Sound forest policy and management decisions to mitigate rising atmospheric CO 2 depend upon accurate methodologies to quantify forest carbon pools and fluxes over large tracts of land. LiDAR remote sensing is a rapidly evolving technology for quantifying aboveground biomass and thereby carbon pools; however, little work has evaluated the efficacy of repeat LiDAR measures for spatially monitoring aboveground carbon pools through time. Our study objective was therefore to evaluate the use of discrete return airborne LiDAR for quantifying biomass change and carbon flux from repeat field and LiDAR surveys. We collected LiDAR data in 2003 and 2009 across~20,000 ha of an actively managed, mixed conifer forest landscape in northern Idaho. The Random Forest machine learning algorithm was used to impute aboveground biomass pools of trees, saplings, shrubs, herbaceous plants, coarse and fine woody debris, litter, and duff using field-based forest inventory data and metrics derived from the LiDAR collections. Separate predictive tree aboveground biomass models were developed from the 2003 and 2009 field and LiDAR data, and biomass change was estimated at the plot, pixel, and landscape levels by subtracting 2003 predictions from 2009 predictions. Traditional stand exam data were used to independently validate 2003 and 2009 tree aboveground biomass predictions and tree aboveground biomass change estimates at the stand level. Over this 6-year period, we found a mean increase in tree aboveground biomass due to forest growth across the non-harvested portions of 4.1 Mg/ha/yr. We found that 26.3% of the landscape had been harvested during this time period which outweighed growth at the landscape level, resulting in a net tree aboveground biomass change of − 5.7 Mg/ha/yr, and − 2.3 Mg/ha/yr in total aboveground carbon, summed across all the aboveground biomass pools. Change in aboveground biomass was related to forest successional status; younger stands gained two-to threefold less biomass than did more mature stands. This result suggests that even the most mature forest stands are valuable carbon sinks, and implies that forest management decisions that include longer harvest rotation cycles are likely to favor higher levels of aboveground carbon storage in this system. A 30-fold difference in LiDAR sampling density between the 2003 and 2009 collections did not affect plot-scale biomass estimation. These results suggest that repeat LiDAR surveys are useful for accurately quantifying high resolution, spatially explicit biomass and carbon dynamics in conifer forests. Published by Elsevier Inc.

Hydrologic response to rainfall on fragmented or burnt hillslopes is strongly influenced by the ensuing connectivity of runoff and erosion processes. Yet cross-scale process connectivity is seldom evaluated in field studies owing to scale limitations in experimental design. This study quantified surface susceptibility and hydrologic response across point to hillslope scales at two degraded unburnt and burnt woodland sites using rainfall simulation and hydrologic modelling. High runoff (31–47 mm) and erosion (154–1893 g m–2) measured at the patch scale (13 m2) were associated with accumulation of fine-scale (0.5-m2) splash-sheet runoff and sediment and concentrated flow formation through contiguous bare zones (64–85% bare ground). Burning increased the continuity of runoff and sediment availability and yield. Cumulative runoff was consistent across plot scales whereas erosion increased with increasing plot area due to enhanced sediment detachment and transport. Modelled hillslope-scale runoff and erosion reflected measured patch-scale trends and the connectivity of processes and sediment availability. The cross-scale experiments and model predictions indicate the magnitude of hillslope response is governed by rainfall input and connectivity of surface susceptibility, sediment availability, and runoff and erosion processes. The results demonstrate the importance in considering cross-scale structural and functional connectivity when forecasting hydrologic and erosion responses to disturbances.

Comprehensive assessment of ecological change after fires have burned forests and rangelands is important if we are to understand, predict and measure fire effects. We highlight the challenges in effective assessment of fire and burn severity in the field and using both remote sensing and simulation models. We draw on diverse recent research for guidance on assessing fire effects on vegetation and soil using field methods, remote sensing and models. We suggest that instead of collapsing many diverse, complex and interacting fire effects into a single severity index, the effects of fire should be directly measured and then integrated into severity index keys specifically designed for objective severity assessment. Using soil burn severity measures as examples, we highlight best practices for selecting imagery, designing an index, determining timing and deciding what to measure, emphasising continuous variables measureable in the field and from remote sensing. We also urge the development of a severity field assessment database and research to further our understanding of causal mechanisms linking fire and burn severity to conditions before and during fires to support improved models linking fire behaviour and severity and for forecasting effects of future fires.

Wildland fire management has reached a crossroads. Current perspectives are not capable of answering interdisciplinary adaptation and mitigation challenges posed by increases in wildfire risk to human populations and the need to reintegrate fire as a vital landscape process. Fire science has been, and continues to be, performed in isolated “silos,” including institutions (e.g., agencies versus universities), organizational structures (e.g., federal agency mandates versus local and state procedures for responding to fire), and research foci (e.g., physical science, natural science, and social science). These silos tend to promote research, management, and policy that focus only on targeted aspects of the “wicked” wildfire problem. In this article, we provide guiding principles to bridge diverse fire science efforts to advance an integrated agenda of wildfire research that can help overcome disciplinary silos and provide insight on how to build fire-resilient communities.

Comparative phylogeographic studies have had mixed success in identifying common phylogeographic patterns among co-distributed organisms. Whereas some have found broadly similar patterns across a diverse array of taxa, others have found that the histories of different species are more idiosyncratic than congruent. The variation in the results of comparative phylogeographic studies could indicate that the extent to which sympatrically-distributed organisms share common biogeographic histories varies depending on the strength and specificity of ecological interactions between them. To test this hypothesis, we examined demographic and phylogeographic patterns in a highly specialized, coevolved community – Joshua trees (Yucca brevifolia) and their associated yucca moths. This tightly-integrated, mutually interdependent community is known to have experienced significant range changes at the end of the last glacial period, so there is a strong a priori expectation that these organisms will show common signatures of demographic and distributional changes over time. Using a database of >5000 GPS records for Joshua trees, and multi-locus DNA sequence data from the Joshua tree and four species of yucca moth, we combined paleaodistribution modeling with coalescent-based analyses of demographic and phylgeographic history. We extensively evaluated the power of our methods to infer past population size and distributional changes by evaluating the effect of different inference procedures on our results, comparing our palaeodistribution models to Pleistocene-aged packrat midden records, and simulating DNA sequence data under a variety of alternative demographic histories. Together the results indicate that these organisms have shared a common history of population expansion, and that these expansions were broadly coincident in time. However, contrary to our expectations, none of our analyses indicated significant range or population size reductions at the end of the last glacial period, and the inferred demographic changes substantially predate Holocene climate changes.

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