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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.

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.

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.

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.

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.

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.