Coarse woody debris (CWD; i.e. downed limbs and boles) serves numerous ecosystem functions, which vary according to the degree of decay. CWD decay is often described using five categories based on readily observed physical characteristics ranging from freshly fallen (Class I) to advanced decay with little structural integrity (Class V). Though useful in categorizing downed wood in a forest, these categories do not necessarily provide information about time since death or the decay process. Dendrochronology can be used to assign death dates to CWD and begin to provide a temporal description of the decay process. We used standard dendrochronological techniques to determine the death dates of 94 CWD samples from five common hardwood taxa in southern Indiana. Across taxa, the time since death of Class I (1.4 ± 1.7 years; mean ± SD; least decayed class) was significantly shorter than Class II (5.2 ± 3.6 years), which was shorter than the more decayed classes (Class III: 11.5 ± 4.9, and Class IV: 11.2 ± 5.6 years). Within this general trend, time since death within a decay class varied greatly among taxa. Combining dendrochronology techniques with visual decay characteristics can improve our understanding of CWD's role and provide a more precise timeline for biomass and nutrient turnover within forested systems.

The Portainé mountain catchment, containing the Port Ainé ski resort (Lleida, Spanish Pyrenees), displays active erosional and depositional phenomena caused by periodic torrential floods. These events present a potential risk and incur significant economic losses. In ungauged remote catchments (like Portainé), trees might be the only paleohydrological source of information regarding past floods. Thus, we estimated the temporal and spatial distribution of torrential floods by dendrogeomorphological techniques to assess whether human impact (land-use changes and infrastructure works) affected their frequency and magnitude. One-hundred and sixty-six samples from 67 trees belonging to 10 different species were analyzed; past flood events of the last 50 years were identified by dating and relating evidence between them. Moreover, a detailed geomorphological study was performed and the available historical data compiled. Our multi-evidence analysis provides new insight into the occurrence of paleofloods. Changes in flood frequency since 2006, especially from 2008, suggest that the geomorphological equilibrium has been disturbed, coinciding with both major earthworks within the ski resort and intense but not extraordinary rainfall. This conclusion has important implications for land planning and the design of future projects in the mountain watersheds.

The São Francisco River basin is one of the most drought-prone regions of Brazil. Seasonally dry tropical forests (SDTF) are widely distributed in the basin and we developed a short chronology of Cedrela fissilis annual ring widths from SDTF fragments based on 89 cores from 44 trees dating from 1961 to 2015. The average correlation among all radii (RBAR) is 0.52. The tree-ring chronology is correlated with wet season precipitation totals, must strongly and consistently near the beginning of the wet season. The spatial pattern of correlation covers most of the southern portion of the Brazilian Drought Polygon and the sub-basins of the two largest tributaries of the São Francisco River, in some areas exceeding r = 0.60. The chronology is also correlated with total annual discharge of the Rio São Francisco River measured at Barra (r = 0.489; 1961–2015), which is very promising in a country that generates two thirds of its electricity from hydroelectric power plants, particularly if this short chronology can be extended with trees exceeding 150-years old known to still exist in the region.

Despite the widespread use of ponderosa pine as an important hydroclimate proxy, we actually understand very little about its climate response in the Northern Rockies. Here, we analyze two new ponderosa pine chronologies to investigate how climate influences annual growth. Despite differences in precipitation amount and timing and large elevation differences (1820 m versus 1060 m), ring width at both sites was strongly driven by water availability. The mid-elevation, water-limited site responded well to previous fall precipitation whereas the wetter, high-elevation site responded to growing season precipitation and temperature. When precipitation and temperature were simultaneously accounted for using the standardized precipitation evapotranspiration index, ring-width response between sites converged and appeared nearly identical. Water stress drove the timing of ponderosa pine growth by a combination of factors such as strong water dependence, and determinate growth physiology, as indicated by lag-1 autocorrelation. When analyzing response to single-month climate variables, precipitation from growing-season months dominates. When we examined seasonal variables, climate from the previous year became more important. Temporal fidelity of the climatic response at both sites maintained significance across the historical record, although the relationship weakened at the low-elevation site. The collection of new tree-ring data sets such as these for central Idaho improves our understanding of ponderosa pine growth response to climate.

Here we examine climatic influences on inter-annual variation in latewood tree growth (i.e. ring-width indices, RWI_LW) and stable-carbon isotope discrimination (Δ¹³C_LW) from 1950 to 2013 at two SNOTEL snowpack monitoring sites in the Oregon Cascade Mountains. Douglas-fir and mountain hemlock trees were sampled at the lower and upper elevation sites where annual peak snow water equivalent (SWE) averaged 467 and 1128 mm, respectively. RWI_LW chronologies were poorly correlated among sites/species (r = 0.23, P = 0.063) and neither exhibited strong correlations with monthly or seasonal climate variables. By contrast, Δ¹³C_LW chronologies were significantly correlated (r = 0.69, P < 0.001) and exhibited stronger climate responses. Multiple regression analyses identified summertime maximum temperature (T_max) and/or vapor pressure deficit (VPD) as the primary drivers of Δ¹³C_LW. Secondary influences included summertime precipitation, specific humidity, cloud cover, and SWE from the previous fall and winter. Overall, our findings suggest that Cascade mixed conifer forests will become increasingly drought stressed as rising temperatures cause progressively diminished snowpacks. Moreover, our Δ¹³C_LW records also provide a proof of concept showing strong potential to expand summertime T_max reconstructions to other snowy, montane locations.

Global climate change will alter forests by shifting species ranges, which has implications for their ecological functions. Annual tree-ring widths and wood density are useful proxies for carbon cycle studies across a range of species. Here, using a dendroecological approach we sought to understand the carbon accumulation rates of two representative pine species growing on contrasting wet (P. arizonica) and dry (P. cembroides) sites and reveal how such species cope with climate variability. Although the rate of carbon gain was not significantly different across sites, we found that variations in carbon accumulation responded differently to specific hydroclimate drivers, site conditions, or to functional features of each species, which are still to be explored. Overall, annual carbon accumulation (C) was less sensitive to climate variability than ring width and wood density. Annual C was more sensitive to rainfall in the cold season (P. arizonica) and to the start of spring (both species). Our species-specific approach provided a suitable basis for modeling projections in the long-term carbon balance in these forests. Using species-specific tree-ring data has the potential to yield better estimations given that tree rings reflect fine spatial and temporal resolution, thereby reducing the uncertainty in forest carbon budgets.

Machine learning (ML) is a widely unexplored field in dendroclimatology, but it is a powerful tool that might improve the accuracy of climate reconstructions. In this paper, different ML algorithms are compared to climate reconstruction from tree-ring proxies. The algorithms considered are multiple linear regression (MLR), artificial neural networks (ANN), model trees (MT), bagging of model trees (BMT), and random forests of regression trees (RF). April-May mean temperature at a Quercus robur stand in Slovenia is predicted with mean vessel area (MVA, correlation coefficient with April-May mean temperature, r = 0.70, p < 0.001) and earlywood width (EW, r = –0.28, p < 0.05). Similarly, June-August mean temperature is predicted with stable carbon isotope (δ¹³C, r = 0.72, p < 0.001), stable oxygen isotope (δ¹⁸O, r = 0.32, p < 0.05) and tree-ring width (TRW, r = 0.11, p > 0.05 (ns)) chronologies. The predictive performance of ML algorithms was estimated by 3-fold cross-validation repeated 100 times. In both spring and summer temperature models, BMT performed best respectively in 62% and 52% of the 100 repetitions. The second-best method was ANN. Although BMT gave the best validation results, the differences in the models’ performances were minor. We therefore recommend always comparing different ML regression techniques and selecting the optimal one for applications in dendroclimatology.