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Results for “Journal of Applied Statistics”

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At least 127 records · Page 7Linked to original sources
USGS1999

Evaluation of the flood-pulse concept based on statistical models of growth of selected fishes of the upper Mississippi River system

The flood-pulse concept (FPC) states that annual inundation is the principal force responsible for productivity and biotic interactions in river-floodplain systems. Somatic growth is one component of production, and we hypothesized that, if the FPC applies, growth of fishes that use the moving littoral zone should differ among years with differing flood pattern, whereas nonlittoral fishes would show no such response. Growth of largemouth bass (Micropterus salmoides) and bluegill (Lepomis macrochirus), species that exploit littoral resources, increased during a year having an unusual warm-season flood in the Upper Mississippi River system and was reduced during low-water years. Growth of white bass (Morone chrysops), which do not rely heavily on the littoral zone, did not differ significantly between the extreme-flood and low-water years. Patterns of growth of black crappie (Pomoxis nigromaculatus), which have intermediate dependence on the moving littoral zone, were somewhat ambiguous. These results are consistent with the hypothesis that the FPC applies, at least under certain conditions, to this temperate river system. Our results can also provide an important basis from which to assess some costs and benefits of water level management strategies in large regulated temperate rivers.

Canadian Journal of Fisheries and Aquatic Sciences↗
USGS2011

Nutrients and sediment in frozen-ground runoff from no-till fields receiving liquid-dairy and solid-beef manures

Nutrients and sediment in surface runoff from frozen agricultural fields were monitored within three small (16.0 ha [39.5 ac] or less), adjacent basins at a no-till farm in southwest Wisconsin during four winters from 2003 to 2004 through 2006 to 2007. Runoff depths and flow-weighted constituent concentrations were compared to determine the impacts of surface-applied liquid-dairy or solid-beef manure to frozen and/or snow-covered ground. Despite varying the manure type and the rate and timing of applications, runoff depths were not significantly different among basins within each winter period. Sediment losses were low (generally less than 22 kg ha −1 [20 lb ac −1 ] in any year) and any statistical differences in sediment concentrations among basins were not related to the presence or absence of manure or the amount of runoff. Concentrations and losses of total nitrogen and total phosphorus were significantly increased in basins that had either manure type applied less than one week preceding runoff. These increases occurred despite relatively low application rates. Lower concentrations and losses were measured in basins that had manure applied in fall and early winter and an extended period of time (months) had elapsed before the first runoff event. The highest mean, flow-weighted concentrations of total nitrogen (31.8 mg L −1 ) and total phosphorus (10.9 mg L −1 ) occurred in winter 2003 to 2004, when liquid-dairy manure was applied less than one week before runoff. On average, dissolved phosphorus accounted for over 80% of all phosphorus measured in runoff during frozen-ground periods. The data collected as part of this study add to the limited information on the quantity and quality of frozen-ground runoff at field edges, and the results highlight the importance of manure management decisions during frozen-ground periods to minimize nutrients lost in surface runoff.

Wisconsin↗
USGS2025

Causal analysis of fire regime drivers in California

Background Understanding the relative contribution of climate and human factors to wildfires is critical for managing risk across California’s diverse ecosystems, in the United States (US). Aims We propose a model that distinguishes between proximate and ultimate drivers of fire regimes and apply it to a century of fire and climate data to assess regional variation in causal mechanisms. Methods We analyzed fire statistics (1910–2021) alongside climate and weather data, stratifying the state by 10 ecoregions. Key results Northern forests had the strongest correlation with the proximate factor fuel aridity, ultimately due to climate. Fire rotation intervals exceeded 100 years, implicating woody fuel accumulation as an additional factor. Lightning ignitions occurred in decadal bursts, with dense strike events potentially overwhelming fire-fighting resources. Lower elevation/latitude foothill ecoregions experienced highest fire activity following wet winters and springs, implicating control by herbaceous fuel loads and a negative effect of global warming on future fires. Human ignitions dominate in these ecoregions, and population growth contributes to expansion of powerlines, a major ignition source. Conclusions While climate change may increase fire activity in forested ecoregions, its role is less pronounced in non-forested ecoregions, where human ignition sources are the dominant factor. Implications Different areas within ecoregions may require different management actions that reflect the specific proximate and ultimate factors at play.

California↗
USGS2010

Geographic variation in the plumage coloration of willow flycatchers Empidonax traillii

The ability to identify distinct taxonomic groups of birds (species, subspecies, geographic races) can advance ecological research efforts by determining connectivity between the non-breeding and breeding grounds for migrant species, identifying the origin of migrants, and helping to refine boundaries between subspecies or geographic races. Multiple methods are available to identify taxonomic groups (e.g., morphology, genetics), and one that has played an important role for avian taxonomists over the years is plumage coloration. With the advent of electronic devices that can quickly and accurately quantify plumage coloration, the potential of using coloration as an identifier for distinct taxonomic groups, even when differences are subtle, becomes possible. In this study, we evaluated the degree to which plumage coloration differs among the four subspecies of the willow flycatcher Empidonax traillii, evaluated sources of variation, and considered the utility of plumage coloration to assign subspecies membership for individuals of unknown origin. We used a colorimeter to measure plumage coloration of 374 adult willow flycatchers from 29 locations across their breeding range in 2004 and 2005. We found strong statistical differences among the mean plumage coloration values of the four subspecies; however, while individuals tended to group around their respective subspecies' mean color value, the dispersion of individuals around such means overlapped. Mean color values for each breeding site of the three western subspecies clustered together, but the eastern subspecies' color values were dispersed among the other subspecies, rather than distinctly clustered. Additionally, sites along boundaries showed evidence of intergradation and intermediate coloration patterns. We evaluated the predictive power of colorimeter measurements on flycatchers by constructing a canonical discriminant model to predict subspecies origin of migrants passing through the southwestern U.S. Considering only western subspecies, we found that individuals can be assigned with reasonable certainty. Applying the model to migrants sampled along the Colorado River in Mexico and the U.S. suggests different migration patterns for the three western subspecies. We believe that the use of plumage coloration, as measured by electronic devices, can provide a powerful tool to look at ecological questions in a wide range of avian species.

Journal of Avian Biology↗
USGS1978

Insecticides applied to a nursery colony of little brown bats (Myotis lucifugus): Lethal concentrations in brain tissues

Forty-six Myotis lucifugus were collected in May and June 1974 at a nursery colony in Hillsborough County, New Hampshire, that had been sprayed with DDT and chlordane in August and September 1973. When collected, 27 bats were alive, two were convulsing, and 17 were dead. Brains, carcasses, and milk and masticated insects from stomachs were analyzed for organochlorine insecticides and poly-chlorinated biphenyls (PCB's). Concentrations of chemical residues in brains of surviving bats were compared with those of dead and convulsing bats. These comparisons indicated that DDT was the cause of death. Lethal brain concentrations of DDT in adult females averaged 24.52 parts per million (ppm) and suggested that adult M. lucifugus are approximately twice as sensitive to DDT as are adult laboratory rats and mice. Juvenile bats were about 1.5 times more sensitive than adult bats. Large chemical residues were present in milk. We found a statistically significant relationship between declines in carcass residues in lactating females and uterine regression for six of 10 toxicants. Among juveniles, there were corresponding, significant increases (for nine of 10 toxicants) in carcass levels of residues correlated with increasing age (growth of forearm).

New Hampsire↗
USGS2021

Techniques to improve ecological interpretability of black box machine learning models

Statistical modeling of ecological data is often faced with a large number of variables as well as possible nonlinear relationships and higher-order interaction effects. Gradient boosted trees (GBT) have been successful in addressing these issues and have shown a good predictive performance in modeling nonlinear relationships, in particular in classification settings with a categorical response variable. They also tend to be robust against outliers. However, their black-box nature makes it difficult to interpret these models. We introduce several recently developed statistical tools to the environmental research community in order to advance interpretation of these black-box models. To analyze the properties of the tools, we applied gradient boosted trees to investigate biological health of streams within the contiguous USA, as measured by a benthic macroinvertebrate biotic index. Based on these data and a simulation study, we demonstrate the advantages and limitations of partial dependence plots (PDP), individual conditional expectation (ICE) curves and accumulated local effects (ALE) in their ability to identify covariate–response relationships. Additionally, interaction effects were quantified according to interaction strength (IAS) and Friedman’s H 2 "> H 2 statistic. Interpretable machine learning techniques are useful tools to open the black-box of gradient boosted trees in the environmental sciences. This finding is supported by our case study on the effect of impervious surface on the benthic condition, which agrees with previous results in the literature. Overall, the most important variables were ecoregion, bed stability, watershed area, riparian vegetation and catchment slope. These variables were also present in most identified interaction effects. In conclusion, graphical tools (PDP, ICE, ALE) enable visualization and easier interpretation of GBT but should be supported by analytical statistical measures. Future methodological research is needed to investigate the properties of interaction tests. Supplementary materials accompanying this paper appear on-line.

Journal of Agricultural, Biological, and Environme↗
USGS2015

Tidal and seasonal variations in calving flux observed with passive seismology

The seismic signatures of calving events, i.e., calving icequakes, offer an opportunity to examine calving variability with greater precision than is available with other methods. Here using observations from Yahtse Glacier, Alaska, we describe methods to detect, locate, and characterize calving icequakes. We combine these icequake records with a coincident, manually generated record of observed calving events to develop and validate a statistical model through which we can infer iceberg sizes from the properties of calving icequakes. We find that the icequake duration is the single most significant predictor of an iceberg's size. We then apply this model to 18 months of seismic recordings and find elevated iceberg calving flux during the summer and fall and a pronounced lull in calving during midwinter. Calving flux is sensitive to semidiurnal tidal stage. Large calving events are tens of percent more likely during falling and low tides than during rising and high tides, consistent with a view that deeper water has a stabilizing influence on glacier termini. Multiple factors affect the occurrence of mechanical fractures that ultimately lead to iceberg calving. At Yahtse Glacier, seismology allows us to demonstrate that variations in the rate of submarine melt are a dominant control on iceberg calving rates at seasonal timescales. On hourly to daily timescales, tidal modulation of the normal stress against the glacier terminus reveals the nonlinear glacier response to changes in the near-terminus stress field.

Alaska↗
USGS2023

Revealing the extent of sea otter impacts on bivalve prey through multi-trophic monitoring and mechanistic models

Sea otters are apex predators that can exert considerable influence over the nearshore communities they occupy. Since facing near extinction in the early 1900s, sea otters are making a remarkable recovery in Southeast Alaska, particularly in Glacier Bay, the largest protected tidewater glacier fjord in the world. The expansion of sea otters across Glacier Bay offers both a challenge to monitoring and stewardship and an unprecedented opportunity to study the top-down effect of a novel apex predator across a diverse and productive ecosystem. Our goal was to integrate monitoring data across trophic levels, space, and time to quantify and map the predator–prey interaction between sea otters and butter clams Saxidomus gigantea , one of the dominant large bivalves in Glacier Bay and a favoured prey of sea otters. We developed a spatially-referenced mechanistic differential equation model of butter clam dynamics that combined both environmental drivers of local population growth and estimates of otter abundance from aerial survey data. We embedded this model in a Bayesian statistical framework and fit it to clam survey data from 43 intertidal and subtidal sites across Glacier Bay. Prior to substantial sea otter expansion, we found that butter clam density was structured by an environmental gradient driven by distance from glacier (represented by latitude) and a quadratic effect of current speed. Estimates of sea otter attack rate revealed spatial heterogeneity in sea otter impacts and a negative relationship with local shoreline complexity. Sea otter exploitation of productive butter clam habitat substantially reduced the abundance and altered the distribution of butter clams across Glacier Bay, with potential cascading consequences for nearshore community structure and function. Spatial variation in estimated sea otter predation processes further suggests that community context and local environmental conditions mediate the top-down influence of sea otters on a given prey. Overall, our framework provides high-resolution insights about the interaction among components of this food web and could be applied to a variety of other systems involving invasive species, epidemiology or migration.

Journal of Animal Ecology↗
USGS2018

Estimating fluvial discharges coincident with 21st century coastal storms modeled with CoSMoS

On the open coast, flooding is largely driven by tides, storm surge, waves, and in areas near coastal inlets, the magnitude and co-occurrence of high fluvial discharges. Statistical methods are typically used to estimate the individual probability of coastal storm and fluvial discharge occurrences for use in sophisticated flood hazard models. A challenge arises when considering possible future climate changes and the relation between the intensity of extreme coastal water levels and high fluvial discharges. In this study, the Coastal Storm Modeling System (CoSMoS) is used to dynamically downscale global climate projections to local-scale storm-driven coastal water levels, including associated fluvial discharges. An efficient approach to derive 21st century projected fluvial discharges for rivers within San Francisco Bay was developed, leveraging a readily-available time-series of projected (2010 – 2100) discharge rates of the predominant river system (the “Delta”). Delta projections were used to estimate flow rates of 8 Bay rivers for the IPCC's CMIP5 RCP4.5 climate scenario. Relationships describing discharge rates, normalized by respective watershed areas, were developed and applied to projected data to generate 21st century fluvial discharge time-series for each river. Results indicate decreasing discharge rates throughout the 21 st century with the exception of extreme flows.

California↗
USGS2006

Source parameters of microearthquakes at Mount St Helens (USA)

We estimate the source parameters for a selection of microearthquakes that occurred at Mount St Helens in the period 1995–1998. Excluding the activity of 2004 September, this time period includes the most intense episode of earthquake activity since the last dome-building eruption in 1986 October. 200 seismograms were processed to obtain seismic moments, source radii, stress drops and average fault slip. The source parameters were determined from the spectral analysis of P waves, after correction for attenuation and site effects. In particular, P -wave quality ( Q p ) and site ( S ) factors have been previously calculated in the frequency ranges 2–7 Hz and 18–30 Hz. Because it was impossible to perform corrections for Q p and S over the whole spectrum we applied a new approach, based on the notion of ‘ holed spectrum ’, to estimate spectral parameters. The term ‘ holed spectrum ’ indicates a spectrum lacking corrected spectral amplitude values at certain frequencies. We carried out a statistical study to verify that dealing with the ‘ holed spectrum ’ does not lead to significant differences in the estimates of spectral parameters. We also investigated the dependence of spectral parameters (low-frequency level, corner frequency and high-frequency decay) on the bandwidth of spectral hole, and defined the threshold values for three different spectral models. Displacement ‘ holed spectra ’, corrected by attenuation and site response, are then used to determine spectral parameters in order to calculate seismic source parameters. Seismic moments range from 10 17 to 10 19 dyne-cm, source dimensions from 100 to 350 m, and average fault slip from 0.003 to 0.1 cm. Self-similarity seems to break down in that stress drops are very low (0.1–1 bars). We postulate that seismicity is associated with a brittle shear failure mechanism occurring in a highly heterogeneous material under a relatively low stress regime.

Washington↗
USGS2024

A ‘how-to’ guide for estimating animal diel activity using hierarchical models

Animal diel activity patterns can aid understanding of (a) how species behaviourally adapt to anthropogenic and natural disturbances, (b) mechanisms of species co-existence through temporal partitioning, and (c) community or ecosystem effects of diel activity shifts. Activity patterns often vary spatially, a feature ignored by the kernel density estimators (KDEs) currently used for estimating diel activity. Ignoring this source of heterogeneity may lead to biased estimates of uncertainty and misleading conclusions regarding the drivers of diel activity. Thus, there is a need for more flexible statistical approaches for estimating activity patterns and testing hypotheses regarding their biotic and abiotic drivers. We illustrate how trigonometric terms and cyclic cubic splines combined with hierarchical models can provide a valuable alternative to KDEs. Like KDEs, these models accommodate circular data, but they can also account for site-to-site and other sources of variability, correlation amongst repeated measures, and variable sampling effort. They can also more readily quantify and test hypotheses related to the effects of covariates on activity patterns. Through empirical case studies, we illustrate how hierarchical models can quantify changes in activity levels due to seasonality and in response to biotic and abiotic factors (e.g. anthropogenic stressors and co-occurrence). We also describe frequentist and Bayesian approaches for quantifying site-specific (conditional) and population-averaged (marginal) activity patterns. We provide guidelines and tutorials with detailed step-by-step instructions for fitting and interpreting hierarchical models applied to time-stamped data, such as those recorded by camera traps and audio recorders. We conclude that this approach offers a viable, flexible, and effective alternative to KDEs when modelling animal activity patterns.

Journal of Animal Ecology↗
USGS2013

Woody vegetation communities of tidal freshwater swamps in South Carolina, Georgia and Florida (US) with comparisons to similar systems in the US and South America

Questions What are the general tree communities found in tidal freshwater swamps along four large coastal rivers in the southeastern United States (US)? How do these communities compare to other tidal freshwater swamps in the US and South America? Locations Tidal floodplains of major rivers along the Atlantic and Gulf coasts of the southeastern US: Savannah, Altamaha, Suwannee and Apalachicola Rivers. Methods An extensive survey of trees and shrubs was conducted to describe the communities from a range of tidal freshwater swamps. River basins studied include micro-tidal (Gulf coast) and meso-tidal (Atlantic coast) regimes, and study areas were located both near and distant to primary channels. A total of 128 plots (100 m2 each) were inventoried, distributed evenly over the Savannah and Altamaha Rivers along the Atlantic coast, and the Suwannee and Apalachicola Rivers along the Gulf coast. Multivariate statistics helped discern communities and the significant indicator species in each. Results Four general communities were characterized and named according to the strongest individual indicator species in each: Water Tupelo (Nyssa aquatica) Community, Swamp Tupelo (Nyssa biflora) Community, Dwarf Palmetto (Sabal minor) Community and Cabbage Palm (Sabal palmetto) Community. Conclusions Descriptions of most tidal freshwater swamps in the southeastern US fit within the communities described in this study. Because studies that make inferences between environmental drivers (e.g. salinity, hydroperiod, hurricanes) and specific community types are best applied to the same communities (but perhaps different river systems), this work provides a framework by which tidal freshwater forested wetlands can be accurately compared based on their tree communities. We suggest that, within the broad range of our inventories, the four communities described identify the primary associations that should be tracked within most tidal freshwater swamps of the US. However, we identify some river basins in the US that do not fit this construct. Diversity of major tree communities in tidal freshwater swamps outside the US is generally much lower (with the notable exception of Amazonian hardwood tidal várzea), as are basal area values.

Georgia;Florida;South Carolina↗
USGS2017

The Bayesian group lasso for confounded spatial data

Generalized linear mixed models for spatial processes are widely used in applied statistics. In many applications of the spatial generalized linear mixed model (SGLMM), the goal is to obtain inference about regression coefficients while achieving optimal predictive ability. When implementing the SGLMM, multicollinearity among covariates and the spatial random effects can make computation challenging and influence inference. We present a Bayesian group lasso prior with a single tuning parameter that can be chosen to optimize predictive ability of the SGLMM and jointly regularize the regression coefficients and spatial random effect. We implement the group lasso SGLMM using efficient Markov chain Monte Carlo (MCMC) algorithms and demonstrate how multicollinearity among covariates and the spatial random effect can be monitored as a derived quantity. To test our method, we compared several parameterizations of the SGLMM using simulated data and two examples from plant ecology and disease ecology. In all examples, problematic levels multicollinearity occurred and influenced sampling efficiency and inference. We found that the group lasso prior resulted in roughly twice the effective sample size for MCMC samples of regression coefficients and can have higher and less variable predictive accuracy based on out-of-sample data when compared to the standard SGLMM.

Journal of Agricultural, Biological, and Environme↗
USGS2009

Age-distribution estimation for karst groundwater: Issues of parameterization and complexity in inverse modeling by convolution

Convolution modeling is useful for investigating the temporal distribution of groundwater age based on environmental tracers. The framework of a quasi-transient convolution model that is applicable to two-domain flow in karst aquifers is presented. The model was designed to provide an acceptable level of statistical confidence in parameter estimates when only chlorofluorocarbon (CFC) and tritium ( 3 H) data are available. We show how inverse modeling and uncertainty assessment can be used to constrain model parameterization to a level warranted by available data while allowing major aspects of the flow system to be examined. As an example, the model was applied to water from a pumped well open to the Madison aquifer in central USA with input functions of CFC-11, CFC-12, CFC-113, and 3 H, and was calibrated to several samples collected during a 16-year period. A bimodal age distribution was modeled to represent quick and slow flow less than 50 years old. The effects of pumping and hydraulic head on the relative volumetric fractions of these domains were found to be influential factors for transient flow. Quick flow and slow flow were estimated to be distributed mainly within the age ranges of 0-2 and 26-41 years, respectively. The fraction of long-term flow (>50 years) was estimated but was not dateable. The different tracers had different degrees of influence on parameter estimation and uncertainty assessments, where 3 H was the most critical, and CFC-113 was least influential.

Journal of Hydrology↗
USGS2000

Stress resistance strategy in an arid land shrub: interactions between developmental instability and fractal dimention

This paper investigates allocation of energy to mechanisms that generate and preserve architectural forms (i.e. developmental stability, complexity of branching patterns) and productivity (growth and reproduction) in response to environmental disturbances (i.e. grazing and resource availability). The statistical error in translational symmetry was used to detect random intra-individual variability during development. This can be thought of as a measure of developmental instability caused by stress. Additionally, we use changes in fractal complexity and shoot distribution of branch structures as an alternate indicator of stress. These methods were applied to Anthyllis cytisoides L., a semi-arid environment shrub, to ascertain the effect of grazing and slope exposure on developmental traits in a 2×2 factorial design. The results show that A. cytisoides maintains developmental stability at the expense of productivity. Anthyllis cytisoides was developmentally more stable when grazed and when on south-facing, as opposed to north-facing slopes. On the contrary, shoot length, leaf area, fractal dimension and reproductive-to-vegetative allocation ratio were larger in north- than in south-facing slopes. As a consequence, under extreme xeric conditions, shrub mortality increased in north-facing slopes, especially when not grazed. The removal of transpiring area and the reduction of plant competition favoured developmental stability and survival in grazed plants. Differences between grazed and ungrazed plants were most evident in more mesic (north-facing) areas.

Journal of Fish Diseases↗
USGS2014

A bootstrap estimation scheme for chemical compositional data with nondetects

The bootstrap method is commonly used to estimate the distribution of estimators and their associated uncertainty when explicit analytic expressions are not available or are difficult to obtain. It has been widely applied in environmental and geochemical studies, where the data generated often represent parts of whole, typically chemical concentrations. This kind of constrained data is generically called compositional data, and they require specialised statistical methods to properly account for their particular covariance structure. On the other hand, it is not unusual in practice that those data contain labels denoting nondetects, that is, concentrations falling below detection limits. Nondetects impede the implementation of the bootstrap and represent an additional source of uncertainty that must be taken into account. In this work, a bootstrap scheme is devised that handles nondetects by adding an imputation step within the resampling process and conveniently propagates their associated uncertainly. In doing so, it considers the constrained relationships between chemical concentrations originated from their compositional nature. Bootstrap estimates using a range of imputation methods, including new stochastic proposals, are compared across scenarios of increasing difficulty. They are formulated to meet compositional principles following the log-ratio approach, and an adjustment is introduced in the multivariate case to deal with nonclosed samples. Results suggest that nondetect bootstrap based on model-based imputation is generally preferable. A robust approach based on isometric log-ratio transformations appears to be particularly suited in this context. Computer routines in the R statistical programming language are provided.

Journal of Chemometrics↗
USGS2023

Identification of seismo-volcanic regimes at Whakaari/White Island (New Zealand) via systematic tuning of an unsupervised classifier

We present an algorithm based on Self-Organizing Maps (SOM) and k-means clustering to recognize patterns in a continuous 12.5-year tremor time series recorded at Whakaari/White Island volcano, New Zealand (hereafter referred to as Whakaari). The approach is extendable to a variety of volcanic settings through systematic tuning of the classifier. Hyperparameters are evaluated by statistical means, yielding a combination of “ideal” SOM parameters for the given data set. Extending from this, we applied a Kernel Density Estimation approach to automatically detect changes within the observed seismicity. We categorize the Whakaari seismic time series into regimes representing distinct volcano-seismic states during recent unrest episodes at Whakaari (2012/2013, 2016, and 2019). There is a clear separation in classification results between background regimes and those representing elevated levels of unrest. Onset of unrest is detected by the classifier 6 weeks before the August 2012 eruption, and ca. 3.5 months before the December 2019 eruption, respectively. Regime changes are corroborated by changes in commonly monitored tremor proxies as well as with reported volcanic activity. The regimes are hypothesized to represent diverse mechanisms including: system pressurization and depressurization, degassing, and elevated surface activity. Labeling these regimes improves visualization of the 2012/2013 and 2019 unrest and eruptive episodes. The pre-eruptive 2016 unrest showed a contrasting shape and nature of seismic regimes, suggesting differing onset and driving processes. The 2016 episode is proposed to result from rapid destabilization of the shallow hydrothermal system, while rising magmatic gases from new injections of magma better explain the 2012/2013 and 2019 episodes.

Whakaari, White Island↗
USGS2013

Potential climate change impacts on temperate forest ecosystem processes

Large changes in atmospheric CO2, temperature and precipitation are predicted by 2100, yet the long-term consequences for carbon, water, and nitrogen cycling in forests are poorly understood. We applied the PnET-CN ecosystem model to compare the long-term effects of changing climate and atmospheric CO2 on productivity, evapotranspiration, runoff, and net nitrogen mineralization in current Great Lakes forest types. We used two statistically downscaled climate projections, PCM B1 (warmer and wetter) and GFDL A1FI (hotter and drier), to represent two potential future climate and atmospheric CO2 scenarios. To separate the effects of climate and CO2, we ran PnET-CN including and excluding the CO2 routine. Our results suggest that, with rising CO2 and without changes in forest type, average regional productivity could increase from 67% to 142%, changes in evapotranspiration could range from –3% to +6%, runoff could increase from 2% to 22%, and net N mineralization could increase 10% to 12%. Ecosystem responses varied geographically and by forest type. Increased productivity was almost entirely driven by CO2 fertilization effects, rather than by temperature or precipitation (model runs holding CO2 constant showed stable or declining productivity). The relative importance of edaphic and climatic spatial drivers of productivity varied over time, suggesting that productivity in Great Lakes forests may switch from being temperature to water limited by the end of the century.

Michigan;Minnesota;Wisconsin↗