Geology topics

Jason S. McLachlan

Publications and source records attributed to Jason S. McLachlan.

11 recordsLinked to original sources

USGS2024

Near-term ecological forecasting for climate change action

A substantial increase in predictive capacity is needed to anticipate and mitigate the widespread change in ecosystems and their services in the face of climate and biodiversity crises. In this era of accelerating change, we cannot rely on historical patterns or focus primarily on long-term projections that extend decades into the future. In this Perspective, we discuss the potential of near-term (daily to decadal) iterative ecological forecasting to improve decision-making on actionable time frames. We summarize the current status of ecological forecasting and focus on how to scale up, build on lessons from weather forecasting, and take advantage of recent technological advances. We also highlight the need to focus on equity, workforce development, and broad cross-disciplinary and non-academic partnerships.

Nature Climate Change↗

USGS2023

The NEON Ecological Forecasting Challenge

The 21st century continues to be characterized by major changes to the environment and the ecosystem services upon which society depends. Anticipating and responding to these changes requires that scientists explicitly forecast future conditions in real time (Dietze et al . 2018 ). Ecological forecasting, like weather and epidemiological forecasting, involves integrating data and models to generate quantitative predictions of the future state of ecological systems before observations are collected. The iterative cycle of creating forecasts, evaluating them with new observations, updating the models, and then making new forecasts has the potential to accelerate learning across many ecological subdisciplines. This cycle builds on openly available data, often published soon after collection, as is increasingly common in ecological observatory networks, such as the National Ecological Observatory Network (NEON). To accelerate improvements in ecological forecasting, we designed and launched the NEON Ecological Forecasting Challenge (hereafter, “Challenge”) (Figure 1 ), an open platform for the ecological and data science communities to forecast NEON data before they are collected.

Frontiers in Ecology and the Environment↗

USGS2020

Forest responses to last-millennium hydroclimate variability are governed by spatial variations in ecosystem sensitivity

Forecasts of future forest change are governed by ecosystem sensitivity to climate change, but ecosystem model projections are under-constrained by data at multidecadal and longer timescales. Here, we quantify ecosystem sensitivity to centennial-scale hydroclimate variability, by comparing dendroclimatic and pollen-inferred reconstructions of drought, forest composition and biomass for the last millennium with five ecosystem model simulations. In both observations and models, spatial patterns in ecosystem responses to hydroclimate variability are strongly governed by ecosystem sensitivity rather than climate exposure. Ecosystem sensitivity was higher in models than observations and highest in simpler models. Model-data comparisons suggest that interactions among biodiversity, demography and ecophysiology processes dampen the sensitivity of forest composition and biomass to climate variability and change. Integrating ecosystem models with observations from timescales extending beyond the instrumental record can better understand and forecast the mechanisms regulating forest sensitivity to climate variability in a complex and changing world.

Ecology Letters↗

USGS2020

Comparison of settlement-era vegetation reconstructions for STEPPS and REVEALS pollen–vegetation models in the northeastern United States

Reconstructions of prehistoric vegetation composition help establish natural baselines, variability, and trajectories of forest dynamics before and during the emergence of intensive anthropogenic land use. Pollen–vegetation models (PVMs) enable such reconstructions from fossil pollen assemblages using process-based representations of taxon-specific pollen production and dispersal. However, several PVMs and variants now exist, and the sensitivity of vegetation inferences to PVM selection, variant, and calibration domain is poorly understood. Here, we compare the reconstructions, parameter estimates, and structure of a Bayesian hierarchical PVM, STEPPS, both to observations and to REVEALS, a widely used PVM, for the pre–Euro-American settlement-era vegetation in the northeastern United States (NEUS). We also compare NEUS-based STEPPS parameter estimates to those for the upper midwestern United States (UMW). Both PVMs predict the observed macroscale patterns of vegetation composition in the NEUS; however, reconstructions of minor taxa are less accurate and predictions for some taxa differ between PVMs. These differences can be attributed to intermodel differences in structure and parameter estimates. Estimates of pollen productivity from STEPPS broadly agree with estimates produced for use in REVEALS, while comparison between pollen dispersal parameter estimates shows no significant relationship. STEPPS parameter estimates are similar between the UMW and NEUS, suggesting that STEPPS parameter estimates are transferable between floristically similar regions and scales.

Connecticut, Maine, Massachusetts, New Hampshire, ↗

USGS2019

Quantifying trends and uncertainty in prehistoric forest composition

Forest ecosystems in eastern North America were in flux over the last several thousand years, well before Euro-American land clearance and the 20th-century onset of anthropogenic climate change. However, the magnitude and uncertainty of prehistoric vegetation change have been difficult to quantify because of the multiple ecological, dispersal, and sedimentary processes that govern the relationship between forest composition and fossil pollen assemblages. Here we extend STEPPS, a Bayesian hierarchical spatio-temporal pollen-vegetation model, to estimate changes in forest composition in the upper Midwestern United States from about 2000 to 200 years ago. Using this approach, we identify areas of statistically and ecologically significant change. Between 2000 and 200 years ago, forest composition significantly changed across broad regions of north-central Wisconsin and Minnesota. Rates of compositional change varied spatially, and can be linked to previously reported events. The single largest change is the infilling of Tsuga canadensis in northern Wisconsin over the past 2000 years. Despite this range in-filling, the range limit of T. canadensis was largely stable, with modest expansion westward. The regional ecotone between temperate hardwood forests and northern mixed hardwood/conifer forests shifted southwestward by 15-20 km in Minnesota and Northwestern Wisconsin. Fraxinus, Ulmus, and other mesic hardwoods expanded in the Big Woods region of southern Minnesota. However, some areas showed no significant change, suggesting high complexity in the spatiotemporal patterns of past forest dynamics. The increasing density of paleoecological data networks and advances in statistical modeling approaches now enables the confident detection of subtle but significant changes in forest composition over the last 2000 years.

Minnesota, Wisconsin↗

USGS2019

Sharp savanna-forest transitions in the Midwest followed environmental gradients but are absent from the modern landscape

Historically, closed eastern forests transitioned into open savannas and prairies in the US Midwest, but this transition is poorly understood. To investigate the eastern boundary of the prairie-forest ecotone, we conducted a case study of historic and modern vegetation patterns of the Yellow River watershed in northwest Indiana. Historic vegetation came from the Public Land Survey notes collected in the early 1800s, whereas modern vegetation came from the Forest Inventory Analysis and USGS National Land Cover Database. We mapped historical survey vegetation data using GIS to reconstruct the region’s past and current forest composition and structure. We also mapped climate, topography, and soil composition across the watershed to investigate the relationship between historic vegetation and environmental gradients. We found a sharp transition in the presettlement forest structure and composition, with dense deciduous forests in the eastern portion of our study area and open oak savannas in the west. The savanna ecosystem dominated in sandy well-drained soils and was at a slightly lower elevation than the adjacent closed forest. Modest environmental changes accompanied major vegetation changes in the past, which might suggest fire and hydrological patterns helped maintain the sharp ecotone. By contrast, the modern forest shows no difference in tree density and composition across the watershed, which is consistent with major land use and hydrology changes in the watershed since settlement. On the modern landscape, land that was historically closed forest now has higher agricultural productivity compared to land that was historically savanna, whereas the historic savanna currently supports more mesic forest. These results suggest the environmental gradient continues to subtly shape the landscape. Though land use change has largely removed the closed mixed hardwood forests and oak savannas from this area, a better understanding of the historic vegetation and the conditions that supported it can help inform land management and restoration, as well as reveal ecological processes that drive vegetation transitions.

Indiana↗

USGS2017

A model-based approach to wildland fire reconstruction using sediment charcoal records

Lake sediment charcoal records are used in paleoecological analyses to reconstruct fire history, including the identification of past wildland fires. One challenge of applying sediment charcoal records to infer fire history is the separation of charcoal associated with local fire occurrence and charcoal originating from regional fire activity. Despite a variety of methods to identify local fires from sediment charcoal records, an integrated statistical framework for fire reconstruction is lacking. We develop a Bayesian point process model to estimate the probability of fire associated with charcoal counts from individual-lake sediments and estimate mean fire return intervals. A multivariate extension of the model combines records from multiple lakes to reduce uncertainty in local fire identification and estimate a regional mean fire return interval. The univariate and multivariate models are applied to 13 lakes in the Yukon Flats region of Alaska. Both models resulted in similar mean fire return intervals (100–350 years) with reduced uncertainty under the multivariate model due to improved estimation of regional charcoal deposition. The point process model offers an integrated statistical framework for paleofire reconstruction and extends existing methods to infer regional fire history from multiple lake records with uncertainty following directly from posterior distributions.

Environmetrics↗

USGS2017

Climatic history of the northeastern United States during the past 3000 years

Many ecosystem processes that influence Earth system feedbacks, including vegetation growth, water and nutrient cycling, and disturbance regimes, are strongly influenced by multi-decadal to millennial-scale variations in climate that cannot be captured by instrumental climate observations. Paleoclimate information is therefore essential for understanding contemporary ecosystems and their potential trajectories under a variety of future climate conditions. With the exception of fossil pollen records, there are a limited number of northeastern US (NE US) paleoclimate archives that can provide constraints on its temperature and hydroclimate history. Moreover, the records that do exist have not been considered together. Tree-ring data indicate that the 20th century was one of the wettest of the past 500 years in the eastern US (Pederson et al., 2014), and lake-level records suggest it was one of the wettest in the Holocene (Newby et al., 2014); how such results compare with other available data remains unclear, however. Here we conduct a systematic review, assessment, and comparison of paleotemperature and paleohydrological proxies from the NE US for the last 3000 years. Regional temperature reconstructions are consistent with the long-term cooling trend (1000 BCE–1700 CE) evident in hemispheric-scale reconstructions, but hydroclimate reconstructions reveal new information, including an abrupt transition from wet to dry conditions around 550–750 CE. NE US paleo data suggest that conditions during the Medieval Climate Anomaly were warmer and drier than during the Little Ice Age, and drier than today. There is some evidence for an acceleration over the past century of a longer-term wetting trend in the NE US, and coupled with the abrupt shift from a cooling trend to a warming trend from increased greenhouse gases, may have wide-ranging implications for species distributions, ecosystem dynamics, and extreme weather events. More work is needed to gather paleoclimate data in the NE US, make inter-proxy comparisons, and improve estimates of uncertainty in the reconstructions.

Climate of the Past↗

USGS2016

Novel and lost forests in the Upper Midwestern United States, from new estimates of settlement-era composition, stem density, and biomass

EuroAmerican land-use and its legacies have transformed forest structure and composition across the United States (US). More accurate reconstructions of historical states are critical to understanding the processes governing past, current, and future forest dynamics. Here we present new gridded (8x8km) reconstructions of pre-settlement (1800s) forest composition and structure from the upper Midwestern US (Minnesota, Wisconsin, and most of Michigan), using 19th Century Public Land Survey System (PLSS), with estimates of relative composition, above-ground biomass, stem density, and basal area for 28 tree types. This mapping is more robust than past efforts, using spatially varying correction factors to accommodate sampling design, azimuthal censoring, and biases in tree selection.

Michigan, Minnesota Wisconsin↗

USGS2016

The effects of anthropogenic land cover change on pollen-vegetation relationships in the American Midwest

Fossil pollen assemblages provide information about vegetation dynamics at time scales ranging from centuries to millennia. Pollen-vegetation models and process-based models of dispersal typically assume stable relationships between source vegetation and corresponding pollen in surface sediments, as well as stable parameterizations of dispersal and productivity. These assumptions, however, are largely unevaluated. This paper reports a test of the stability of pollen-vegetation relationships using vegetation and pollen data from the Midwestern region of the United States, during a period of large changes in land use and vegetation driven by Euro-American settlement. We compared a dataset of pollen records for the early settlement-era with three other datasets of pollen and forest composition for two time periods: before Euro-American settlement, and the late 20th century. Results from generalized linear models for thirteen genera indicate that pollen-vegetation relationships significantly differ (p < 0.05) between pre-settlement and the modern era for several genera: Fagus, Betula, Tsuga, Quercus, Pinus , and Picea . The estimated pollen source radius for the 8 km gridded vegetation data and associated pollen data is 25–85 km, consistent with prior studies using similar methods and spatial resolutions. Hence, the rapid changes in land cover associated with the Anthropocene affect the accuracy of ecological predictions for both the future and the past. In the Anthropocene, paleoecology should move beyond the assumption that pollen-vegetation relationships are stable over time. Multi-temporal calibration datasets are increasingly possible and enable paleoecologists to better understand the complex processes governing pollen-vegetation relationships through space and time.

Michigan, Minnesota, Wisconsin↗

USGS2016

Quantifying pollen-vegetation relationships to reconstruct ancient forests using 19th-century forest composition and pollen data

Mitigation of climate change and adaptation to its effects relies partly on how effectively land-atmosphere interactions can be quantified. Quantifying composition of past forest ecosystems can help understand processes governing forest dynamics in a changing world. Fossil pollen data provide information about past forest composition, but rigorous interpretation requires development of pollen-vegetation models (PVMs) that account for interspecific differences in pollen production and dispersal. Widespread and intensified land-use over the 19th and 20th centuries may have altered pollen-vegetation relationships. Here we use STEPPS, a Bayesian hierarchical spatial PVM, to estimate key process parameters and associated uncertainties in the pollen-vegetation relationship. We apply alternate dispersal kernels, and calibrate STEPPS using a newly developed Euro-American settlement-era calibration data set constructed from Public Land Survey data and fossil pollen samples matched to the settlement-era using expert elicitation. Models based on the inverse power-law dispersal kernel outperformed those based on the Gaussian dispersal kernel, indicating that pollen dispersal kernels are fat tailed. Pine and birch have the highest pollen productivities. Pollen productivity and dispersal estimates are generally consistent with previous understanding from modern data sets, although source area estimates are larger. Tests of model predictions demonstrate the ability of STEPPS to predict regional compositional patterns.

Quaternary Science Reviews↗