Historical Observations Research Articles

The case for stand management guidelines as dynamic as global change: Aspen forest stockings of the western Great Lakes

Since the development of contemporary stocking techniques a century ago, the combination of climatic, atmospheric, financial, and social factors that determine forest management strategies have changed, altering aspen stand dynamics in the western Great Lakes, USA. Despite this, aspen management is still informed by 1970s management guides that are based on 1920s inventories; hence, a century exists between the data that underlie current management guidelines and current stand conditions. We hypothesized that current aspen stands may support higher stocking and height growth than nearly a century ago at relatively similar age and site indices, due to increased atmospheric CO2 concentrations and fertilization, intensive coppice harvests, and other factors. To explore this question, we compared historic aspen observations with comparable contemporary data from the USDA Forest Service’s Forest Inventory and Analysis program. The results show increased stand stocking levels as well as increased height growth of aspen throughout the region over the historic inventory data. Although other controlled experimental studies support the hypothesis of increased carbon fertilization altering aspen size-density relationships, our study is the first to examine an empirical application to forest management guides. Our results suggest a comprehensive reevaluation of aspen growth dynamics under contemporary environmental conditions is warranted. We highlight the need to assess the value of current stocking standards in an era of increasingly variable environmental conditions and to reimagine a more dynamic, responsive, and predictive approach to guide forest management for future application as global change may accelerate.

The Usage of State Space Models in Mortality Modeling and Predictions

In demography, mortality modeling with respect to age and time dimensions is often associated with the traditionally used Lee-Carter model. The Lee-Carter model considers a constant set of parameters of agespecific mortality change for forecasts, which can lead to the problem of overcoming the biodemographic limit. The main motivation of this paper is the use of more flexible models for mortality modeling. The paper explores the use of state space models for modeling and predicting mortality in a form not typically used in demography. In this context, it is a generalized Poisson state space model with overdispersion parameters. Concerning the empirical results, a comparison is made between the predictive abilities of the Lee-Carter and the generalized Poisson state space model with overdispersion parameters. The state space Poisson model with overdispersion parameters led to better results with respect to the comparison of modeled and historical observations. However, when comparing the predictions in the cross-validation area, both models were represented with similar overall mean squared error.

On the forecastability of food insecurity

Food insecurity, defined as the lack of physical or economic access to safe, nutritious and sufficient food, remains one of the main challenges included in the 2030 Agenda for Sustainable Development. Near real-time data on the food insecurity situation collected by international organizations such as the World Food Programme can be crucial to monitor and forecast time trends of insufficient food consumption levels in countries at risk. Here, using food consumption observations in combination with secondary data on conflict, extreme weather events and economic shocks, we build a forecasting model based on gradient boosted regression trees to create predictions on the evolution of insufficient food consumption trends up to 30 days in to the future in 6 countries (Burkina Faso, Cameroon, Mali, Nigeria, Syria and Yemen). Results show that the number of available historical observations is a key element for the forecasting model performance. Among the 6 countries studied in this work, for those with the longest food insecurity time series, that is Syria and Yemen, the proposed forecasting model allows to forecast the prevalence of people with insufficient food consumption up to 30 days into the future with higher accuracy than a naive approach based on the last measured prevalence only. The framework developed in this work could provide decision makers with a tool to assess how the food insecurity situation will evolve in the near future in countries at risk. Results clearly point to the added value of continuous near real-time data collection at sub-national level.

Extreme sea level changes over the tropical western Pacific in 1.5 °C and 2.0 °C warmer climates

The tropical western Pacific and the adjacent South China Sea are home to many low-lying islands and coastal zones that are vulnerable to flood hazards resulting from extreme sea level (ESL) changes. Based on the hourly sea level recorded by 15 tide gauges during the period 1980-2018, this study evaluates the historical trend of ESLs over this region. On this basis, a regression model for hourly future sea-level prediction is established by combining the atmospheric reanalysis products, the tidal harmonics, and the outputs of three climate models archived by the Coupled Model Intercomparison Project Phase 6 (CMIP6) to evaluate the future ESL changes in 1.5 °C and 2.0 °C warmer climates. The historical trend of ESLs show that the ESLs along the coasts and islands of the tropical western Pacific have significantly risen during the past decades, which is mainly contributed by the mean sea level rise. And results from the historical observations and the prediction model show that in a warming climate from 1980 to 2050, both the mean sea levels and ESLs rise with fluctuations. The mean sea level change plays an important role in the secular trend of ESLs, while the interannual-to-decadal variability of ESLs is significantly affected by tides and extreme weather events. Under the warming scenario of 1.5°C, the changes in the return levels of ESL relative to the historical period are generally small at most tide gauge sites. Compared with the situations under 1.5°C warming, the return levels of ESL at most selected tide gauges will rise more significantly under the 2.0°C warming scenario, so the frequency of the current 100-year return level will reduce to less than 10 years at most stations. The above results suggest that this additional 0.5°C warming will cause a huge difference in the ESLs along the coasts and islands of the tropical western Pacific. As proposed in the Paris climate agreement, it is very necessary to limit anthropogenic warming to 1.5°C instead of 2.0°C, which will substantially reduce the potential risk of flood disasters along the coasts and islands of the tropical western Pacific.

Human-induced changes in the global meridional overturning circulation are emerging from the Southern Ocean

In a warming climate, the Global Meridional Overturning Circulation (GMOC) is expected to change significantly with a risk of disrupting the global redistribution of ocean properties that sustains marine ecosystems, carbon cycle, and others. Here we make a novel attempt to utilize a diagnostic ocean & sea-ice model to estimate the GMOC and its interdecadal changes since the mid-1950s that are consistent with historical hydrographic observations. We find that significant changes in the GMOC have already occurred, most notably in the upper and lower overturning cells in the Southern Ocean. The former has expanded poleward and into denser water and strengthened by 3–4 Sv since the mid-1970s, while the latter has contracted and weakened by a similar rate during the same period. These changes are driven by the increasing Southern Hemisphere (SH) Ferrel cell and associated increases in the westerlies and the surface buoyancy loss over its sinking branch, and the increasing Antarctic meltwater discharge, in response to ozone depletion in the SH stratosphere and increasing atmospheric CO2. A large-scale readjustment of the GMOC seems to be underway in the South Atlantic and Indo-Pacific Oceans since the mid-2000s in response to the Southern Ocean changes.

Exploring the impacts of climate change and human activities on future runoff variations at the seasonal scale

Most watersheds around the world have been changing their natural flow patterns because of the coupled effects of climate change and human activities. Understanding the quantitative impacts of projected climate and human factors on future runoff variations is essential for adaptability assessment of water resource management, especially at the seasonal scale where their intra-annual changes are detected. In this study, an extended Budyko framework that combines traditional elasticity and decomposition methods is developed to analyze future seasonal runoff variations. The upper reach of the Hanjiang River basin (UHRB) in China is used as a case study. The case results are quantified and compared with the monthly ABCD model using historical hydrometeorological observations over 1961–2020 and near-future climate projections over 2031–2060 from the multi-model ensemble of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b). We find that: (1) human activities including the operation of the Han-to-Wei inter-basin water transfer (IBWT) and reforestation projects have a substantial negative impact on runoff reduction in this region, accounting for a proportion of ∼70% in energy-limited seasons and ∼65% in water-limited seasons; and (2) the role of climate change projected by ISIMIP3b will intensify in energy-limited seasons, especially under the high-emission scenario with the increasing trend of effective precipitation,. Additionally, the performance of our extended Budyko framework is sufficiently robust to parameter disturbance experiments, indicating its applicability as an alternative for exploring future seasonal runoff variations.

“Bulgarin's Manor” by F.V.B.: on the Question of the Properties of Network Paraliterature

The article analyzes the anonymous poetic text “Bulgarin's Manor”, which is a small anonymous poem published on the Internet systems Ridero and Amazon on February 2, 2022 under the laconic cryptonym F.V.B., referring to the initials of one of the main anti-heroes of Russian literature - Faddey Venediktovich Bulgarin. This context, connected with Russian-Estonian relations, on the one hand, and the history of Russian literature, on the other, seems to us the most significant for commentary. Based on historical and literary observations and reconstructing the historical and cultural contexts reflected in the textual structures of the work, we show how the anecdotes that accompany the personality of Thaddeus Bulgarin (1789-1859) and the myths that describe the history of Karlovo are transformed in a naive, amateur mind. Special attention is paid to the explicit intertext, represented primarily by the works of Nicolay Gogol and Prosper Merimee and implicit intertextual connections leading to the work of Alexander Pushkin. The study of the work allows us to be convinced of the total mastery of the idea of a palimpsest by modern actors of network literature.

Evaluating neural network models in site-specific solar PV forecasting using numerical weather prediction data and weather observations

The effective use of solar photovoltaic (PV) installations implies the integration of solar PV output into overall energy consumption planning, optimization, and control. Moreover, day-ahead trading of electricity in Europe makes day-ahead solar PV forecasting utterly important, and thus its accuracy becomes of particular interest. Data-driven PV forecasting models are typically trained using numerical weather prediction (NWP) data, the availability of which represents one of the main obstacles in modeling. In this study, we investigate an alternative scenario, in which an artificial neural network (ANN) is trained on weather observations and then tested on NWP data to simulate the model's use in operational PV forecasting. In the experiments, solar PV output data, historical weather observations, and historical NWP data were collected from three sites in eastern Finland. The results showed that, although training ANN on observational data leads to a slight decrease in its performance compared to ANN trained on NWP data, it still outperforms a physical model. In practice, this alternative scenario means that if historical NWP data are not available for model training, observational data allow effective model selection and parameter tuning, and then generalization error estimates are gradually updated using online NWP data.

A Hybrid Ensemble Learning Model for Short-Term Solar Irradiance Forecasting Using Historical Observations and Sky Images

A hybrid multi-modal ensemble learning model is proposed for short-term solar irradiance forecasting based on historical observations and sky images in this paper. In the proposed model, historical observations of solar irradiance are utilized to extract temporal characteristics, and ground-based sky images are used as an exogenous input to offer cloud cover information. A powerful ensemble learning model, Extreme Gradient Boosting (XGBoost), is employed to capture the function relationships between input features and future observations. Since mean squared error loss is sensitive to the extreme large or small historical irradiance, a novel loss function is proposed to improve robustness of XGBoost. In order to find out the best controlling parameters, Rao-1 algorithm is employed for its easy operation. To validate performance of the proposed method, a solar irradiance dataset containing three-year historical observations and ground-based sky images collected from Folsom is employed. Meanwhile, five commonly applied methods, LASSO, Ridge regression, support vector regression, boosted regression trees, the generic XGBoost, are considered as benchmarking methods. The forecasting horizons from 5 to 30 min are considered for all compared methods while two metrics, mean absolute error and root mean squared error, are computed. Experimental results prove that the proposed hybrid model has better forecasting performance compared with benchmarking methods over all forecasting horizons.

Interaction-Aware Prediction for Cut-In Trajectories With Limited Observable Neighboring Vehicles

Predicting the future trajectories of risky maneuvers such as cut-in is of great significance for intelligent vehicles to alarm potential crashes in advance. A more critical issue is the use of limited observable neighboring vehicles to achieve reliable trajectory prediction. Motivated by it, this paper studies the trajectory prediction for cut-in maneuvers from ego-vehicle's-view. An interaction-aware model is developed for long-term prediction based on the attention mechanism, where both the encoder and the social interactor are implemented through multi-head attentions and feed forward layers. First, an attention-based trajectory encoder module with temporal embedding is presented to extract hidden features from historical observations. Then another attention-based module is constructed to characterize social interactions between the target vehicle and its neighbors. In the interactor, a novel social mask layer is designed to improve prediction performance in different scenarios, especially for discretionary cut-in maneuvers. Experimental comparison with the state-of-the-art models on public dataset demonstrates the excellent performance of our proposed model on cut-in trajectory prediction. In addition, ablation studies are conducted to highlight the strengths of the main modules in our model.

Deep reinforcement learning-based collaborative routing algorithm for clustered MANETs

Flexible adaptation to differentiated quality of service (QoS) is quite important for future 6G network with a variety of services. Mobile ad hoc networks (MANETs) are able to provide flexible communication services to users through self-configuration and rapid deployment. However, the dynamic wireless environment, the limited resources, and complex QoS requirements have presented great challenges for network routing problems. Motivated by the development of artificial intelligence, a deep reinforcement learning-based collaborative routing (DRLCR) algorithm is proposed. Both routing policy and subchannel allocation are considered jointly, aiming at minimizing the end-to-end (E2E) delay and improving the network capacity. After sufficient training by the cluster head node, the Q-network can be synchronized to each member node to select the next hop based on local observation. Moreover, we improve the performance of training by considering historical observations, which can improve the adaptability of routing policies to dynamic environments. Simulation results show that the proposed DRLCR algorithm outperforms other algorithms in terms of resource utilization and E2E delay by optimizing network load to avoid congestion. In addition, the effectiveness of the routing policy in a dynamic environment is verified.

Projecting the Changes in Multifaceted Characteristics of Heatwave Events Across China

AbstractUnderstanding future variations and trends of heatwave events has critical implications for the ecosystem and human health. However, the diverse metrics of heatwave affect the comparable assessment of heatwave evolution at regional scales. The inadequate consideration of the enhanced warming trend and local antecedent heat conditions at different climate zones undermines the comprehensive understanding of future heatwave changes. Here, we systematically assess variations and trends in duration, frequency, and intensity of heatwave events in China from 1961 to 2100, using historical observations and climate model simulations from Coupled Model Intercomparison Project Phase 6. The increased rates and trends in the duration and frequency are more evident than those in intensity and severe heatwave days. Regionally, the northern and western regions are projected to suffer longer and more frequent consecutive heatwaves, while southern regions are likely at greater risk of severe heatwave days. A comparison among four scenarios shows that the future heatwave characteristics projected by the high forcing Shared Socioeconomic Pathway (SSP5‐8.5) exhibit substantial intensification than those in other three experiments, imposing intractable dangers to numerous organisms and ecosystems. Under the SSP1‐2.6, the acceleration of all heatwave characteristics is projected to slow down in all regions after 2040. In addition to maximum temperature, temperature advections are projected to contribute to heatwave intensification in western regions. Our results provide a comprehensive assessment of future variations and trends in heatwave events. The comparable future changes across unevenly developed regions are necessary for improving regional adaptive capacity to extreme heat risk.

Regulating the time of the crop model clock: A data assimilation framework for regions with high phenological heterogeneity

in crop growth data assimilation systems, the mismatch between simulated and observed phenology significantly deteriorates the performance of crop growth modeling. This situation may be more severe for smallholder farmers-managed fields, where the phenological heterogeneity was high even when climate condition was relatively uniform. Previous studies investigated the non-sequential methods to retrospectively assimilate historical phenology observations. However, approaches to dynamically assimilating phenological measurements through sequential data assimilation methods remain unexplored one of the most intractable challenges of dynamic phenology assimilation is that a considerable proportion of model parameters and variables are entangled with phenology, therefore simply assimilating phenological measurements could disturb the model clock. This study aims to establish a robust crop data assimilation framework capable of assimilating phenological measurements in real time without disturbing the model clock the framework used an open-source version of the AquaCrop model to simulate crop growth and used the ensemble Kalman filter (EnKF) to assimilate observations sequentially. A parameter refresh method was proposed to restore the phenological consistency of model parameters after updating the phenology state. Assimilation strategies with different observation types and compositions of state vectors were designed after a global sensitivity analysis of model parameters. These strategies were evaluated through the Observing System Simulation Experiments (OSSE), and the selected strategies were tested in a real-world case. the results of the OSS Experiments show that the phenological mismatch problem greatly affects crop growth simulation, and this mismatch could not be narrowed effectively by assimilating non-phenological observations. Assimilating phenological measurements with the proposed parameters refresh method and assimilation strategies closed this mismatch and produced better performance compared to the Restart-EnKF method. In the real-world paddy rice case, assimilating phenology with the proposed strategies significantly improved yield estimation in low-yield plots (less than 4 ton/ha) compared to assimilating canopy cover (CC) alone, with an R2 increase from 0.07 to 0.48. Assimilating CC, biomass and phenology simultaneously produced the best yield estimation for all plots, with R2 = 0.57 and RMSE = 1.00 ton/ha. assimilating phenology under a consistent model clock significantly improved yield estimation when the phenological heterogeneity of plots was high. the results highlight the effectiveness and robustness of the established data assimilation framework for dynamic crop growth simulation, indicating the potentials of the proposed data assimilation framework for regional in-season crop modeling and yield forecasting.

Accurate storm surge prediction using a multi-recurrent neural network structure

This paper considers storm surge prediction using a neural network and considering multiple physical characteristics. Based on the factors that influence storm surges and historical observation data, we divide the input to the neural network into time features extracted from the prediction target and the auxiliary features that affect storm surges, and construct a feature gate within multiple recurrent neural network (RNN) cells. Historical hurricane data are used to assess the effectiveness and accuracy of the proposed model. Comparative analysis against a long short-term memory (LSTM) storm surge prediction model is conducted to verify the prediction performance of the proposed method. The comparison results show that the multi-RNN model is superior to the LSTM model in terms of four evaluation metrics and for all lead times. In particular, the multi-RNN model accurately predicts the maximum storm surge water level, and the prediction results are more consistent with the rise and fall of the water. A comparison of the storm surge forecasts using inputs from different time intervals under different evaluation indices confirms the generalization and stability of our proposed model. The experiments of storm surge prediction at six stations further confirm the wide applicability of the model.

Non-linear response of glacier melting to Holocene warming in Svalbard recorded by sedimentary iron (oxyhydr)oxides

The recent acceleration of ice-sheet loss with its direct impact on sea-level rise and coastal ecosystems is of major environmental and societal concern. However, the effect of atmospheric temperature increases on long-term glacier retreat remains poorly defined due to limited historical observations and uncertainties in numerical ice-sheet models, which challenges climate change adaptation planning. Here, we present a novel approach for investigating the time-transgressive response of Arctic glaciers since the last deglaciation, using glacially-derived Fe-(oxyhydr)oxide layers preserved in glacimarine sediments from a large fjord system in Svalbard. Glacial weathering releases large amounts of Fe, resulting in the deposition of Fe-(oxyhydr)oxide particulates in nearby marine sediments, which can serve as fossil indicators of past glacial melting events. Our results indicate that Svalbard glaciers retreated at a rate of 18 to 41 m/yr between 16.3 and 10.8 kyr BP, synchronously with the progressive rise in atmospheric and oceanic temperatures. From 10.8 kyr BP, glacier retreat markedly accelerated (up to ∼116 m/yr) when regional atmospheric temperatures exceeded modern values. Coupled with field observations, this finding directly supports a non-linear response of glacial melting to summer air temperature increases. In addition to suggesting that ice-sheet loss and sea-level rise may further accelerate in the near future, this study paves the way for the use of sedimentary Fe-(oxyhydr)oxide layers in subarctic environments for reconstructing past glacial dynamics.

Design Rainfall Change of Rainwater Source Control Facility to Meet Future Scenarios in Beijing.

Rainwater source control facilities are essential to sponge city construction in China. Their size is determined based on historical rainfall data. However, with global warming and rapid urban development, rainfall characteristics have also changed, potentially leading to the failure of rainwater source- control facilities to manage surface water in the future. In this study, the design rainfall's change and spatial distribution are analyzed using historical (1961-2014) observation rainfall data and future (2020-2100) projection data of three CMIP6 climate models. The results show that EC-Earth3 and GFDL-ESM4 project that future design rainfall will increase. EC-Earth3 projects a significant increase, while MPI-ESM1-2 projects that the design rainfall will decrease significantly. From the perspective of space, the design rainfall isoline in Beijing has always increased from northwest to southeast. In the historical period, the difference in design rainfall in different regions has reached 19 mm, and this regional heterogeneity shows an increasing trend in the future projection of EC-Earth3 and GFDL-ESM4. The difference in design rainfall in different regions is 26.2 mm and 21.7 mm, respectively. Therefore, it is necessary to consider future rainfall changes in the design of rainwater source control facilities. The relationship curve between the volume capture ratio (VCR) of annual rainfall and design rainfall based on the rainfall data of the project site or region should be analyzed to determine the design rainfall of the rainwater source control facilities.

Reconstructed River Water Temperature Dataset for Western Canada 1980–2018

Continuous water temperature data are important for understanding historical variability and trends of river thermal regime, as well as impacts of warming climate on aquatic ecosystem health. We describe a reconstructed daily water temperature dataset that supplements sparse historical observations for 55 river stations across western Canada. We employed the air2stream model for reconstructing water temperature dataset over the period 1980–2018, with air temperature and discharge data used as model inputs. The model was calibrated and validated by comparing with observed water temperature records, and the results indicate a reasonable statistical performance. We also present historical trends over the ice-free summer months from June to September using the reconstructed dataset, which indicate- significantly increasing water temperature trends for most stations. Besides trend analysis, the dataset could be used for various applications, such as calculation of heat fluxes, calibration/validation of process-based water temperature models, establishment of baseline condition for future climate projections, and assessment of impacts on ecosystems health and water quality.

Dynamic Multi-View Graph Neural Networks for Citywide Traffic Inference

Accurate citywide traffic inference is critical for improving intelligent transportation systems with smart city applications. However, this task is very challenging given the limited training data, due to the high cost of sensor installment and maintenance across the entire urban space. A more practical scenario to study the citywide traffic inference is effectively modeling the spatial and temporal traffic patterns with limited historical traffic observations. In this work, we propose a dynamic multi-view graph neural network for citywide traffic inference with the method CTVI+. Specifically, for the temporal dimension, we propose a temporal self-attention mechanism that is capable of learning the dynamics of traffic data with the time-evolving traffic volume variations. For spatial dimension, we build a multi-view graph neural network, employing the road-wise message passing scheme to capture the region dependencies. With the designed spatial-temporal learning paradigms, we enable our traffic inference model to encode the dynamism from both spatial and temporal traffic patterns, which is reflective of intra- and inter-road traffic correlations. In our evaluation, CTVI+ achieves consistent better performance compared with different baselines on real-world traffic volume datasets. Further ablation study validates the effectiveness of key components in CTVI+. We release the model implementation at https://github.com/dsj96/TKDD.

Temperature effect on Brazilian soybean yields, and farmers’ responses

ABSTRACTBrazil is one of the largest suppliers of commodities in the world, partly due to the agricultural expansion in the Cerrado biome that began in the 1970s. However, as areas with better soil and climate for agriculture become scarce, farmers advanced to marginal lands, where precipitation is less reliable for rainfed crops. The overall goal of this paper is to investigate the implications of occupying drier areas in the Cerrado biome, which are likely to become more so in the future. We estimated the effect of temperature and precipitation on soybean yields (kg/ha) using panel data regression at the municipal level, from 1980 to 2016, and at the farm-level in a subset region known as Matopiba. Our analysis estimated a reduction of 6% in soybean yield for each 1°C increase in temperature. According to interviewed farmers, the consequences of the recent droughts include land concentration and increased indebtedness. Based on historical observations, agricultural technologies do not provide adaptation to extreme heat and created a dependency on capital availability for investments in irrigation, raising questions about the future sustainability of this capital-intensive agricultural system if breakthroughs in adaptation do not occur.

The New Zealand bracken fern rhizome, Pteridium esculentum (G.Forst): a toxic food plant of pre‐European Māori

ABSTRACTThe two species of bracken fern, Pteridium esculentum and Pteridium aquilinum, are well known to produce neoplastic lesions and thiamine deficiency when consumed by mammals, with severe consequences to health. New Zealand Pre‐European Māori are known to have consumed rhizomes of P. esculentum as food with little or no recorded consequences to health. Processing methods by Māori prior to consumption may have helped to detoxify this food. We carried out LDH toxicity tests on rhizomes that had been pre‐processed before simulated digestion to test this possibility. We tested rhizomes harvested each month of the year, different components of the rhizome, both raw and roasted rhizomes, rhizomes stored for up to 12 months, and rhizomes leached for up to 24 hours. All specimens remained equally toxic within experimental error. We carried out a detailed analysis of nutrients in bracken rhizome and compared this with kūmara, Ipomoea batatas, another important food plant for pre‐European Māori, and found that bracken rhizome has c. 70% of the caloric value of kūmara. A cost/benefit analysis of the two plants suggested that the reward for effort is greatest for kūmara by a modest amount. Analysis of historic ethnographic observations relating to bracken rhizome from AD 1769 to the 1840s provides complex and contradictory evidence of the role of bracken rhizome in the Māori economic system. Although there is clear evidence that Māori greatly favoured chewing rhizomes, this fondness may result from the presence of one or more plant secondary metabolites (PSM), such as ecdysone, which are known to be addictive. Our analysis of the evidence favours the plant being essentially a famine food, filling in the period between planting and harvest of kūmara, known as the ‘hungry gap’ between October and April in the southern hemisphere. However, it would also have provided an important source of food for travellers, as fern‐lands are widespread. Our analysis of archaeological information did not produce unequivocal direct evidence of bracken rhizome consumption. However, the presence of extreme tooth wear and a unique pattern of first molar dislocation, attributed to the use of teeth to strip starch from rhizomes, has been shown to be present at all periods of New Zealand prehistory. This is contrary to the finding of some other researchers.