O. tuberculata , and generalist Pulex simulans , are found on prairie dogs and in their burrows. Fleas are affected by climate, which varies across the range of black‐tailed prairie dogs ( Cynomys ludovicianus ), but these effects may be ameliorated somewhat due to the burrowing habits of prairie dogs. Our goal was to assess how temperature and precipitation affect off‐host flea abundance and whether relative flea abundance varied across the range of black‐tailed prairie dogs. Flea abundance was measured by swabbing 300 prairie dog burrows at six widely distributed sites in early and late summer of 2016 and 2017. Relative abundance of flea species varied among sites and sampling sessions. Flea abundance and prevalence increased with monthly mean high temperature and declined with higher winter precipitation. Predicted climate change in North America will likely influence flea abundance and distribution, thereby impacting plague dynamics in prairie dog colonies.

Arizona, California, Colorado, Idaho, Kansas, Mont↗

USGS2020

Probabilistic application of an integrated catchment-estuary-coastal system model to assess the evolution of inlet-interrupted coasts over the 21st century

Inlet-interrupted sandy coasts are dynamic and complex coastal systems with continuously evolving geomorphological behaviors under the influences of both climate change and human activities. These coastal systems are of great importance to society (e.g., providing habitats, navigation, and recreational activities) and are affected by both oceanic and terrestrial processes. Therefore, the evolution of these inlet-interrupted coasts is better assessed by considering the entirety of the Catchment-Estuary-Coastal (CEC) systems, under plausible future scenarios for climate change and increasing pressures due to population growth and human activities. Such a holistic assessment of the long-term evolution of CEC systems can be achieved via reduced-complexity modeling techniques, which are also ably quantifying the uncertainties associated with the projections due to their lower simulation times. Here, we develop a novel probabilistic modeling framework to quantify the input-driven uncertainties associated with the evolution of CEC systems over the 21 st century. In this new approach, probabilistic assessment of the evolution of inlet-interrupted coasts is achieved by (1) probabilistically computing the exchange sediment volume between the inlet-estuary system and its adjacent coast, and (2) distributing the computed sediment volumes along the inlet-interrupted coast. The model is applied at three case study sites: Alsea estuary (United States), Dyfi estuary (United Kingdom), and Kalutara inlet (Sri Lanka). Model results indicate that there are significant uncertainties in projected volume exchange at all the CEC systems (min-max range of 2.0 million cubic meters in 2100 for RCP 8.5), and the uncertainties in these projected volumes illustrate the need for probabilistic modeling approaches to evaluate the long-term evolution of CEC systems. A comparison of 50 th percentile probabilistic projections with deterministic estimates shows that the deterministic approach overestimates the sediment volume exchange in 2100 by 15–30% at Alsea and Kalutara estuary systems. Projections of coastline change obtained for the case study sites show that accounting for all key processes governing coastline change along inlet-interrupted coasts in computing coastline change results in projections that are between 20 and 134% greater than the projections that would be obtained if only the Bruun effect were taken into account, underlining the inaccuracies associated with using the Bruun rule at inlet-interrupted coasts.

Frontiers in Applied Mathematics and Statistics↗

USGS2021

Diets and stable isotope signatures of native and nonnative Leucisid fishes advances our understanding of the Yellowstone Lake food web

(1) Many forage fishes, such as Leucisids (minnows) have depauperate studies on diet composition or stable isotope signatures, as these fishes are often only viewed as food for higher trophic levels. The need exists to understand and document the diet and stable isotope signatures of Leucisids (redside shiner, longnose dace, lake chub) in relation to the community ecology and food-web dynamics in Yellowstone Lake, especially given an invasive piscivore introduction and potential future effects of climate change on the Yellowstone Lake ecosystem. (2) Diet data collected during summer of 2020 were analyzed by species using proportion by number, frequency of occurrence, and mean proportion by weight, and diet overlap was compared using Schoener’s index (D). Stable isotope (δ 15 N and δ 13 C) values were estimated by collecting tissue during the summer of 2020 by species, and isotopic overlap was compared using 40% Bayesian ellipses. (3) Nonnative redside shiners and lake chub had similar diets, and native longnose dace diet differed from the nonnative Leucisids. Diet overlap was also higher between the nonnative Leucisids, and insignificant when comparing native and nonnative Leucisids. No evidence existed to suggest a difference in δ 15 N signatures among the species. Longnose dace had a mean δ 13 C signature of −15.65, indicating an decreased reliance on pelagic prey compared to nonnative Leucisids. Nonnative redside shiners and lake chub shared 95% of isotopic niche space, but stable isotope overlap was <25% for comparisons between native longnose dace and the nonnative Leucisids. (4) This study established the diet composition and stable isotope signatures of Leusicids residing in Yellowstone Lake, thus expanding our knowledge of Leucisid feeding patterns and ecology in relation to the native and nonnative species in the ecosystem. We also expand upon our knowledge of Leucisids in North America. Additionally, quantifying minnow diets can provide a baseline for understanding food web response to invasive suppression management actions.

Wyoming↗

USGS2012

Uncertainty quantification for environmental models

Environmental models are used to evaluate the fate of fertilizers in agricultural settings (including soil denitrification), the degradation of hydrocarbons at spill sites, and water supply for people and ecosystems in small to large basins and cities—to mention but a few applications of these models. They also play a role in understanding and diagnosing potential environmental impacts of global climate change. The models are typically mildly to extremely nonlinear. The persistent demand for enhanced dynamics and resolution to improve model realism [17] means that lengthy individual model execution times will remain common, notwithstanding continued enhancements in computer power. In addition, high-dimensional parameter spaces are often defined, which increases the number of model runs required to quantify uncertainty [2]. Some environmental modeling projects have access to extensive funding and computational resources; many do not. The many recent studies of uncertainty quantification in environmental model predictions have focused on uncertainties related to data error and sparsity of data, expert judgment expressed mathematically through prior information, poorly known parameter values, and model structure (see, for example, [1,7,9,10,13,18]). Approaches for quantifying uncertainty include frequentist (potentially with prior information [7,9]), Bayesian [13,18,19], and likelihood-based. A few of the numerous methods, including some sensitivity and inverse methods with consequences for understanding and quantifying uncertainty, are as follows: Bayesian hierarchical modeling and Bayesian model averaging; single-objective optimization with error-based weighting [7] and multi-objective optimization [3]; methods based on local derivatives [2,7,10]; screening methods like OAT (one at a time) and the method of Morris [14]; FAST (Fourier amplitude sensitivity testing) [14]; the Sobol' method [14]; randomized maximum likelihood [10]; Markov chain Monte Carlo (MCMC) [10]. There are also bootstrapping and cross-validation approaches.Sometimes analyses are conducted using surrogate models [12]. The availability of so many options can be confusing. Categorizing methods based on fundamental questions assists in communicating the essential results of uncertainty analyses to stakeholders. Such questions can focus on model adequacy (e.g., How well does the model reproduce observed system characteristics and dynamics?) and sensitivity analysis (e.g., What parameters can be estimated with available data? What observations are important to parameters and predictions? What parameters are important to predictions?), as well as on the uncertainty quantification (e.g., How accurate and precise are the predictions?). The methods can also be classified by the number of model runs required: few (10s to 1000s) or many (10,000s to 1,000,000s). Of the methods listed above, the most computationally frugal are generally those based on local derivatives; MCMC methods tend to be among the most computationally demanding. Surrogate models (emulators)do not necessarily produce computational frugality because many runs of the full model are generally needed to create a meaningful surrogate model. With this categorization, we can, in general, address all the fundamental questions mentioned above using either computationally frugal or demanding methods. Model development and analysis can thus be conducted consistently using either computation-ally frugal or demanding methods; alternatively, different fundamental questions can be addressed using methods that require different levels of effort. Based on this perspective, we pose the question: Can computationally frugal methods be useful companions to computationally demanding meth-ods? The reliability of computationally frugal methods generally depends on the model being reasonably linear, which usually means smooth nonlin-earities and the assumption of Gaussian errors; both tend to be more valid with more linear

SIAM News↗

USGS2026

Millennial-scale climatic and cultural impacts on vegetation and fire at the southern edge of the Rocky Mountains, USA

Mountain forests and woodlands in semiarid regions of the world are threatened by climatic change and other human impacts. In the southwestern USA, climate and culturally driven changes to the structure and fire regimes of dry coniferous forests over recent centuries are well documented by tree-ring archives. However, the roles of climate and people as drivers of millennial-scale changes are less understood. We present a new record of vegetation dynamics and regional fire activity inferred from pollen, microscopic charcoal, and sediment geochemistry from Santa Fe Lake, NM (3532 m a.s.l.), the southernmost natural lake in the Rocky Mountains. Declining elemental intensities (e.g., Ti, Fe, Si) record local deglaciation after 14,000 cal yr BP followed by upslope expansion of trees indicated by increasing Pinus and Picea pollen percentages as the climate warmed during the Late Glacial. Cool, dry growing seasons probably limited the establishment of dense forests until after 12,200 cal yr BP when we hypothesize that Pinus ponderosa (ponderosa pine) expanded regionally (i.e., within 10–100 km) due to continued warming and a possible increase in monsoonal precipitation. Subalpine forests established near Santa Fe Lake by 10,300 cal yr BP as indicated by abundant conifer stomates and increasing Picea and Pinus aristata pollen percentages, which are highest after 5100 cal yr BP. Increasing Juniperus -type, Quercus , and Pinus edulis -type pollen record changes in vegetation belts at lower elevations after 10,300 cal yr BP. Maize pollen, an unambiguous indicator of Puebloan agriculture, first appears at 800 cal yr BP (1150 cal yr CE) and is present through 1600 cal yr CE when Spanish colonization brought cultural upheaval and population loss to local indigenous communities. Regional fire activity, inferred from microscopic charcoal influx, remained relatively constant for most of our record. However, average microscopic charcoal influx more than doubled relative to the rest of the Holocene during the last 2000 years, with the highest influx after 1550 cal yr BP (400 cal yr CE). The increased influx of microscopic charcoal coincides with archaeological evidence for dramatic regional population growth in the northern Rio Grande region and probably reflects extensive cultural burning associated with the development of an agricultural economy. Although climate was an important driver of vegetation change and fire regimes in the mountains of the southwestern USA during the Holocene, our results suggest that human impacts on regional burning during the last 2000 years far exceeded earlier climate-driven variation.

Rocky Mountains↗

USGS2016

Population trends for North American winter birds based on hierarchical models

Managing widespread and persistent threats to birds requires knowledge of population dynamics at large spatial and temporal scales. For over 100 yrs, the Audubon Christmas Bird Count (CBC) has enlisted volunteers in bird monitoring efforts that span the Americas, especially southern Canada and the United States. We employed a Bayesian hierarchical model to control for variation in survey effort among CBC circles and, using CBC data from 1966 to 2013, generated early-winter population trend estimates for 551 species of birds. Selecting a subset of species that do not frequent bird feeders and have ≥25% range overlap with the distribution of CBC circles (228 species) we further estimated aggregate (i.e., across species) trends for the entire study region and at the level of states/provinces, Bird Conservation Regions, and Landscape Conservation Cooperatives. Moreover, we examined the relationship between ten biological traits—range size, population size, migratory strategy, habitat affiliation, body size, diet, number of eggs per clutch, age at sexual maturity, lifespan, and tolerance of urban/suburban settings—and CBC trend estimates. Our results indicate that 68% of the 551 species had increasing trends within the study area over the interval 1966–2013. When trends were examined across the subset of 228 species, the median population trend for the group was 0.9% per year at the continental level. At the regional level, aggregate trends were positive in all but a few areas. Negative population trends were evident in lower latitudes, whereas the largest increases were at higher latitudes, a pattern consistent with range shifts due to climate change. Nine of 10 biological traits were significantly associated with median population trend; however, none of the traits explained >34% of the deviance in the data, reflecting the indirect relationships between population trend estimates and species traits. Trend estimates based on the CBC are broadly congruent with estimates based on the North American Breeding Bird Survey, another large-scale monitoring program. Both of these efforts, conducted by citizen scientists, will be required going forward to ensure robust inference about population dynamics in the face of climate and land cover changes.

Ecosphere↗

USGS2017

Multiple methods for multiple futures: Integrating qualitative scenario planning and quantitative simulation modeling for natural resource decision making

Scenario planning helps managers incorporate climate change into their natural resource decision making through a structured “what-if” process of identifying key uncertainties and potential impacts and responses. Although qualitative scenarios, in which ecosystem responses to climate change are derived via expert opinion, often suffice for managers to begin addressing climate change in their planning, this approach may face limits in resolving the responses of complex systems to altered climate conditions. In addition, this approach may fall short of the scientific credibility managers often require to take actions that differ from current practice. Quantitative simulation modeling of ecosystem response to climate conditions and management actions can provide this credibility, but its utility is limited unless the modeling addresses the most impactful and management-relevant uncertainties and incorporates realistic management actions. We use a case study to compare and contrast management implications derived from qualitative scenario narratives and from scenarios supported by quantitative simulations. We then describe an analytical framework that refines the case study’s integrated approach in order to improve applicability of results to management decisions. The case study illustrates the value of an integrated approach for identifying counterintuitive system dynamics, refining understanding of complex relationships, clarifying the magnitude and timing of changes, identifying and checking the validity of assumptions about resource responses to climate, and refining management directions. Our proposed analytical framework retains qualitative scenario planning as a core element because its participatory approach builds understanding for both managers and scientists, lays the groundwork to focus quantitative simulations on key system dynamics, and clarifies the challenges that subsequent decision making must address.

Climate Risk Management↗