利用树木年轮重建赣南地区1890年以来 2-3月份温度的变化

采用树木年轮气候学方法,利用江西赣南地区马尾松(Pinus massoniana)的年轮宽度资料,分析了马尾松径向生长与气候要素变化的相关及响应关系,结果表明赣南地区马尾松径向生长与当年2—3月份的平均温度相关性最高。在响应分析的基础上,重建了江西赣南地区1890年以来2—3月份温度的变化历史。重建序列显示在过去119a中研究区存在3个较为明显的冷期(1892—1906年、1918—1922年、1944—1957年)和3个明显的暖期(1909—1917年、1959—1968年和1998—2008年)。     

Individual and time-varying tree-ring growth to climate sensitivity of Pinus tabuliformis Carr. and Sabina przewalskii Kom. in the eastern Qilian Mountains, China

Individual tree-ring width chronologies and mean chronologies from Pinus tabuliformis Carr. (Chinese pine) and Sabina przewalskii Kom. (Qilian juniper) tree cores were collected and analyzed from two sites in the eastern Qilian Mountains of China. The chronologies were used to analyze individual and time-varying tree-ring growth to climate sensitivity with monthly mean air temperature and total precipitation data for the period 1958–2008. Climate–growth relationships were assessed with correlation functions and their stationarity and consistency over time were measured using moving correlation analysis. Individuals’ growth–climate correlations suggested increased percentages of individuals are correlated with certain variables (e.g., current June temperature at the P. tabuliformis site; previous June, December and current May temperature and May precipitation at the S. przewalskii site). These same climatic variables also correspond to the mean chronology correlations. A decreased percentage of individuals correlated with these climatic variables indicates a reduced sensitivity of the mean chronology. Moving correlation analysis indicated a significant change over time in the sensitivity of trees to climatic variability. Our results suggested: (1) that individual tree analysis might be a worthwhile tool to improve the quality and reliability of the climate signal from tree-ring series for dendroclimatology research; and (2) time-dependent fluctuations of climate growth relationships should be taken into account when assessing the quality and reliability of reconstructed climate signals.     

Multi-Year Lags between Forest Browning and Soil Respiration at High Northern Latitudes

High-latitude northern ecosystems are experiencing rapid climate changes, and represent a large potential climate feedback because of their high soil carbon densities and shifting disturbance regimes. A significant carbon flow from these ecosystems is soil respiration (R _S, the flow of carbon dioxide, generated by plant roots and soil fauna, from the soil surface to atmosphere), and any change in the high-latitude carbon cycle might thus be reflected in R _S observed in the field. This study used two variants of a machine-learning algorithm and least squares regression to examine how remotely-sensed canopy greenness (NDVI), climate, and other variables are coupled to annual R _S based on 105 observations from 64 circumpolar sites in a global database. The addition of NDVI roughly doubled model performance, with the best-performing models explaining ∼62% of observed R _S variability. We show that early-summer NDVI from previous years is generally the best single predictor of R _S, and is better than current-year temperature or moisture. This implies significant temporal lags between these variables, with multi-year carbon pools exerting large-scale effects. Areas of decreasing R _S are spatially correlated with browning boreal forests and warmer temperatures, particularly in western North America. We suggest that total circumpolar R _S may have slowed by ∼5% over the last decade, depressed by forest stress and mortality, which in turn decrease R _S. Arctic tundra may exhibit a significantly different response, but few data are available with which to test this. Combining large-scale remote observations and small-scale field measurements, as done here, has the potential to allow inferences about the temporal and spatial complexity of the large-scale response of northern ecosystems to changing climate.     

A new method for reconstructing past-climate trends using tree-ring data and kernel smoothing

Mediterranean high-relief karst areas are very vulnerable to changes in temporal patterns of precipitation and temperature. Understanding climate change in these areas requires current climate trends to be assessed within the context of the variability of rainfall and temperature trends in the recent past. A major difficulty is that the instrumental record in these high-relief areas is very limited and the use of data from paleoclimatic proxies, such as tree-ring data, is required to infer past climate variability. Furthermore, for complex relationships between tree-ring data and climatic variables, it is almost impossible to infer past inter-annual variations in temperature or precipitation, and the inference is limited to the reconstruction of low-frequency variability (i.e., the trend). To do so, in this work, we propose a new method based on detecting trends (by kernel smoothing) in tree variables that show maximum correlation with the trends (also estimated by kernel smoothing) of climate variables. This enables a standard regression framework to be established to reconstruct past climate. We have used tree-ring proxy data from Abies pinsapo to evaluate past climate trends in the Sierra de las Nieves karst massif in Southern Spain. Our analysis has found that during the last three hundred years the smoothed mean annual rainfall steadily decreased until the beginning of the 20th century and thereafter it remained more or less constant until the end of the century. On the other hand, the smoothed mean annual temperature has steadily increased since the beginning of the 18th century until recent times. These trends are also suggested by the climate projections for the latter part of the current 21st century. As the study area is a high-relief karst massif of significant hydrologic and ecologic interest, the implications of these trends should be taken into account when formulating effective action plans to mitigate the impact of climate change.     

The dendrochronological potential of Baikiaea plurijuga in Zambia

Climate has been demonstrated to change at different scales for as far back as we have been able to reconstruct it. However, anthropogenic factors have accelerated and are predicted to cause significant changes in temperature and precipitation around the globe. As a consequence, vegetation is being affected. To understand the historical behaviour of individual tree species and have insight on the potential effects of climate change, tree-ring studies have been applied. In this study, we examined a genus new to dendrochronology, namely Baikiaea plurijuga (Spreng.) Harm that dominates the Zambezi teak forests in Zambia with the objective of determining whether B. plurijuga forms annual rings and if so, whether these rings are cross-datable. We further determined the relationship between ring- width of B. plurijuga and climatic variables with the aim of understanding the potential climate change effects on the growth of these species in Zambia. We collected tree-ring samples from three Zambezi Teak forest reserves: Zambezi, Ila, and Masese located in Kabompo, Namwala, and Sesheke study sites respectively. Our examination of wood anatomical structures reviewed that the wood of B. plurijuga is diffuse porous and forms annual rings which were confirmed with samples of known age. The analysis resulted in three strong tree-ring chronologies of B. plurijuga. These chronologies were correlated with climate data from local weather stations which correlated negatively with evaporation and temperature and positively with rainfall. Our regression analysis indicated that evaporation has the highest influence on tree growth at all the study sites compared to temperature and rainfall alone. Evaporation in November and March, for example, explained almost a third of the radii’s variance at the Namwala and Sesheke sites. The likely future temperature increase and rainfall decrease that are projected by IPCC for Southern Africa are likely to adversely affect B. plurijuga in Zambia.     

Climate–growth relationships of the dominant tree species from semi-arid savanna woodland in Ethiopia

Long-term climate–growth relationships, were examined in tree rings of four co-occurring tree species from semi-arid Acacia savanna woodlands in Ethiopia. The main purpose of the study was to prove the presence of annual tree rings, evaluate the relationship between radial growth and climate parameters, and evaluate the association of El Niño and drought years in Ethiopia. The results showed that all species studied form distinct growth boundaries, though differences in distinctiveness were revealed among the species. Tree rings of the evergreen Balanites aegyptiaca were separated by vessels surrounding a thin parenchyma band and the growth boundary of the deciduous acacias was characterized by thin parenchyma bands. The mean annual diameter increment ranged from 3.6 to 5.0 mm. Acacia senegal and Acacia seyal showed more enhanced growth than Acacia tortilis and B. aegyptiaca. High positive correlations were found between the tree-ring width chronologies and precipitation data, and all species showed similar response to external climate forcing, which supports the formation of one tree-ring per year. Strong declines in tree-ring width correlated remarkably well with past El Niño Southern Oscillation (ENSO) events and drought/famine periods in Ethiopia. Spectral analysis of the master tree-ring chronology indicated occurrences of periodic drought events, which fall within the spectral peak equivalent to 2–8 years. Our results proved the strong linkage between tree-ring chronologies and climate, which sheds light on the potential of dendrochronological studies developing in Ethiopia. The outcome of this study has important implications for paleoclimatic reconstructions and in restoration of degraded lands.     

Prediction of the digestible crude protein and protein digestibility of feed ingredients for rabbits from chemical analysis

Ninety-five data of individual feedstuffs, for which information on chemical analyses and crude protein digestibility (CPD) was available, were subject to sequential multiple linear regression analysis employing CPD and digestible crude protein (DCP) as dependent variables and chemical analysis as independent variables. The feedstuffs were classed separately into dry forages (n=26), cereals and cereal by-products (n=29), protein concentrates (n=18) and by-products (n=22) groups. The procedure followed in each of the groups was to utilize: (1) the feedstuffs in which the Van Soest fiber and the Weende analysis were simultaneously available, and (2) all feedstuffs with data of the variables selected in the first step. The CP content was the best single predictor of the DCP content of feeds, the best equation in terms of R² was obtained in the total group: DCP (g kg⁻¹)=−34.67+0.876×CP (g kg⁻¹), R²=0.948, RSD=7.36, n=93. The addition of the ash content to sequential analysis improved the accuracy of DCP prediction, increasing the R² up to 0.960. Acid detergent fiber was the second independent variable selected in the cereals and by-products groups, the R² obtained was, respectively, higher (0.969) and lower (0.895) than that of general equation. The correlations among the independent variables employed and CPD were low. Only when in the total group the nitrogen linked to acid detergent fiber (N–ADF) measurement was included as independent variable (n=11) a high R² (0.905) was obtained. The N–ADF content that included the heat-damaged protein and the nitrogen associated with lignin could be adequate to estimate the indigestible protein content of feeds. However, more data points would be necessary to confirm its accuracy, taking into account the non-uniformity in the distribution of current data.     

From modeling the kinetics of cell enlargement to building tracheidograms of Larix gmelinii Rupr. (Rupr.) in the permafrost zone in Siberia

Boreal forest ecosystems are a crucial element in the global climate balance. In harsh environments functioning lateral meristems of trees are more regulated by the exogenous (included local climate) than endogenous factors. This information is encoded in the tree-ring structure which can be effectively decomposed by the process-based tree-ring growth simulations. Moreover, the process-based modeling can be used to describe non-linear processes linking climate variables with tree-ring formation. In this study, we applied the Vaganov-Shashkin model to simulate seasonal cell production and cell enlargement of Larix gmelinii Rupr. (Rupr.) growing in the permafrost zone of Central Siberia. We developed a procedure for calculating the radial cell diameter based on specific Gompertz function combined with the “instantaneous tracheidogram” approach to estimate effectively seasonal cell production and timing of cell enlargement under climate control. Simulated cell number and cell size matched well with direct xylogenesis observations. The developed procedure demonstrate strong relationships between seasonal simulated growth rate and measured tracheid radial size (the average correlation is 0.64, $p$ < 0.01). A highly significant correlation ($p$ < 0.01) between simulated and observed cell profiles was obtained for 71% of the growing seasons over the period 1950–2011. The strong exponential relationship (R² = 0.67) was obtained between the day of the year (DOY) when cambial cell transfers into enlargement zone and simulated time intervals of cell enlargement. Based on the strong exponential relationship it was possible to reproduce the basic pattern of the observed tracheidograms over 1950–2011 with a systematic overestimation of final cell sizes at the beginning of the growing season, which will be possible to eliminate by using more anatomical data (trees) and longer period. The proposed approach of simulating intra-annual cell dynamics (cell production) has a great potential for studying how climate affects tree-ring formation.     

VS-oscilloscope: A new tool to parameterize tree radial growth based on climate conditions

It is generally assumed in dendroecological studies that annual tree-ring growth is adequately determined by a linear function of local or regional precipitation and temperature with a set of coefficients that are temporally invariant. However, various researchers have maintained that tree-ring records are the result of multivariate, often nonlinear biological and physical processes. To describe critical processes linking climate variables with tree-ring formation, the process-based tree-ring Vaganov–Shashkin model (VS-model) was successfully used. However, the VS-model is a complex tool requiring a considerable number of model parameters that should be re-estimated for each forest stand. Here we present a new visual approach of process-based tree-ring model parameterization (the so-called VS-oscilloscope) which allows the simulation of tree-ring growth and can be easily used by researchers and students. The VS-oscilloscope was tested on tree-ring data for two species (Larix gmeliniiand Picea obovata) growing in the permafrost zone of Central Siberia. The parameterization of the VS-model provided highly significant positive correlations (p < 0.0001) between simulated growth curves and original tree-ring chronologies for the period 1950–2009. The model outputs have shown differences in seasonal tree-ring growth between species that were well supported by the field observations. To better understand seasonal tree-ring growth and to verify the VS-model findings, a multi-year natural field study is needed, including seasonal observation of the thermo-hydrological regime of the soil, duration and rate of tracheid development, as well as measurements of their anatomical features.     

A Landscape and Climate Data Logistic Model of Tsetse Distribution in Kenya

Background

Trypanosoma spp, biologically transmitted by the tsetse fly in Africa, are a major cause of illness resulting in both high morbidity and mortality among humans, cattle, wild ungulates, and other species. However, tsetse fly distributions change rapidly due to environmental changes, and fine-scale distribution maps are few. Due to data scarcity, most presence/absence estimates in Kenya prior to 2000 are a combination of local reports, entomological knowledge, and topographic information. The availability of tsetse fly abundance data are limited, or at least have not been collected into aggregate, publicly available national datasets. Despite this limitation, other avenues exist for estimating tsetse distributions including remotely sensed data, climate information, and statistical tools.

Methodology/Principal Findings

Here we present a logistic regression model of tsetse abundance. The goal of this model is to estimate the distribution of tsetse fly in Kenya in the year 2000, and to provide a method by which to anticipate their future distribution. Multiple predictor variables were tested for significance and for predictive power; ultimately, a parsimonious subset of variables was identified and used to construct the regression model with the 1973 tsetse map. These data were validated against year 2000 Food and Agriculture Organization (FAO) estimates. Mapcurves Goodness-Of-Fit scores were used to evaluate the modeled fly distribution against FAO estimates and against 1973 presence/absence data, each driven by appropriate climate data.

Conclusions/Significance

Logistic regression can be effectively used to produce a model that projects fly abundance under elevated greenhouse gas scenarios. This model identifies potential areas for tsetse abandonment and expansion.     

dendroTools: R package for studying linear and nonlinear responses between tree-rings and daily environmental data

We introduce in this paper the dendroTools R package for studying the statistical relationships between tree-ring parameters and daily environmental data. The core function of the package is daily_response(), which works by sliding a moving window through daily environmental data and calculating statistical metrics with one or more tree ring proxies. Possible metrics are correlation coefficient, coefficient of determination and adjusted coefficient of determination. In addition to linear regression, it is possible to use a nonlinear artificial neural network with the Bayesian regularization training algorithm (brnn). dendroTools provides the opportunity to use daily climate data and robust nonlinear functions for the analysis of climate-growth relationships. Models should thus be better adapted to the real (continuous) growth of trees and should gain in predictive capabilities. The dendroTools R package is freely available in the CRAN repository. The functionality of the package is demonstrated on two examples, one using a mean vessel area (MVA) chronology and one a traditional tree-ring width (TRW).     

Responses of radial growth and stable carbon isotopes to climate in the northern Tianshan Mountains

Climate change has profound effects on forest ecosystems. Schrenk spruce (P. schrenkiana) is a natural conifer species endemic to the arid inland areas of Asia. In this study, the relationship between tree-ring parameters of P. schrenkiana and major meteorological factors were analyzed, and the main limiting factors for tree radial growth and stable carbon isotope fractionation were explored. Our results indicate that moisture stress before and during the growing season have an important influence on radial growth of P. schrenkiana, especially, the correlation coefficient between tree-ring width and vapor pressure deficit (VPD) from previous August to current July is as high as −0.622 (n = 51, p < 0.01). Collinearity analysis further supports the conclusion that the limiting factor for the radial growth of P. schrenkiana is moisture. Although the correlation analysis results show that the tree-ring δ¹³C_corr is significantly positively correlated with sunshine duration (SD), additional analysis based on first order difference variables suggests that the climate factor may not be the only limiting factor for the stable carbon isotope fractionation of tree rings in the Sayram Lake Basin. This lays the foundation for the assessment of forest management practices and carbon sink capacity in light of future climate change.     

Growth ring response of two Atlantic Forest tree species pre- and post-dam operation in Southern Brazil

Hydropower plants are important sources of renewable energy, but the climatic impacts of their constructions remain poorly explored. Considering that tree growth analysis is a useful tool to identify environmental impacts, this study aimed at using climate records and tree-ring chronologies to understand possible local climate changes caused by the construction of a hydropower plant in the 1980s in the State of Paraná, Southern Brazil. Historical climatic data were obtained from the local meteorological station and surrounding municipalities and analyzed using ANOVA and means tests. The Pettitt test was additionally used to identify change-points in the meteorological data. Wood samples from a total of 60 trees from Araucaria angustifolia (Bertol.) Kuntze (Araucariaceae) and Cedrela fissilis Vell. (Meliaceae) were collected, and tree-ring chronologies were built using dendrochronological standard procedures. Chronologies for A. angustifolia and C. fissilis represented time periods from 1800 to 2016 and 1899–2015, respectively. Tree-ring growth responses to climatic variables were evaluated by adjusting generalized mixed linear models and the Spearman correlations. Our results evidenced that the hydropower plant altered the local climate, mostly influencing the hydrological cycle by increasing local rainfall, with monthly rain volumes being statistically higher than in other meteorological stations. Significant responses in the growth of A. angustifolia were found to be associated with the water level increase caused by the dam and of C. fissilis due to the increase in cloud cover.     

Consistency of growth response to precipitation by Pseudopiptadenia contorta in two Atlantic Forest remnants

Tree growth sensitivity to climate can vary over space and time. This variability generates inconsistency in growth response to climate, which makes it difficult to assess the effects of past climate and global climate change on tree growth. A previous short-term study of Pseudopiptadenia contorta found a consistent growth response to climate in distinct locations, which raises the question, is the growth response of P. contorta to climate consistent over the long-term? We aimed to assess whether there is a common pattern of variation in tree-ring width, build tree-ring width chronologies, and verify the consistency of the climate-growth response of P. contorta in two Atlantic Forest remnants. Wood samples were collected in Reserva Biológica de Poço das Antas (RBPA) and Reserva Biológica de Tinguá (RBT) in the state of Rio de Janeiro, Brazil. Conventional dendrochronology methods were used for cross-dating, to build chronologies and to assess the climate-growth relationship. A common growth pattern was detected for P. contorta, and two tree-ring width chronologies were constructed. A congruent growth response was found for trees of RBPA and RBT to annual and spring precipitation as well as precipitation in the rainy months. Other climate-growth relationships were detected with other precipitation and temperature variables. Considering that P. contorta is a widespread species, occurring in other Brazilian biomes and forest formations, it is a promising model for developing further dendrochronological research including regional networks of replicated site chronologies, which could facilitate the reconstruction of historical climatic series and predictions of future impacts of climate change in tropical areas.     

Age-related climate sensitivity in Pinus Edulis at Dinosaur National Monument,Colorado, USA

The influence of tree age on climate sensitivity is of central importance in dendrochronology. Recent research has highlighted the disparate nature of age-dependent growth responses across species and geographic locations. We compared growth sensitivity and the influence of climate in Pinus edulis (Piñon) of varying ages at Dinosaur National Monument (DINO, northwestern Colorado, USA. Piñon is a particularly good species for this study because of its long life-span and climate sensitivity, and the DINO site is at the northern extreme of the current distribution. We evaluated changes in climate-growth relationships in piñon using total ring-width measurements and running averages of chronology statistics, mean sensitivity (MS) and coefficient of variation (CV), and we investigated growth response to climate variability as trees age. These measures indicated initial low growth sensitivity, increasing as trees reached mid-life stages, approximately 200–250 years, then relatively constant sensitivity from 250-800+ years. First order partial auto correlation (PAC1) declined throughout the life stages of piñon at DINO. The trend in declining autocorrelation leads to higher MS values in the older age classes. Greater year to year variation indicates less persistence in the study population, hence lower autocorrelation.We investigated the degree to which this relationship could be explained by the summer Palmer Drought Severity Index (PDSI) and whether this relationship varied with tree age. The strength of the tree-ring growth response to PDSI was at a maximum during the first two centuries of growth (R² = 0.54). between two and six centuries (R² = 0.48), after which we detected a decline in the sensitivity of tree growth to PDSI with increasing age (R² = 0.41). This study adds to the literature on age-related climate sensitivity in trees; our findings indicate that age-related changes in climate-tree-ring growth responses should be considered when climate variables are reconstructed from tree-ring width chronologies, and specifically from Pinus edulis.     

Detrending climate data prior to climate–growth analyses in dendroecology: A common best practice?

Tree growth varies closely with high–frequency climate variability. Since the 1930s detrending climate data prior to comparing them with tree growth data has been shown to better capture tree growth sensitivity to climate. However, in a context of increasingly pronounced trends in climate, this practice remains surprisingly rare in dendroecology. In a review of Dendrochronologia over the 2018–2021 period, we found that less than 20 % of dendroecological studies detrended climate data prior to climate-growth analyses. With an illustrative study, we want to remind the dendroecology community that such a procedure is still, if not more than ever, rational and relevant. We investigated the effects of detrending climate data on climate–growth relationships across North America over the 1951–2000 period. We used a network of 2536 tree individual ring-width series from the Canadian and Western US forest inventories. We compared correlations between tree growth and seasonal climate data (Tmin, Tmax, Prec) both raw and detrended. Detrending approaches included a linear regression, 30-yr and 100-yr cubic smoothing splines. Our results indicate that on average the detrending of climate data increased climate–growth correlations. In addition, we observed that strong trends in climate data translated to higher variability in inferred correlations based on raw vs. detrended climate data. We provide further evidence that our results hold true for the entire spectrum of dendroecological studies using either mean site chronologies and correlations coefficients, or individual tree time series within a mixed-effects model framework where regression coefficients are used more commonly. We show that even without a change in correlation, regression coefficients can change a lot and we tend to underestimate the true climate impact on growth in case of climate variables containing trends. This study demonstrates that treating climate and tree-ring time series “like-for-like” is a necessary procedure to reduce false negatives and positives in dendroecological studies. Concluding, we recommend using the same detrending for climate and tree growth data when tree-ring time series are detrended with splines or similar frequency-based filters.     

The influence of the de Vries (∼ 200-year) solar cycle on climate variations: Results from the Central Asian Mountains and their global link

Long-term climatic changes related to solar forcing were examined using millennium-scale palaeoclimatic reconstructions from the Central Asian mountain region, i.e. summer temperature records for the Tien Shan mountains and precipitation records for the Tibetan Plateau. The reconstructions were based on juniper tree-ring width records, i.e. Juniperus turkestanica for the Tien Shan and Sabina przewalskii for the Tibetan Plateau. The data were processed using spectral and wavelet analysis and filtered in the frequency range related to major solar activity periodicities. The results obtained for various tree-ring chronologies indicate palaeoclimatic oscillations in the range of the de Vries (～ 210-year) solar cycles through the last millennium.The quasi-200-year variations revealed in the palaeoclimatic reconstructions correlate well (R² = 0.58–0.94) with solar activity variations (Δ¹⁴C variations). The quasi-200-year climatic variations have also been detected in climate-linked processes in Asia, Europe, North and South America, Australia, and the Arctic and Antarctica. The results obtained point to a pronounced influence of solar activity on global climatic processes.Analysis has shown that climate response to the long-term global solar forcing has a regional character. An appreciable delay in the climate response to the solar signal can occur (up to 150 years). In addition, the sign of the climate response can differ from the solar signal sign. The climate response to long-term solar activity variations (from 10s to 1000s years) manifests itself in different climatic parameters, such as temperature, precipitation and atmospheric and oceanic circulation. The climate response to the de Vries cycle has been found to occur not only during the last millennia but also in earlier epochs, up to hundreds of millions years ago.     

Climate–growth analysis of Qilian juniper across an altitudinal gradient in the central Qilian Mountains, northwest China

In the context of global warming, it is of high importance to assess the influence of climatic change and geographic factors on the radial growth of high-elevation trees. Using tree-ring data collected from four stands of Qilian juniper (Juniperus przewalskii Kom.) across an altitudinal gradient in the central Qilian Mountains, northwest China, we compared the radial growth characteristics and climate–growth relationships at different elevations. Results indicated that there was little difference in the tree-ring parameters of the four chronologies. Correlation analyses both for unfiltered and 10-year high-passed data of monthly climatic variables and chronologies were presented to investigate the climatic forcing on tree growth, and results revealed that the correlation patterns were consistent among the four sites, especially for high-passed data. We employed the principal components analysis method to obtain the first principal component (PC1) of the four chronologies and computed the correlations between PC1 and climate factors. The PC1 correlated significantly with winter (November–January) temperature, prior August and current May temperature, and precipitation in the previous September and current January and April, indicating that tree growth in this region was mainly limited by cold winter temperature and drought in early growing season and prior growing season (prior August and September). However, the climate–growth relationships were unstable; with an increase in temperature, the sensitivity of tree growth to temperature had decreased over the past few decades. Considering the instability of the climate–growth relationships, climate reconstructions based on tree rings in the study area should be approached with more caution.     

Comparison of an optimal regression method for climate reconstruction with the compare_methods() function from the dendroTools R package

The selection of a regression technique for climate reconstruction may have an important impact on reconstructed values. In this paper, we introduce the compare_methods() function from the dendroTools R package. This function compares different regression algorithms and returns validation results for each. In addition to mean validation metrics and ranks derived from these, transfer functions should have a key role in the evaluation of different regression algorithms. These are also returned as the output of compare_methods(). Our methodology is introduced on two case studies, one using a mean vessel area (MVA) chronology and one using a standardised tree-ring width (TRW) chronology. The nonlinear machine learning methods compared in our study provided relatively small (if any) improvements in terms of explaining climatic variance. However, they do offer different treatments of extreme values, and if providing more plausible climate reconstructions, this could make them a useful tool for climate reconstruction. We propose the use of the compare_methods() function as a standard methodological check before performing climate reconstruction.     

Effectiveness of land use and disturbance measures,compared to climate,soil, and topography,for modeling relative abundances of tree species in the eastern United States

Environmental factors control species distributions and abundances, but effectiveness of land use and disturbance variables for modeling species generally is unknown compared to climate, soil, and topography variables. Therefore, I used predictor variables from categories of 1) land use and disturbance, 2) climate, and 3) soil, topography, and wind speed to model the relative abundances (i.e., percentage of all trees) of 65 common tree species in the eastern United States, with a contrast to presence-absence models of species distributions. First, I modeled variables within each category to identify the five most important variables. Then, I combined variables from each category to isolate most important variables, based on five model combinations of input variables from each category, ranging from one (i.e., three total) to five (i.e., 15 total) variables. From the five models of combined categories for each tree species, I identified the model with the greatest R² value. Overall, climate variables were most important for tree species models with one and two input variables from each category, but land use and disturbance variables were most important for models with three to five input variables from each category. Although a range of R² values occurred by species and number of input model variables, 32 species had best models with greatest R² values of 0.50 to 0.81. For all best species models, the most important variables were temperature of the warmest quarter, historical fire return interval for all fires, agricultural area during years 1850 to 1997, and precipitation of the driest month. Current land cover classes, which are accessible and the most commonly modeled land use variables, were not important for modeling tree species abundances or distributions. Climate variables were most important for modeling species distributions. Results support the concept that while climate sets soft boundaries on distributions, relative abundances within distributions are affected by other filters. Future modeling may establish other important land use and disturbance variables, or refinements within the important variables of historical fire return interval and agricultural area over time, advancing integration of both land use and climate variables into studies.