Mark J. Ducey

Legacy of fire slows carbon accumulation in Amazonian forest regrowth

Amazonian farmers and ranchers use fire to clear land for agriculture and pasture as part of extensive land-use strategies that have deforested 500 000 km2 over the past 25 years. Ash from burning biomass fertilizes crops and pastures, but declining productivity often occurs after a few years, generally leading to land abandonment and further clearing. Subsequent forest regrowth partially offsets carbon emissions from deforestation, but is often repeatedly cleared and burned. In the first quantitative, basin-wide assessment of the effect of repeated clearing and burning on forest regrowth, our analysis of data from 93 stands at nine locations across the region indicates that stands with a history of five or more fires suffer on average a greater than 50% reduction in carbon accumulation. In the absence of management interventions, Amazonian landscapes dominated by this pronounced legacy of fire are apt to accumulate very little carbon and will remain highly susceptible to recurrent burning.

Potential biomass accumulation in Amazonian regrowth forests.

Biomass accumulation in the secondary forests of abandoned pastures and slash-and-burn agricultural fallows is an important but poorly constrained component of the regional carbon budget for the Brazilian Amazon. Using empirical relationships derived from a global analysis, we predicted potential aboveground biomass accumulation (ABA) for the regions regrowth forests based on soil texture and climate data. For regrowth forests on nonsandy soils, the globally derived relationship provided a nearly unbiased linear predictor of Amazonian validation data consisting of 66 stands at seven sites; there was no significant difference between stands that regrew following use as pasture land and those that regrew following slash-and-burn agriculture. For regrowth forests on nonsandy soil, the 1 sigma error range of our ABA model was 58%–171% for the Amazonian validation data. For regrowth forests on sandy soils, the validation data were limited to 19 stands at one site, and the globally derived relationship was substantially biased multiplicatively and nonlinearly. Hence we developed a regional refinement by adding to our validation data ABA values from the two Amazonian sites with sandy soil that had previously been included in the global analysis. Based on a conservative jackknife goodness-of-fit assessment (leaving out one site at a time), we calculated a 1 sigma error range of 42%–158% for our sandy soil Amazonian regrowth forest ABA model. We present our predictions of potential regrowth forest ABA as a set of 0.5° resolution maps for the region at 5, 10, and 20 years following abandonment.

A fuzzy set approach to the problem of sustainability

Evaluating sustainability in forest management requires decision makers to consider multiple, conflicting definitions in an environment of risk, uncertainty, and incomplete or non-quantitative information. Zadehs fuzzy set theory provides a rigorous, flexible approach to the problem of defining and evaluating sustainability and comparing alternative scenarios. We discuss how fuzzy set concepts can be used to better manage for goals such as sustainability, equity, and ecosystem health. We then show how a simple tabular technique using fuzzy sets can be used to compare management alternatives, incorporate multiple objectives, and identify knowledge gaps and areas of disagreement. The simplicity and flexibility of this technique provides needed support for more explicit, objective decisions.

Characterization of the structure, dynamics, and productivity of mixed-species stands: review and perspectives

The growth and yield of mixed-species stands has become an important topic of research since there are certain advantages of this type of forest as regards functions and services. However, the concepts and methods used to characterize mixed stands need to be understood, as well as harmonized and standardized. In this review we have compiled a set of measures, indices, and methods at stand level to characterize the structure, dynamics, and productivity of mixed stands, and we discuss the pros and cons of their application in growth and yield studies. Parameters for the characterization of mixed stand structure such as stand density, species composition, horizontal (intermingling) and vertical tree distribution pattern, tree size distribution, and age composition are described, detailing the potential as well as the constraints of these parameters for understanding resource capture, use, and efficiency in mixed stands. Furthermore, a set of stand-level parameters was evaluated to characterize the dynamics of mixed stands, e.g. height growth and space partitioning, self- and alien-thinning, and growth partitioning among trees. The deviations and changes in the behaviour of the analysed parameters in comparison with pure stand conditions due to inter-specific interactions are of particular interest. As regards stand productivity, we reviewed site productivity indices, the growth–density relationship in mixed stands as well as methods to compare productivity in mixed versus monospecific stands. Finally, we discuss the main problems associated with the methodology such as up-scaling from tree to stand level as well as the relevance of standardized measures and methods for improving forest growth and yield research in mixed stands. The main challenges are also outlined, especially the need for qualitatively sound data.

Assessment of JERS-1 SAR for monitoring secondary vegetation in Amazonia: I. Spatial and temporal variability in backscatter across a chrono-sequence of secondary vegetation stands in Rondonia

Quantification of the direct impact of land use in the tropics on net biotic carbon flux relies on estimates of rates of deforestation, pre- and post-disturbance biomass, and fate of the cleared land. While existing remote sensing applications are providing estimates of the rates of deforestation and the fate of the cleared land (pasture, croplands, or secondary vegetation), techniques for estimating biomass of natural systems with remote sensing are needed. Synthetic Aperture Radar (SAR) presents a unique opportunity for imaging tropical forests under most cloud conditions and potentially provides information on vegetation biomass. Models for estimating above-ground biomass from SAR data have been developed. In this paper we examine the temporal and spatial variability of mean normalized radar cross-section across a chrono-sequence of secondary vegetation stands and clearings in Rondonia, Brazil. We also assess the impact of the observed temporal and spatial variability in normalized radar cross-section on estimating biomass of secondary vegetation stands. Results indicate that, while quantitative estimates of biomass are not stable due to intrinsic texture, system noise, and environmental effects, JERS-1 data are still useful for categorizing relative differences in development of secondary vegetation stands. Merging Japanese Earth Resources Satellite 1 (JERS-1) SAR data with Landsat Thematic Mapper (TM) derived age information data provide improved characterization of clearings and secondary vegetation in Rondonia.

SPATIAL PATTERNS OF FOREST CHARACTERISTICS IN THE WESTERN UNITED STATES DERIVED FROM INVENTORIES

In the western United States, forest ecosystems are subject to a variety of forcing mechanisms that drive dynamics, including climate change, land-use/land-cover change, atmospheric pollution, and disturbance. To understand the impacts of these stressors, it is crucial to develop assessments of forest properties to establish baselines, determine the extent of changes, and provide information to ecosystem modeling activities. Here we report on spatial patterns of characteristics of forest ecosystems in the western United States, including area, stand age, forest type, and carbon stocks, and comparisons of these patterns with those from satellite imagery and simulation models. The USDA Forest Service collected ground-based measurements of tree and plot information in recent decades as part of nationwide forest inventories. Using these measurements together with a methodology for estimating carbon stocks for each tree measured, we mapped county-level patterns across the western United States. Because forest ecosystem properties are often significantly different between hardwood and softwood species, we describe patterns of each. The stand age distribution peaked at 60-100 years across the region, with hardwoods typically younger than softwoods. Forest carbon density was highest along the coast region of northern California, Oregon, and Washington and lowest in the arid regions of the Southwest and along the edge of the Great Plains. These results quantify the spatial variability of forest characteristics important for understanding large-scale ecosystem processes and their controlling mechanisms. To illustrate other uses of the inventory-derived forest characteristics, we compared them against examples of independently derived estimates. Forest cover compared well with satellite-derived values when only productive stands were included in the inventory estimates. Forest types derived from satellite observations were similar to our inventory results, though the inventory database suggested more heterogeneity. Carbon stocks from the Century model were in good agreement with inventory results except in the Pacific Northwest and part of the Sierra Nevada, where it appears that harvesting and fire in the 20th century (processes not included in the model runs) reduced measured stand ages and carbon stocks compared to simulations.

Forest community analysis and the point-centered quarter method.

The point-centered-quarter (PCQ) method has been applied in community analysis since the publication of the method nearly 50 years ago. This and other distance methods offer increased sampling efficiency over fixed-area plots (FAP), but have long been known to produce biased density estimates when plant distribution deviates from random spatial patterns. Spatial indices have been developed to quantify the direction of this bias when plant distributions are aggregated or evenly distributed. Its continued use, especially in community analysis, requires additional scrutiny in measurements of community structure. We measured 14 forest stands of varying age, elevation and disturbance regime using FAP and PCQ methods. Density estimates were biased, with the point-centered quarter method lower than fixed-area plot estimates when stems were aggregated and higher when stems were evenly spaced. In general the PCQ method underestimated species richness. The efficiency of the PCQ method makes it popular for ordination studies, although comparison of community structure varied from 18% to 90% similarity between the measurements of species basal area in the same stands using the two different methods. The bias observed in calculations of stem density, species abundance and community similarity indicate that use of the PCQ method should be approached with caution when used in community level analysis.

Representing uncertainty in silvicultural decisions: An application of the Dempster-Shafer theory of evidence.

Abstract Forest management decisions often must be made using sparse data and expert judgment. The representation of this knowledge in traditional approaches to decision analysis implies a precise value for probabilities or, in the case of Bayesian analysis, a precisely specified joint distribution for unknown parameters. The precision of this specification does not depend on the strength or weakness of the evidence on which it is based. This often leads to exaggerated precision in the results of decision analyses, and obscures the importance of imperfect information. Here, I suggest an alternative based on the Dempster–Shafer theory of evidence, which differs from conventional approaches in allowing the allocation of belief to subsets of the possible outcomes, or, in the case of a continuous set of possibilities, to intervals. The Dempster–Shafer theory incorporates Bayesian analysis as a special case; a critical difference lies in the representation of ignorance or uncertainty. I present examples of silvicultural decision-making using belief functions for the case of no data, sparse data, and adaptive management under increasing data availability. An approach based on the Dempster–Shafer principles can yield not only indications of optimal policies, but also valuable information about the level of certainty in decision-making.

Trade-associated pathways of alien forest insect entries in Canada.

Long-distance introductions of new invasive species have often been driven by socioeconomic factors, such that traditional “biological” invasion models may not be capable of estimating spread fully and reliably. In this study we present a new methodology to characterize and predict pathways of human-assisted entries of alien forest insects. We have developed a stochastic quantitative model of how these species may be moved with commodity flow through a network of international marine ports and major transportation corridors in Canada. The study makes use of a Canadian roadside survey database and data on Canadian marine imports, complemented with geo-referenced information on ports of entry, populated places and empirical observations of historical spread rates for invasive pests. The model is formulated as a probabilistic pathway matrix, and allows for quantitative characterization of likelihoods and vectors of new pest introductions from already or likely-to-be infested locations. We applied the pathway model to estimate the rates of human-assisted entry of alien forest insect species across Canada as well as cross-border transport to locations in the US. Results suggest a relatively low nationwide entry rate for Canada when compared to the US (0.338 new forest insect species per year vs. 1.89). Among Canadian urban areas, Greater Toronto and Greater Vancouver appear to have the highest alien forest insect entry potential, exhibiting species entry rates that are comparable with estimated rates at mid-size US urban metropolises.

Multistage point relascope and randomized branch sampling for downed coarse woody debris estimation

New sampling methods have recently been introduced that allow estimation of downed coarse woody debris using an angle gauge, or relascope. The theory behind these methods is based on sampling straight pieces of downed coarse woody debris. When pieces deviate from this ideal situation, auxillary methods must be employed. We describe a two-stage procedure where the relascope is used to select pieces of downed coarse woody debris in the first stage. If the pieces so chosen on the first stage have multiple branches and detailed estimates are required for the entire piece, then a second stage sample is advocated using the randomized branch technique. Both techniques are reviewed and an example is given examining possible surrogate variables for the second stage.