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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.

A primary focus of historical biogeography is to understand changes in species ranges, abundance and genetic connectivity, and changes in community composition. Traditionally, biogeographic inference has relied on distinct lines of evidence, including DNA sequences, fossils and hindcasted ecological niche models. In this review we propose that the development of integrative modeling approaches that leverage multiple distinct data types from diverse disciplines has the potential to revolutionize the field of biogeography. Although each data type contains information on a distinct aspect of species’ biogeographic histories, few studies formally integrate multiple types in analysis. For example, post hoc congruence among analyses based on different data types (e.g. fossils and genetics) is commonly assumed to indicate likely biogeographic histories. Unfortunately, analyses of different data often reach discordant conclusions. Thus, fundamental and unresolved debates continue regarding speed and timing of postglacial migration, location and size of glacial refugia, and degree of long distance dispersal. Formal statistical integration can help address these issues. More specifically, formal integration can leverage all available evidence, account for inherent biases associated with different data types, and quantify data and process uncertainty. Novel, quantitative integration of data and models across fields is now possible due to recent advances in cyberinfrastructure, spatial modeling, online and aggregated ecological databases, data processing and quantitative methods. Our purpose is to make the case for and give examples of rigorous integration of genetic, fossil and environmental/occurrence data for inferring biogeographic history. In particular, we 1) review the need for such a framework; 2) explain common data types and approaches used to infer biogeographic history (and the challenges with each); 3) review state‐of‐the‐art examples of data integration in biogeography; 4) lay out a series of novel, suggested improvements on current methods; and 5) provide an outlook on technical feasibility and future opportunities.

Interactions between drought and insect defoliation may dramatically alter forest function under novel climate and disturbance regimes, but remain poorly understood. We empirically tested two important hypotheses regarding tree responses to drought and insect defoliation: (a) trees exhibit delayed, persistent, and cumulative growth responses to these stressors; (b) physiological feedbacks in tree responses to these stressors exacerbate their impacts on tree growth. These hypotheses remain largely untested at a landscape scale, yet are critical for predicting forest function under novel future conditions, given the connection between tree growth and demographic processes such as mortality and regeneration. We developed a Bayesian hierarchical model to quantify the ecological memory of tree growth to past water deficits and insect defoliation events, derive antecedent variables reflecting the persistent and cumulative effects of these stressors on current growth, and test for their interactive effects. The model was applied to extensive tree growth, weather, and defoliation survey data from western and eastern regions of the Canadian boreal forest impacted by recent drought and defoliation events and characterized by contrasting tree compositions, climates, and insect defoliators. Results revealed persistent negative tree growth responses to past water (all trees) and defoliation (host trees) stress lasting 3–6 and 10–12 years, respectively, depending on study region. Accounting for the ecological memory of tree growth to water and defoliation stress allowed for detection of interactions not previously demonstrated. Contrary to expectations, we found evidence for positive interactions among non‐host trees likely due to reduced water stress following defoliation events. Regional differences in ecological memory to water stress highlight the role of climate in shaping forest responses to drought. Synthesis. Study results suggest negative feedbacks in tree responses to drought and insect attack may be weaker than predicted for defoliator‐dominated boreal forest systems. Instead, insect defoliation may offset the impacts of water deficit on boreal tree growth by reducing transpirational water demand. This offset mimics increased resistance to drought following forest thinning and may lessen growth and mortality losses due to increased aridity and more severe insect damage forecast for the boreal forest under global change.

We present gridded 8 km-resolution data products of the estimated stem density, basal area, and biomass of tree taxa at Euro-American settlement of the midwestern United States during the middle to late 19th century for the states of Minnesota, Wisconsin, Michigan, Illinois, and Indiana. The data come from settlement-era Public Land Survey (PLS) data (ca. 0.8-km resolution) of trees recorded by land surveyors. The surveyor notes have been transcribed, cleaned, and processed to estimate stem density, basal area, and biomass at individual points. The point-level data are aggregated within 8 km grid cells and smoothed using a generalized additive statistical model that accounts for zero-inflated continuous data and provides approximate Bayesian uncertainty estimates. The statistical modeling smooths out sharp spatial features (likely arising from statistical noise) within areas smaller than about 200 km2. Based on this modeling, presettlement Midwestern landscapes supported multiple dominant species, vegetation types, forest types, and ecological formations. The prairies, oak savannas, and forests each had distinctive structures and spatial distributions across the domain. Forest structure varied from savanna (averaging 27 Mg/ha biomass) to northern hardwood (104 Mg/ha) and mesic southern forests (211 Mg/ha). The presettlement forests were neither unbroken and massively-statured nor dominated by young forests constantly structured by broad-scale disturbances such as fire, drought, insect outbreaks, or hurricanes. Most forests were structurally between modern second growth and old growth. We expect the data product to be useful as a baseline for investigating how forest ecosystems have changed in response to the last several centuries of climate change and intensive Euro-American land use and as a calibration dataset for paleoecological proxy-based reconstructions of forest composition and structure for earlier time periods. The data products (including raw and smoothed estimates at the 8-km scale) are available at the LTER Network Data Portal as version 1.0.

This research has been supported by the Directorate for Geosciences of the National Science Foundation (grant nos. AGS-1602105 and AGS-1903548)., We thank the USGS John Wesley Powell Center for Analysis and Synthesis, which hosted a meeting that led to this synthesis effort. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US government. We thank the original data generators who made their data available for reuse, and we acknowledge the data repositories for safeguarding these assets., Holocene climate reconstructions are useful for understanding the diverse features and spatial heterogeneity of past and future climate change. Here we present a database of western North American Holocene paleoclimate records. The database gathers paleoclimate time series from 184 terrestrial and marine sites, including 381 individual proxy records. The records span at least 4000 of the last 12 000 years (median duration of 10 725 years) and have been screened for resolution, chronologic control, and climate sensitivity. Records were included that reflect temperature, hydroclimate, or circulation features. The database is shared in the machine readable Linked Paleo Data (LiPD) format and includes geochronologic data for generating site-level time-uncertain ensembles. This publicly accessible and curated collection of proxy paleoclimate records will have wide research applications, including, for example, investigations of the primary features of oceanatmospheric circulation along the eastern margin of the North Pacific and the latitudinal response of climate to orbital changes. The database is available for download at https://doi.org/10.6084/m9.figshare.12863843.v1 (Routson and McKay, 2020)., National Science Foundation (NSF) AGS-1602105 AGS-1903548

A comprehensive database of paleoclimate records is needed to place recent warming into the longer-term context of natural climate variability. We present a global compilation of quality-controlled, published, temperature-sensitive proxy records extending back 12,000 years through the Holocene. Data were compiled from 679 sites where time series cover at least 4000 years, are resolved at sub-millennial scale (median spacing of 400 years or finer) and have at least one age control point every 3000 years, with cut-off values slackened in data-sparse regions. The data derive from lake sediment (51%), marine sediment (31%), peat (11%), glacier ice (3%), and other natural archives. The database contains 1319 records, including 157 from the Southern Hemisphere. The multi-proxy database comprises paleotemperature time series based on ecological assemblages, as well as biophysical and geochemical indicators that reflect mean annual or seasonal temperatures, as encoded in the database. This database can be used to reconstruct the spatiotemporal evolution of Holocene temperature at global to regional scales, and is publicly available in Linked Paleo Data (LiPD) format. Fil: Kaufman, Darrell. Northern Arizona University.; Estados Unidos Fil: McKay, Nicholas. Northern Arizona University.; Estados Unidos Fil: Routson, Cody. Northern Arizona University.; Estados Unidos Fil: Erb, Michael. Northern Arizona University.; Estados Unidos Fil: Davis, Basil. University Of Lausanne; Suiza Fil: Heiri, Oliver. University Of Basel; Suiza Fil: Jaccard, Samuel. University Of Bern; Suiza Fil: Tierney, Jessica. University of Arizona; Estados Unidos Fil: Dätwyler, Christoph. University Of Bern; Suiza Fil: Axford, Yarrow. Northwestern University; Estados Unidos Fil: Brussel, Thomas. University of Utah; Estados Unidos Fil: Cartapanis, Olivier. University Of Bern; Suiza Fil: Chase, Brian. Universite de Montpellier; Francia Fil: Dawson, Andria. Mount Royal University; Canadá Fil: de Vernal, Anne. Université du Québec a Montreal; Canadá Fil: Engels, Stefan. University of London; Reino Unido Fil: Jonkers, Lukas. University Of Bremen; Alemania Fil: Marsicek, Jeremiah. University of Wisconsin-Madison; Estados Unidos Fil: Moffa Sánchez, Paola. University of Durham; Reino Unido Fil: Morrill, Carrie. University of Colorado; Estados Unidos Fil: Orsi, Anais. Université Paris-Saclay; Francia Fil: Rehfeld, Kira. Heidelberg University; Alemania Fil: Saunders, Krystyna. Australian Nuclear Science And Technology Organisation; Australia Fil: Sommer, Philipp. University Of Lausanne; Suiza Fil: Thomas, Elizabeth. University At Buffalo; Estados Unidos Fil: Tonello, Marcela Sandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; Argentina Fil: Tóth, Mónika. Balaton Limnological Institute; Hungría Fil: Vachula, Richard. Brown University; Estados Unidos Fil: Andreev, Andrei. Alfred Wegener Institut Helmholtz Centre for Polar and Marine Research; Alemania Fil: Bertrand, Sebastien. Ghent University; Bélgica Fil: Massaferro, Julieta. Administración de Parques Nacionales. Parque Nacional "Nahuel Huapi"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina

Holocene climate reconstructions are useful for understanding the diverse features and spatial heterogeneity of past and future climate change. Here we present a database of western North American Holocene paleoclimate records. The database gathers paleoclimate time series from 209 terrestrial and marine sites, including 382 individual proxy records. The records span at least 4000 of the last 12 000 years (median duration = 10 603 years), and have been screened for resolution, chronologic control, and climate sensitivity. Records were included that reflect temperature, hydroclimate, or circulation features. The database is shared in the machine readable Linked Paleo Data (LiPD) format and includes geochronologic data for generating site-level time-uncertain ensembles. This publicly accessible and curated collection of proxy paleoclimate records will have wide research applications, including, for example, investigations of the primary features of ocean-atmospheric circulation along the eastern margin of the North Pacific and the latitudinal response of climate to orbital changes. The database is available for download at: https://doi.org/10.6084/m9.figshare.12863843.v1 (Routson and McKay, 2020).

A comprehensive database of paleoclimate records is needed to place recent warming into the longer-term context of natural climate variability. We present a global compilation of quality-controlled, published, temperature-sensitive proxy records extending back 12,000 years through the Holocene. Data were compiled from 679 sites where time series cover at least 4000 years, are resolved at sub-millennial scale (median spacing of 400 years or finer) and have at least one age control point every 3000 years, with cut-off values slackened in data-sparse regions. The data derive from lake sediment (51%), marine sediment (31%), peat (11%), glacier ice (3%), and other natural archives. The database contains 1319 records, including 157 from the Southern Hemisphere. The multi-proxy database comprises paleotemperature time series based on ecological assemblages, as well as biophysical and geochemical indicators that reflect mean annual or seasonal temperatures, as encoded in the database. This database can be used to reconstruct the spatiotemporal evolution of Holocene temperature at global to regional scales, and is publicly available in Linked Paleo Data (LiPD) format., Measurement(s)climateTechnology Type(s)digital curationFactor Type(s)temporal interval • geographic location • proxy typeSample Characteristic - Environmentclimate systemSample Characteristic - LocationEarth (planet) Machine-accessible metadata file describing the reported data: https://www.ncdc.noaa.gov/paleo/study/27330

Underestimation of uncertainty in ecology runs the risk of producing precise, but inaccurate predictions. Most predictions from ecological models account for only a subset of the various components of uncertainty, making it diffcult to determine which uncertainties drive inaccurate predictions. To address this issue, we leveraged the forecast-analysis cycle and created a new state data assimilation algorithm that accommodates non-normal datasets and incorporates a commonly left-out uncertainty, process error covariance. We evaluated this novel algorithm with a case study where we assimilated 50 years of tree-ring-estimated aboveground biomass data into a forest gap model. To test assumptions about which uncertainties dominate forecasts of forest community and carbon dynamics, we partitioned hindcast variance into five uncertainty components. Contrary to the assumption that demographic stochasticity dominates forest gap dynamics, we found that demographic stochasticity alone massively underestimated forecast uncertainty (0.09% of the total uncertainty) and resulted in overconfident, biased model predictions. Similarly, despite decades of reliance on unconstrained “spin-ups” to initialize models, initial condition uncertainty declined very little over the forecast period and constraining initial conditions with data led to large increases in prediction accuracy. Process uncertainty, which up until now had been diffcult to estimate in mechanistic ecosystem model projections, dominated the prediction uncertainty over the forecast time period (49.1%), followed by meteorological uncertainty (32.5%). Parameter uncertainty, a recent focus of the modeling community, contributed 18.3%. These findings call into question our conventional wisdom about how to improve forest community and carbon cycle projections. This foundation can be used to test long standing modeling assumptions across fields in global change biology and specifically challenges the conventional wisdom regarding which aspects dominate uncertainty in the forest gap models.

Forecasts of forest responses to climate variability are governed by climate exposure and ecosystem sensitivity, but ecosystem model projections and process representations are under-constrained by data at multidecadal and longer timescales. Here, we assess 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 highest in simpler models and higher than observations, suggesting that interactions among biodiversity, demography, and ecophysiology processes dampen the sensitivity of forest composition and biomass to climate variability and change. By integrating ecosystem models with observations from timescales extending beyond the instrumental record, we can better understand and forecast the mechanisms regulating forest sensitivity to climate variability in a complex and changing world.

The question of whether prehistoric human impacts on land cover (i.e. anthropogenic land cover change due to land use, LULC) were sufficiently large to have a major impact on regional cli-mates is still a matter of debate. Climate model simulations have shown that LULC datasets can have large regional impacts on climate in recent and prehistoric time (1). But there are major differences between the available LULC scenarios/datasets such as HYDE (History Database of the Global En-vironment) and Kaplan’s KK10 (2), and diagnoses of inferred carbon-cycle impacts show that none of the scenarios are realistic (3). The only way to provide a useful assessment of the potential for LULC changes to affect climate in the past, is to provide more realistic LULC data based on palaeovegetation and archaeological evidence to improve the LULC datasets used in climate modelling(4). We use the REVEALS model to reconstruct LC from pollen data at a regional scale, and archaeological data to map LU types and distribution, and estimate per capita LU. The archaeology-based LU maps and per-capita LU estimates are used to improve LULC datasets. Pollen-based REVEALS LC estimates are then used to evaluate/validate the new, improved LULC datasets. These new datasets will be used to implement past land use in palaeoclimate and carbon cycle model simulations. Such simulations are necessary to assess the impact of LULC changes in the past and understand the effect of ecosys-tem management on future climate. We present results from five years of PAGES LandCover6k activities. (1) Strandberg G, Kjellström E, Poska A, Wagner S, Gaillard M-J et al. (2014) Regional climate model sim-ulations for Europe at 6 and 0.2 k BP: sensitivity to changes in anthropogenic deforestation. Clim. Past 10, 661–680.(2) Gaillard M-J, Sugita S, Mazier F et al (2010) Holocene land-cover reconstructions for studies on land cover-climate feedbacks. Clim. Past 6, 483-499.(3) Stocker B, Yud Z, Massae C, Joos F (2017) Holocene peatland and ice-core data constraints on the tim-ing and magnitude of CO2 emissions from past land use. www.pnas.org/cgi/doi/10.1073/ pnas.1613889114.(4) Harrison S P, Gaillard M-J, Stocker B D, Vander Linden M, Klein Goldewijk K, Boles O, Braconnot P, Dawson A, Fluet-Chouinard E, Kaplan J O, Kastner T, Pausata F S R, Robinson E, Whitehouse N J, Madella M, and Morrison K D (2019) Development and testing of scenarios for implementing Holocene LULC in Earth Sys-tem Model Experiments, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-125, in review, 2019.

Fossil pollen records are well-established indicators of past vegetation changes. The prevalence of pollen across environmental settings including lakes, wetlands, and marine sediments, has made palynology one of the most ubiquitous and valuable tools for studying past environmental and climatic change globally for decades. A complementary research focus has been the development of statistical techniques to derive quantitative estimates of climatic conditions from pollen assemblages. This paper reviews the most commonly used statistical techniques and their rationale and seeks to provide a resource to facilitate their inclusion in more palaeoclimatic research. To this end, we first address the fundamental aspects of fossil pollen data that should be considered when undertaking pollen-based climate reconstructions. We then introduce the range of techniques currently available, the history of their development, and the situations in which they can be best employed. We reviewthe literature on how to define robust calibration datasets, produce high-quality reconstructions, and evaluate climate reconstructions, and suggest methods and products that could be developed to facilitate accessibility and global usability. To continue to foster the development and inclusion of pollen climate reconstruction methods, we promote the development of reporting standards. When established, such standards should 1) enable broader application of climate reconstruction techniques, especially in regions where such methods are currently underused, and 2) enable the evaluation and reproduction of individual reconstructions, structuring them for the evolving open-science era, and optimising the use of fossil pollen data as a vital means for the study of past environmental and climatic variability. We also strongly encourage developers and users of palaeoclimate reconstruction methodologies to make associated programming code publicly available, which will further help disseminate these techniques to interested communities.

We present gridded 8 km-resolution data products of the estimated biomass, basal area, and stem density of tree taxa at the time of Euro-American settlement of the midwestern United States for the states of Minnesota, Wisconsin, Michigan, Illinois, and Indiana. The data come from settlement-era Public Land Survey (PLS) data (ca. 0.8-km resolution) of trees recorded by land surveyors. The surveyor notes have been transcribed, cleaned, and processed to estimate biomass, basal area, and stem density at individual points on the landscape. The point-level data are then aggregated within grid cells and statistically smoothed using a statistical model that accounts for zero-inflated continuous data with smoothing based on generalized additive modeling techniques and approximate Bayesian uncertainty estimates. We expect this data product to be useful for understanding the state of vegetation in the midwestern United States prior to large-scale Euro-American settlement. In addition to specific regional questions, the data product can serve as a baseline against which to investigate how forests and ecosystems change after intensive settlement. The data products (including both raw and statistically smoothed estimates at the 8-km scale) are being made available at the LTER network data portal as version 1.0.

Tree growth of boreal forest plays an important role on global carbon (C) cycle, while tree growth in the western Canadian boreal mixed forests has been predicted to be negatively affected by regional drought. Individual tree growth can be controlled by many factors, such as competition, climate, tree size and age. However, information about contributions of different factors to tree growth is still limited in this region. In order to address this uncertainty, tree rings of two dominant tree species, trembling aspen (Populus tremuloides Michx.) and white spruce (Picea glauca (Moench.) Voss), were sampled from boreal mixed forest stands distributed across Alberta, Canada. Tree growth rates over different time intervals (10years interval, 1998-2007; 20years interval, 1988-2007; 30years interval, 1978-2007) were calculated to study the effects of different factors (tree size, competition, climate, and age) on tree growth. Results indicated that tree growth of two species were both primarily affected by competition or tree size, while climatic indices showed less effects on tree growth. Growth of trembling aspen was significantly affected by inter- and intraspecific competition, while growth of white spruce was primarily influenced by tree size, followed by competition. Positive relationship was found between growth of white spruce and competition index of coniferous group, suggesting an intraspecific mutualism mechanism within coniferous group. Our results further suggested that competition driven succession was the primary process of forest composition shift in the western Canadian boreal mixed forest. Although drought stress increased tree mortality, decline of stem density under climate change released competition stress of surviving trees, which in turn sustained growth of surviving trees. Therefore, climatic indices showed fewer effects on growth of dominant tree species compared to other factors in our study.

Ecology has joined a world of big data. Two complementary frameworks define big data: data that exceed the analytical capacities of individuals or disciplines or the “Four Vs” axes of volume, variety, veracity, and velocity. Variety predominates in ecoinformatics and limits the scalability of ecological science. Volume varies widely. Ecological velocity is low but growing as data throughput and societal needs increase. Ecological big-data systems include in situ and remote sensors, community data resources, biodiversity databases, citizen science, and permanent stations. Technological solutions include the development of open code- and data-sharing platforms, flexible statistical models that can handle heterogeneous data and sources of uncertainty, and cloud-computing delivery of high-velocity computing to large-volume analytics. Cultural solutions include training targeted to early and current scientific workforce and strengthening collaborations among ecologists and data scientists. The broader goal is to maximize the power, scalability, and timeliness of ecological insights and forecasting.

Many studies have already addressed the existence of unstable and nonlinear relationships between radial growth of white spruce (Picea glauca) and climate variables in boreal forests along the high latitudes (>60° N). However, along the mid-latitudes, the climate-growth relationship is still poorly understood. In this study, we used a network of ring-width chronologies from 40 white spruce sites along a wide latitudinal gradients from 52° N to 58° N in Alberta, Canada and attempted to understand the complicated response of tree growth to climatic variables and to identify the main limiting factor for the radial growth of white spruce. We combined the empirical linear statistics with the process-based Vaganov-Shashkin Lite (VS-Lite) model requiring only latitude, monthly mean temperature, and monthly total precipitation information together to better clarify growth-climate relationship. The linear statistical methods indicated that the previous summer temperature imposed a strong negative impact on the radial growth of white spruce while the precipitation and climate moisture index in prior and current summer both had significant positive effects on the radial growth. Similarly, the VS-Lite model showed that the radial growth of white spruce was limited by soil moisture. This suggests that temperature-induced drought is the main limiting factor for the radial growth of white spruce. Furthermore, climate-growth relationship varied along different elevations, latitudes, and growing degree days (GDD >5 °C). The radial growth of white spruce in northern stands was often more strongly limited by temperature-induced drought due to the higher temperature and lower precipitation. As the global climate change is in progress, we suggest that more large-scale and continuous investigations are needed to address the spatial variation in growth-climate relationship due to the temperature-induced drought.

Adequate and advance knowledge of the response of forest ecosystems to temperature-induced drought is critical for a comprehensive understanding of the impacts of global climate change on forest ecosystem structure and function. Recent massive decline in aspen-dominated forests and an increased aspen mortality in boreal forests have been associated with global warming, but it is still uncertain whether the decline and mortality are driven by drought. We used a series of ring-width chronologies from 40 trembling aspen (Populus tremuloides Michx.) sites along a latitudinal gradient (from 52° to 58°N) in western Canada, in an attempt to clarify the impacts of drought on aspen growth by using Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). Results indicated that prolonged and large-scale droughts had a strong negative impact on trembling aspen growth. Furthermore, the spatiotemporal variability of drought indices is useful for explaining the spatial heterogeneity in the radial growth of trembling aspen. Due to ongoing global warming and rising temperatures, it is likely that severer droughts with a higher frequency will occur in western Canada. As trembling aspen is sensitive to drought, we suggest that drought indices could be applied to monitor the potential effects of increased drought stress on aspen trees growth, achieve classification of eco-regions and develop effective mitigation strategies to maintain western Canadian boreal forests.

Forest ecosystems in eastern North America have been in flux for 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 spatiotemporal pollen–vegetation model, to estimate changes in forest composition in the upper Midwestern United States from about 2,100 to 300 yr ago. Using this approach, we find evidence for large changes in the relative abundance of some species, and significant changes in community composition. However, these changes took place against a regional background of changes that were small in magnitude or not statistically significant, suggesting complexity in the spatiotemporal patterns of forest dynamics. The single largest change is the infilling of Tsuga canadensis in northern Wisconsin over the past 2,000 yr. Despite range infilling, 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. 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 2,000 yr.

Anthropogenic changes in land use and land cover (LULC) during the pre-industrial Holocene could have affected regional and global climate. Current LULC scenarios are based on relatively simple assumptions and highly uncertain estimates of population changes through time. Archaeological and palaeoenvironmental reconstructions have the potential to refine these assumptions and estimates. The Past Global Changes (PAGES) LandCover6k initiative is working towards improved reconstructions of LULC globally. In this paper, we document the types of archaeological data that are being collated and how they will be used to improve LULC reconstructions. Given the large methodological uncertainties involved, we propose methods to evaluate the revised scenarios by using independent pollen-based reconstructions of land cover and of climate. A further test involves carbon-cycle simulations to determine whether the LULC reconstructions are consistent with constraints provided by ice-core records of CO2 evolution and modern-day LULC. Finally, we outline a protocol for using the improved LULC reconstructions in palaeoclimate simulations within the framework of the Palaeoclimate Modelling Intercomparison Project in order to quantify the magnitude of anthropogenic impacts on climate through time and ultimately to improve the realism of Holocene climate simulations., Geoscientific Model Development Discussions, ISSN:1991-962X, ISSN:1991-9611

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 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.

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.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Andria Dawson, David Austin, David Walker, Sarah Appleton, Bronwyn M. Gillanders, Shelly M. Griffin, Chika Sakata, Valerie Trouet

Insects, diseases, fire and drought and other disturbances associated with global climate change contribute to forest decline and mortality in many parts of the world. Forest decline and mortality related to drought or insect outbreaks have been observed in North American aspen forests. However, little research has been done to partition and estimate their relative contributions to growth declines. In this study, we combined tree-ring width and basal area increment series from 40 trembling aspen (Populus tremuloides Michx.) sites along a latitudinal gradient (from 52° to 58°N) in western Canada and attempted to investigate the effect of drought and insect outbreaks on growth decline, and simultaneously partition and quantify their relative contributions. Results indicated that the influence of drought on forest decline was stronger than insect outbreaks, although both had significant effects. Furthermore, the influence of drought and insect outbreaks showed spatiotemporal variability. In addition, our data suggest that insect outbreaks could be triggered by warmer early spring temperature instead of drought, implicating that potentially increased insect outbreaks are expected with continued warming springs, which may further exacerbate growth decline and death in North America aspen mixed forests.

Understanding the complex interactions of competition, climate warming-induced drought stress, and photosynthetic productivity on the radial growth of trees is central to linking climate change impacts on tree growth, stand structure and in general, forest productivity. Using a mixed modeling approach, a stand-level photosynthetic production model, climate, stand competition and tree-ring data from mixedwood stands in western Canada, we investigated the radial growth response of white spruce [Picea glauca (Moench.) Voss] to simulated annual photosynthetic production, simulated drought stress, and tree and stand level competition. The long-term (~80-year) radial growth of white spruce was constrained mostly by competition, as measured by total basal area, with minor effects from drought. There was no relation of competition and drought on tree growth but dominant trees increased their growth more strongly to increases in modeled photosynthetic productivity, indicating asymmetric competition. Our results indicate a co-limitation of drought and climatic factors inhibiting photosynthetic productivity for radial growth of white spruce in western Canada. These results illustrate how a modeling approach can separate the complex factors regulating both multi-decadal average radial growth and interannual radial growth variations of white spruce, and contribute to advance our understanding on sustainable management of mixedwood boreal forests in western Canada. © 2017 Alam, Huang, Stadt, Comeau, Dawson, Gea-Izquierdo, Aakala, Hölttä, Vesala, Mäkelä and Berninger.

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.

Fossil pollen assemblages are widely used to reconstruct past vegetation community composition at time scales ranging from centuries to millennia. These reconstructions often are based on the observed relationships between the proportions of plant taxa in the source vegetation and the proportions of the corresponding pollen types in pollen assemblages collected from surface sediments. Pollen-vegetation models rely upon parameters whose values typically are assumed to be stable through time, but this assumption is largely unevaluated, due in part to the rarity of comprehensive forest data, particularly for earlier time periods. Here we present a new dataset of early settlement-era pollen records for the upper Midwest of North America and combine it with three other pollen and forest composition datasets to assess the stability of the relationship between relative pollen composition and relative abundances of tree genera for two time periods: immediately prior to Euro-American settlement, and the late 20th Century. Over this time interval, Euro-American settlement resulted in widespread forest clearance for agriculture and logging, producing major changes to forest composition and structure and the pollen assemblages produced by these forests. These major changes provide an opportunity to test the constancy of the relationship between pollen and forest vegetation during a period of large vegetation change. Pollen-vegetation relationships are modeled, using a Generalized Linear Model, for thirteen upper Midwestern tree genera. We find that estimates of pollen source radius for the gridded mesoscale data are 25-85 kilometers, consistent with prior studies. Pollen-vegetation relationships are significantly altered for several genera: Fagus, Betula, Tsuga, Quercus, Pinus, and Picea (p < 0.05). The use of contemporary pollen-vegetation relationships to model settlement era community composition significantly under-predicts the presence of Fagus, Betula, Tsuga, Quercus and Picea at all tree densities. Pinus is over-predicted at low relative proportions (

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. Gridded (8x8km) estimates of pre-settlement (1800s) forests from the upper Midwestern US (Minnesota, Wisconsin, and most of Michigan) using 19th Century Public Land Survey (PLS) records provide relative composition, biomass, stem density, and basal area for 26 tree genera. This mapping is more robust than past efforts, using spatially varying correction factors to accommodate sampling design, azimuthal censoring, and biases in tree selection. We compare pre-settlement to modern forests using Forest Inventory and Analysis (FIA) data, with respect to structural changes and the prevalence of lost forests, pre-settlement forests with no current analogue, and novel forests, modern forests with no past analogs. Differences between PLSS and FIA forests are spatially structured as a result of differences in the underlying ecology and land use impacts in the Upper Midwestern United States. Modern biomass is higher than pre-settlement biomass in the northwest (Minnesota and north-eastern Wisconsin, including regions that were historically open savanna), and lower in the east (eastern Wisconsin and Michigan), due to shifts in species composition and, presumably, average stand age. Modern forests are more homogeneous, and ecotonal gradients are more diffuse today than in the past. Novel forest assemblages represent 29% of all FIA cells, while 25% of pre-settlement forests no longer exist in a modern context. Lost forests are centered around the forests of the Tension Zone, particularly in hemlock dominated forests of north-central Wisconsin, and in oak-elm-basswood forests along the forest-prairie boundary in south central Minnesota and eastern Wisconsin. Novel FIA forest assemblages are distributed evenly across the region, but novelty shows a strong relationship to spatial distance from remnant forests in the upper Midwest, with novelty predicted at between 20 to 60km from remnants, depending on historical forest type. The spatial relationships between remnant and novel forests, shifts in ecotone structure and the loss of historic forest types point to significant challenges to land managers if landscape restoration is a priority in the region. The spatial signals of novelty and ecological change also point to potential challenges in using modern spatial distributions of species and communities and their relationship to underlying geophysical and climatic attributes in understanding potential responses to changing climate. The signal of human settlement on modern forests is broad, spatially varying and acts to homogenize modern forests relative to their historic counterparts, with significant implications for future management.

Paleoecological data are integral to ecological and evolutionary analyses. First, they provide an opportunity to study ecological and evolutionary interactions between communities and abiotic environments across time scales. Second, they allow us to study the long-term outcomes of processes that occur infrequently, such as megadroughts, hurricanes, and rapid climate change. Third, the past allows us to study ecological processes in the absence of widespread anthropogenic influence. The R package neotoma, described here, obtains and manipulates data from the NeotomaPaleoecological Database (Neotoma Database: http://www.neotomadb.org). The Neotoma Database is a public-domain searchable repository for multiproxypaleoecological records spanning the past 5 million years. The Neotoma Database provides the data and cyberinfrastructure to study spatiotemporal dynamics of species and communities from the Pliocene to the present; neotoma provides a user interface to enable these studies. neotoma searches the Neotoma Database using terms that can include location, taxon name, or dataset type (e.g., pollen, vertebrate fauna, ostracode) using the Database’s Application Programming Interface (API). The package returns a set of nested metadata associated with the site, including the full assemblage record, geochronological data to enable the rebuilding of age models, dataset metadata (e.g. age range of samples, date of accession into Neotoma, principal investigator), and site metadata (e.g. location, site name and description). neotoma also provides tools to allow cross-site analysis, including the ability to standardize taxonomies using built-in taxonomies derived from the published literature or user-provided taxonomies. We show how key functions in the neotoma package can be used, by reproducing analytic examples from the published literature focusing on Pinus migration following deglaciation and shifts in mammal species distributions during the Pleistocene.

In this paper, a model for the radiation treatment of cancer which includes the effects of the cell cycle is derived from first principles. A malignant cell population is divided into two compartments based on radiation sensitivities. The active compartment includes the four phases of the cell cycle, while the quiescent compartment consists of theG0state. Analysis of this active-quiescent radiation model confirms the classical interpretation of the linear quadratic (LQ) model, which is that a larger α/β ratio corresponds to a fast cell cycle, while a smaller ratio corresponds to a slow cell cycle. Additionally, we find that a large α/β ratio indicates the existence of a significant quiescent phase. The active-quiescent model is extended as a nonlinear birth–death process in order to derive an explicit time dependent expression for the tumour control probability (TCP). This work extends the TCP formula from Zaider and Minerbo and it enables the TCP to be calculated for general time dependent treatment schedules.

Background 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. Changes in Forest Structure We compare pre-settlement to modern forests using US Forest Service Forest Inventory and Analysis (FIA) data to show the prevalence of lost forests (pre-settlement forests with no current analog), and novel forests (modern forests with no past analogs). Differences between pre-settlement and modern forests are spatially structured owing to differences in land-use impacts and accompanying ecological responses. Modern forests are more homogeneous, and ecotonal gradients are more diffuse today than in the past. Novel forest assemblages represent 28% of all FIA cells, and 28% of pre-settlement forests no longer exist in a modern context. Lost forests include tamarack forests in northeastern Minnesota, hemlock and cedar dominated forests in north-central Wisconsin and along the Upper Peninsula of Michigan, and elm, oak, basswood and ironwood forests along the forest-prairie boundary in south central Minnesota and eastern Wisconsin. Novel FIA forest assemblages are distributed evenly across the region, but novelty shows a strong relationship to spatial distance from remnant forests in the upper Midwest, with novelty predicted at between 20 to 60km from remnants, depending on historical forest type. The spatial relationships between remnant and novel forests, shifts in ecotone structure and the loss of historic forest types point to significant challenges for land managers if landscape restoration is a priority. The spatial signals of novelty and ecological change also point to potential challenges in using modern spatial distributions of species and communities and their relationship to underlying geophysical and climatic attributes in understanding potential responses to changing climate. The signal of human settlement on modern forests is broad, spatially varying and acts to homogenize modern forests relative to their historic counterparts, with significant implications for future management.

The demand for large-scale and long-term information on tree growth is increasing rapidly as environmental change research strives to quantify and forecast the impacts of continued warming on forest ecosystems. This demand, combined with the now quasi-global availability of tree-ring observations, has inspired researchers to compile large tree-ring networks to address continental or even global-scale research questions. However, these emergent spatial objectives contrast with paleo-oriented research ideas that have guided the development of many existing records. A series of challenges related to how, where, and when samples have been collected is complicating the transition of tree rings from a local to a global resource on the question of tree growth. Herein, we review possibilities to scale tree-ring data (A) from the sample to the whole tree, (B) from the tree to the site, and (C) from the site to larger spatial domains. Representative tree-ring sampling supported by creative statistical approaches is thereby key to robustly capture the heterogeneity of climate-growth responses across forested landscapes. We highlight the benefits of combining the temporal information embedded in tree rings with the spatial information offered by forest inventories and earth observations to quantify tree growth and its drivers. In addition, we show how the continued development of mechanistic tree-ring models can help address some of the non-linearities and feedbacks that complicate making inference from tree-ring data. By embracing scaling issues, the discipline of dendrochronology will greatly increase its contributions to assessing climate impacts on forests and support the development of adaptation strategies.