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247 records · Page 14Linked to original sources
USGS2020

Climate-induced abrupt shifts in structural states trigger delayed transitions in functional states

Theoretical models suggest that ecosystems can be found in one of several possible alternative stable states, and a shift in structural stable state (SSS) can trigger a change in functional stable state (FSS). But we still lack the empirical evidence to confirm these states and transitions, and to inform the rates of change. Here, a 30-yr dataset from long-term ungrazed and grazed temperate grasslands was analyzed to determine whether abrupt transitions of SSS and FSS can occur. We found that the long-term ungrazed grassland experienced abrupt transitions in the dominant plant functional type (shift in SSS) that was followed by a transition between carbon sink and source 1–2 year later (shift in FSS). A directional shift in precipitation and temperature accounted for 40% of the variation in the SSS transition, while the SSS transition explained 20% of the variation in the FSS transition. In contrast, no abrupt transitions for SSS and FSS were observed in the long-term moderately grazed grassland. These findings provide important insight into the interacting effects of climate change and livestock grazing on ecosystem transitions in temperate grasslands. Moderate utilization of production in ecosystems that have co-evolved with herbivores can offset structural and functional transitions induced by climate change.

Xilin River watershed↗
USGS1999

Processes governing phytoplankton blooms in estuaries. I: The local production-loss balance

The formation and spatial distribution of phytoplankton blooms in estuaries are controlled by (1) local mechanisms, which determine the production-loss balance for a water column at a particular spatial location (i.e. control if a bloom is possible), and (2) transport-related mechanisms, which govern biomass distribution (i.e. control if and where a bloom actually occurs). In this study, the first of a 2-paper series, we use a depth-averaged numerical model as a theoretical tool to describe how interacting local conditions (water column height, light availability, benthic grazing) influence the local balance between phytoplankton sources and sinks. We also explore trends in the spatial variability of the production-loss balance across the topographic gradients between deep channels and lateral shoals which are characteristic of shallow estuaries. For example, under conditions of high turbidity and slow benthic grazing the highest rates of phytoplankton population growth are found in the shallowest regions. On the other hand, with low turbidity and rapid benthic grazing the highest growth rates occur in the deeper areas. We also explore the effects of semidiurnal tidal variation in water column height, as well as spring-neap variability. Local population growth in the shallowest regions is very sensitive to tidal-scale shallowing and deepening of the water column, especially in the presence of benthic grazing. A spring-neap signal in population growth rate is also prominent in the shallow areas. Population growth in deeper regions is less sensitive to temporal variations in tidal elevation. These results show that both shallow and deep regions of estuaries can act as sources or sinks for phytoplankton biomass, depending on the local conditions of mean water column height, tidal amplitude, light-limited growth rate, and consumption by grazers.

Marine Ecology Progress Series↗
USGS2006

Spatio-temporal availability of soft mast in clearcuts in the Southern Appalachians

Soft mast is an important resource for many wild populations in the Southern Appalachians, yet the way clear-cutting affects availability of soft mast though time is not fully understood. We tested a theoretical model of temporal availability of soft mast in clearcuts using empirical data on percent cover and berry production of Gaylussacia, Vaccinium, and Rubus spp. plants in 100 stands that were clearcut (0-122 years old) in the Southern Appalachian Mountains. We modeled the relationship between soft mast availability and stand age, evaluated the effects of topography and forest type on soft mast, developed statistical models for predicting the spatio-temporal distribution of soft mast, and tested the hypothesis that percent cover of berry plants and berry production provided similar information about soft mast availability. We found temporal dynamics explained berry production better than it predicted percent plant cover, whereas topographic variables influenced percent plant cover more than they influenced berry production. Berry production and percent plant cover were highest in ???2-9-year-old stands. Percent plant cover was lowest in 10-69-year-old stands and intermediate in 70+-year-old stands. Three of our spatio-temporal models performed well during model testing and they were not biased by the training data, indicating the inferences about spatio-temporal availability of soft mast extended beyond our sample data. The methods we used to estimate the distribution of soft mast may be useful for modeling distributions of other resources. ?? 2006 Elsevier B.V. All rights reserved.

Forest Ecology and Management↗
USGS2018

Collection methods and quality assessment for Escherichia coli, water quality, and microbial source tracking data within Tumacácori National Historical Park and the upper Santa Cruz River, Arizona, 2015-16

Tumacácori National Historical Park protects the culturally important Mission, San José de Tumacácori, while also managing a portion of the ecologically diverse riparian corridor of the Santa Cruz River. This report describes the methods and quality assurance procedures used in the collection of water samples for the analysis of Escherichia coli ( E. coli ), microbial source tracking markers, suspended sediment, water-quality parameters, turbidity, and the data collection for discharge and stage; the process for data review and approval is also described. Finally, this report provides a quantitative assessment of the quality of the E. coli , microbial source tracking, and suspended sediment data. The data-quality assessment revealed that bias attributed to field and laboratory contamination was minimal, with E. coli detections in only 3 out of 33 field blank samples analyzed. Concentrations in the field blanks were several orders of magnitude lower than environmental concentrations. The microbial source tracking (MST) field blank was below the detection limit for all MST markers analyzed. Laboratory blanks for E. coli at the USGS Arizona Water Science Center and laboratory blanks for MST markers at the USGS Ohio Water Microbiology Laboratory were all below the detection limit. Irreplicate data for E. coli and suspended sediment indicated that bias was not introduced to the data by combining samples collected using discrete sampling methods with samples collected using automatic sampling methods. The split and sequential E. coli replicate data showed consistent analytical variability and a single equation was developed to explain the variability of E. coli concentrations. An additional analysis of analytical variability for E. coli indicated analytical variability around 18 percent relative standard deviation and no trend was observed in the concentration during the processing and analysis of multiple split-replicates. Two replicate samples were collected for MST and individual markers were compared for a base flow and flood sample. For the markers found in common between the two types of samples, the relative standard deviation for the base flow sample was more than 3 times greater than the markers in the flood sample. Sequential suspended sediment replicates had a relative standard deviation of about 1.3 percent, indicating that environmental and analytical variability was minimal. A holding time review and laboratory study analysis supported the extended holding times required for this investigation. Most concentrations for flood and base-flow samples were within the theoretical variability specified in the most probable number approach suggesting that extended hold times did not overly influence the final concentrations reported.

Arizona↗
USGS2014

Strategies for preventing invasive plant outbreaks after prescribed fire in ponderosa pine forest

Land managers use prescribed fire to return a vital process to fire-adapted ecosystems, restore forest structure from a state altered by long-term fire suppression, and reduce wildfire intensity. However, fire often produces favorable conditions for invasive plant species, particularly if it is intense enough to reveal bare mineral soil and open previously closed canopies. Understanding the environmental or fire characteristics that explain post-fire invasive plant abundance would aid managers in efficiently finding and quickly responding to fire-caused infestations. To that end, we used an information-theoretic model-selection approach to assess the relative importance of abiotic environmental characteristics (topoedaphic position, distance from roads), pre-and post-fire biotic environmental characteristics (forest structure, understory vegetation, fuel load), and prescribed fire severity (measured in four different ways) in explaining invasive plant cover in ponderosa pine forest in South Dakota’s Black Hills. Environmental characteristics (distance from roads and post-fire forest structure) alone provided the most explanation of variation (26%) in post-fire cover of Verbascum thapsus (common mullein), but a combination of surface fire severity and environmental characteristics (pre-fire forest structure and distance from roads) explained 36–39% of the variation in post-fire cover of Cirsium arvense (Canada thistle) and all invasives together. For four species and all invasives together, their pre-fire cover explained more variation (26–82%) in post-fire cover than environmental and fire characteristics did, suggesting one strategy for reducing post-fire invasive outbreaks may be to find and control invasives before the fire. Finding them may be difficult, however, since pre-fire environmental characteristics explained only 20% of variation in pre-fire total invasive cover, and less for individual species. Thus, moderating fire intensity or targeting areas of high severity for post-fire invasive control may be the most efficient means for reducing the chances of post-fire invasive plant outbreaks when conducting prescribed fires in this region.

South Dakota, Wyoming↗
USGS2017

A multi-species synthesis of physiological mechanisms in drought-induced tree mortality

Widespread tree mortality associated with drought has been observed on all forested continents and global change is expected to exacerbate vegetation vulnerability. Forest mortality has implications for future biosphere–atmosphere interactions of carbon, water and energy balance, and is poorly represented in dynamic vegetation models. Reducing uncertainty requires improved mortality projections founded on robust physiological processes. However, the proposed mechanisms of drought-induced mortality, including hydraulic failure and carbon starvation, are unresolved. A growing number of empirical studies have investigated these mechanisms, but data have not been consistently analysed across species and biomes using a standardized physiological framework. Here, we show that xylem hydraulic failure was ubiquitous across multiple tree taxa at drought-induced mortality. All species assessed had 60% or higher loss of xylem hydraulic conductivity, consistent with proposed theoretical and modelled survival thresholds. We found diverse responses in non-structural carbohydrate reserves at mortality, indicating that evidence supporting carbon starvation was not universal. Reduced non-structural carbohydrates were more common for gymnosperms than angiosperms, associated with xylem hydraulic vulnerability, and may have a role in reducing hydraulic function. Our finding that hydraulic failure at drought-induced mortality was persistent across species indicates that substantial improvement in vegetation modelling can be achieved using thresholds in hydraulic function.

Nature Ecology & Evolution↗
USGS2021

Detecting resource limitation in a large herbivore population is enhanced with measures of nutritional condition

Resource limitation at the population level is a function of forage quality and its abundance relative to its per capita availability, which in turn, determines nutritional condition of individuals. Effects of resource limitation on population dynamics in ungulates often occur through predictable and sequential changes in vital rates, which can enable assessments of how resource limitation influences population growth. We tested theoretical predictions of bottom-up (i.e., resource limitation) forcing on moose ( Alces alces ) through the lens of vital rates by quantifying the relative influence of intrinsic measures of nutritional condition and extrinsic measures of remotely sensed environmental data on demographic rates. We measured rates of pregnancy, parturition, juvenile, and adult survival for 82 adult females in a population where predators largely were absent. Life stage simulation analyses (LSAs) indicated that interannual fluctuations in adult survival contributed to most of the variability in λ. We then extended the LSA to estimate vital rates as a function of bottom-up covariates to evaluate their influence on λ. We detected weak signatures of effects from environmental covariates that were remotely sensed and spatially explicit to each seasonal range. Instead, nutritional condition strongly influenced rates of pregnancy, parturition, and overwinter survival of adults, clearly implicating resource limitation on λ. Our findings depart from the classic life-history paradigm of population dynamics in ungulates in that adult survival was highly variable and generated most of the variability in population growth rates. At the surface, lack of variation explained by environmental covariates may suggest weak evidence of resource limitation in the population, when nutritional condition actually underpinned most demographics. We suggest that variability in vital rates and effects of resource limitation may depend on context more than previously appreciated, and density dependence can obfuscate the relationships between remotely sensed data and demographic rates.

Wyoming↗
USGS2026

Demographic responses to experimental manipulation of an enclosed population of invasive brown treesnakes (Boiga irregularis)

Understanding how invasive species populations respond to perturbations can give insight into effective management. However, opportunities to test theoretical population responses are rare, as experimental perturbations often run counter to management goals (e.g., suppression). On the island of Guam, a 5-ha outdoor enclosure (with no immigration or emigration) constructed in 2003 serves as a unique population-scale study area for invasive brown treesnakes ( Boiga irregularis ). Capture-mark-recapture studies (CMR) and several experiments have taken place since 2004. Here, we analyze a subset of the CMR data collected between 2008 and 2012, a time period encompassing two experimental perturbations: (1) supplemental feeding to determine if the population is food limited and (2) targeted removal of adult and large-subadult cohorts to quantify the timing and magnitude of the population's demographic response to a simulated control effort. We used the CMR data to fit a Pradel model in a Bayesian framework to compare the population growth rate, survival probability, and recruitment rate prior to, during, and post perturbations. We found that increasing food resources increased population growth and recruitment rates, but that survival remained relatively constant. The removal perturbation resulted in a decrease in population growth rate and survival immediately after the removal, but population growth rate and recruitment began increasing 1 year after the removal perturbation. Our results suggest that brown treesnakes in the enclosed population are prey limited, likely constraining population growth, and that the suppression of population growth after removal of adults and large sub-adults is short-lived (i.e., less than a year).

Guam↗
USGS2021

Habitat associations of breeding conifer-associated birds in managed and regenerating forested stands

Forests are often affected by management that could influence demographics of breeding and post-breeding birds that reside within. Numerous studies have focused on immediate effects from management on wildlife soon after forestry treatment (e.g., 0–5 years), however, fewer studies have examined changes in focal species abundance over longer durations as a forest regenerates after disturbance. We examined how forest management influenced 18 conifer-associated birds during breeding and post-breeding over the forest regeneration period in a landscape dominated by forestry. To achieve this, we paired avian detection data from point count surveys in lowland conifer and mixed-wood forests with Bayesian distance-removal models and an information-theoretic framework. We estimated abundance and associations with seven common forestry treatment categories applied at the stand scale, years-since-harvest (YSH; 5–120+), and seven vegetation variables measured within stands. Forestry treatment categories and YSH were poor predictors of abundance, and none of the 14 species with good-fitting models had associations with these covariates. Twelve of 13 species with good-fitting models had important associations between abundance and vegetation variables. All vegetation variables were associated with abundance of some species, irrespective of the forestry treatment in which the site occurred, including spruce-fir tree composition (seven species), tree basal area (six species), midstory cover (five species), live crown ratio (three species), shrub cover (three species), tree diameter at breast height (two species), and shrub composition (one species). In a companion study, several species assemblages were associated with vegetation variables (i.e., spruce-fir tree composition, tree basal area, and tree diameter at breast height) that varied with YSH and forestry treatments, suggesting that some forestry treatments may indirectly influence avian abundance when certain vegetation outcomes are achieved. Our results suggest that managers should target species-specific vegetation outcomes rather than more broadly categorized forestry treatment types when managing for individual focal species because of large variations in vegetative outcomes across stands within a forest treatment category. Our study informs management and conservation of biodiversity in regions such as the Atlantic Northern Forest where commercial forestry is the dominant human land use.

Maine, New Hampshire, Vermont↗
USGS2021

Gene flow influences the genomic architecture of local adaptation in six riverine fish species

Understanding how gene flow influences adaptive divergence is important for predicting adaptive responses. Theoretical studies suggest that when gene flow is high, clustering of adaptive genes in fewer genomic regions would protect adaptive alleles from recombination and thus be selected for, but few studies have tested it with empirical data. Here, we used restriction site-associated sequencing to generate genomic data for six fish species with contrasting life histories from six reaches of the Upper Mississippi River System, USA. We used four differentiation-based outlier tests and three genotype–environment association analyses to define neutral single nucleotide polymorphisms (SNPs) and outlier SNPs that were putatively under selection. We then examined the distribution of outlier SNPs along the genome and investigated whether these SNPs were found in genomic islands of differentiation and inversions. We found that gene flow varied among species, and outlier SNPs were clustered more tightly in species with higher gene flow. The two species with the highest overall F ST (0.0303–0.0720) and therefore lowest gene flow showed little evidence of clusters of outlier SNPs, with outlier SNPs in these species spreading uniformly across the genome. In contrast, nearly all outlier SNPs in the species with the lowest F ST (0.0003) were found in a single large putative inversion. Two other species with intermediate gene flow ( F ST ~ 0.0025–0.0050) also showed clustered genomic architectures, with most islands of differentiation clustered on a few chromosomes. Our results provide important empirical evidence to support the hypothesis that increasingly clustered architecture of local adaptation is associated with high gene flow.

Molecular Ecology↗
USGS2020

Optimal spatial prioritization of control resources for elimination of invasive species under demographic uncertainty

Populations of invasive species often spread heterogeneously across a landscape, consisting of local populations that cluster in space but are connected by dispersal. A fundamental dilemma for invasive species control is how to optimally allocate limited fiscal resources across local populations. Theoretical work based on perfect knowledge of demographic connectivity suggests that targeting local populations from which migrants originate (sources) can be optimal. However, demographic processes such as abundance and dispersal can be highly uncertain, and the relationship between local population density and damage costs (damage function) is rarely known. We used a metapopulation model to understand how budget and uncertainty in abundance, connectivity, and the damage function, together impact return on investment (ROI) for optimal control strategies. Budget, observational uncertainty, and the damage function had strong effects on the optimal resource allocation strategy. Uncertainty in dispersal probability was the least important determinant of ROI. The damage function determined which resource prioritization strategy was optimal when connectivity was symmetric but not when it was asymmetric. When connectivity was asymmetric, prioritizing source populations had a higher ROI than allocating effort equally across local populations, regardless of the damage function, but uncertainty in connectivity structure and abundance reduced ROI of the optimal prioritization strategy by 57% on average depending on the control budget. With low budgets (monthly removal rate of 6.7% of population), there was little advantage to prioritizing resources, especially when connectivity was high or symmetric, and observational uncertainty had only minor effects on ROI. Allotting funding for improved monitoring appeared to be most important when budgets were moderate (monthly removal of 13–20% of the population). Our result showed that multiple sources of observational uncertainty should be considered concurrently for optimizing ROI. Accurate estimates of connectivity direction and abundance were more important than accurate estimates of dispersal rates. Developing cost-effective surveillance methods to reduce observational uncertainties, and quantitative frameworks for determining how resources should be spatially apportioned to multiple monitoring and control activities are important and challenging future directions for optimizing ROI for invasive species control programs.

Ecological Applications↗
USGS2007

A Bayesian state-space formulation of dynamic occupancy models

Species occurrence and its dynamic components, extinction and colonization probabilities, are focal quantities in biogeography and metapopulation biology, and for species conservation assessments. It has been increasingly appreciated that these parameters must be estimated separately from detection probability to avoid the biases induced by nondetection error. Hence, there is now considerable theoretical and practical interest in dynamic occupancy models that contain explicit representations of metapopulation dynamics such as extinction, colonization, and turnover as well as growth rates. We describe a hierarchical parameterization of these models that is analogous to the state-space formulation of models in time series, where the model is represented by two components, one for the partially observable occupancy process and another for the observations conditional on that process. This parameterization naturally allows estimation of all parameters of the conventional approach to occupancy models, but in addition, yields great flexibility and extensibility, e.g., to modeling heterogeneity or latent structure in model parameters. We also highlight the important distinction between population and finite sample inference; the latter yields much more precise estimates for the particular sample at hand. Finite sample estimates can easily be obtained using the state-space representation of the model but are difficult to obtain under the conventional approach of likelihood-based estimation. We use R and Win BUGS to apply the model to two examples. In a standard analysis for the European Crossbill in a large Swiss monitoring program, we fit a model with year-specific parameters. Estimates of the dynamic parameters varied greatly among years, highlighting the irruptive population dynamics of that species. In the second example, we analyze route occupancy of Cerulean Warblers in the North American Breeding Bird Survey (BBS) using a model allowing for site-specific heterogeneity in model parameters. The results indicate relatively low turnover and a stable distribution of Cerulean Warblers which is in contrast to analyses of counts of individuals from the same survey that indicate important declines. This discrepancy illustrates the inertia in occupancy relative to actual abundance. Furthermore, the model reveals a declining patch survival probability, and increasing turnover, toward the edge of the range of the species, which is consistent with metapopulation perspectives on the genesis of range edges. Given detection/non-detection data, dynamic occupancy models as described here have considerable potential for the study of distributions and range dynamics.

Ecology↗
USGS2011

Comparison of statistical and theoretical habitat models for conservation planning: The benefit of ensemble prediction

Selection of a modeling approach is an important step in the conservation planning process, but little guidance is available. We compared two statistical and three theoretical habitat modeling approaches representing those currently being used for avian conservation planning at landscape and regional scales: hierarchical spatial count (HSC), classification and regression tree (CRT), habitat suitability index (HSI), forest structure database (FS), and habitat association database (HA). We focused our comparison on models for five priority forest-breeding species in the Central Hardwoods Bird Conservation Region: Acadian Flycatcher, Cerulean Warbler, Prairie Warbler, Red-headed Woodpecker, and Worm-eating Warbler. Lacking complete knowledge on the distribution and abundance of each species with which we could illuminate differences between approaches and provide strong grounds for recommending one approach over another, we used two approaches to compare models: rank correlations among model outputs and comparison of spatial correspondence. In general, rank correlations were significantly positive among models for each species, indicating general agreement among the models. Worm-eating Warblers had the highest pairwise correlations, all of which were significant ( P , 0.05). Red-headed Woodpeckers had the lowest agreement among models, suggesting greater uncertainty in the relative conservation value of areas within the region. We assessed model uncertainty by mapping the spatial congruence in priorities (i.e., top ranks) resulting from each model for each species and calculating the coefficient of variation across model ranks for each location. This allowed identification of areas more likely to be good targets of conservation effort for a species, those areas that were least likely, and those in between where uncertainty is higher and thus conservation action incorporates more risk. Based on our results, models developed independently for the same purpose (conservation planning for a particular species in a particular geography) yield different answers and thus different conservation strategies. We assert that using only one habitat model (even if validated) as the foundation of a conservation plan is risky. Using multiple models (i.e., ensemble prediction) can reduce uncertainty and increase efficacy of conservation action when models corroborate one another and increase understanding of the system when they do not.

Ecological Applications↗