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Estimation of the amount of carbon stored in forests is a key challenge for understanding the global carbon cycle, one which remote sensing is expected to help address. However, estimation of carbon storage in moderate to high biomass forests is difficult for conventional optical and radar sensors. Lidar (light detection and ranging) instruments measure the vertical structure of forests and thus hold great promise for remotely sensing the quantity and spatial organization of forest biomass. In this study, we compare the relationships between lidar-measured canopy structure and coincident field measurements of above-ground biomass at sites in the temperate deciduous, temperate coniferous, and boreal coniferous biomes. A single regression for all three sites is compared with equations derived for each site individually. The single equation explains 84% of variance in above-ground biomass (P < 0.0001) and shows no statistically significant bias in its predictions for any individual site.

Habitat distribution models are increasingly used to predict the potential distributions of invasive species and to inform monitoring. However, these models assume that species are in equilibrium with the environment, which is clearly not true for most invasive species. Although this assumption is frequently acknowledged, solutions have not been adequately addressed. There are several potential methods for improving habitat distribution models. Models that require only presence data may be more effective for invasive species, but this assumption has rarely been tested. In addition, combining modeling types to form \"ensemble\" models may improve the accuracy of predictions. However, even with these improvements, models developed for recently invaded areas are greatly influenced by the current distributions of species and thus reflect near- rather than long-term potential for invasion. Larger scale models from species' native and invaded ranges may better reflect long-term invasion potential, but they lack finer scale resolution. We compared logistic regression (which uses presence/absence data) and two presence-only methods for modeling the potential distributions of three invasive plant species on the Olympic Peninsula in Washington, USA. We then combined the three methods to create ensemble models. We also developed climate envelope models for the same species based on larger scale distributions and combined models from multiple scales to create an index of near- and long-term invasion risk to inform monitoring in Olympic National Park (ONP). Neither presence-only nor ensemble models were more accurate than logistic regression for any of the species. Larger scale models predicted much greater areas at risk of invasion. Our index of near- and long-term invasion risk indicates that <4% of ONP is at high near-term risk of invasion while 67–99% of the Park is at moderate or high long-term risk of invasion. We demonstrate how modeling results can be used to guide the design of monitoring protocols and monitoring results can in turn be used to refine models. We propose that, by using models from multiple scales to predict invasion risk and by explicitly linking model development to monitoring, it may be possible to overcome some of the limitations of habitat distribution models.

Placing an upper bound to carbon (C) storage in forest ecosystems helps to constrain predictions on the amount of C that forest management strategies could sequester and the degree to which natural and anthropogenic disturbances change C storage. The potential, upper bound to C storage is difficult to approximate in the field because it requires studying old-growth forests, of which few remain. In this paper, we put an upper bound (or limit) on C storage in the Pacific Northwest (PNW) of the United States using field data from old-growth forests, which are near steady-state conditions. Specifically, the goals of this study were: (1) to approximate the upper bounds of C storage in the PNW by estimating total ecosystem carbon (TEC) stores of 43 old-growth forest stands in five distinct biogeoclimatic provinces and (2) to compare these TEC storage estimates with those from other biomes, globally. Finally, we suggest that the upper bounds of C storage in forests of the PNW are higher than current estimates of C stores, presumably due to a combination of natural and anthropogenic disturbances, which indicates a potentially substantial and economically significant role of C sequestration in the region. Results showed that coastal Oregon stands stored, on average, 1127 Mg C/ha, which was the highest for the study area, while stands in eastern Oregon stored the least, 195 Mg C/ha. In general, coastal Oregon stands stored 307 Mg C/ha more than coastal Washington stands. Similarly, the Oregon Cascades stands stored 75 Mg C/ha more, on average, than the Washington Cascades stands. A simple, area-weighted average TEC storage to 1 m soil depth (TEC100) for the PNW was 671 Mg C/ha. When soil was included only to 50 cm (TEC50), the area-weighted average was 640 Mg C/ha. Subtracting estimates of current forest C storage from the potential, upper bound of C storage in this study, a maximum of 338 Mg C/ha (TEC100) could be stored in PNW forests in addition to current stores.

Floods in forested mountain landscape such as the major flood in the Pacific Northwest in Feb 1996 brings mechanical damage to streams and riparian habitats. This is found to be caused by the transport of soil, sediment and large logs down the steep hillslopes and through steam channels.

Production and mortality are the component processes that together determine the biomass dynamics of forests. Due to the significant role of forests in the global carbon cycle, it is important to assess how these two processes affect the maximum biomass attained by forests, as well as the dynamics leading up to and following peak biomass. We address these questions for two sets of plots in Picea sitchensis‐Tsuga heterophylla forest on the northern Oregon coast that originated from a catastrophic wildfire in the 1840s, using new data on dynamics of live trees and stocks of coarse woody debris (CWD). The set of plots closest to the ocean and occupying steeper, more dissected terrain with areas of thin soils has lower biomass, lower net primary production (NPP) of bole wood and higher tree mortality as a fraction of standing biomass. The two sets of plots have similar CWD levels, most of which has accumulated in the last 25 yr. The present disparity in biomass between the two sets of plots appears to be the result of lower NPP on the low‐biomass plots for the entire 140+ yr history of the forest. Over the 58 yr that the high‐biomass plots have been measured (from stand age 85 to 143 yr), NPP of bole wood has declined by 41%. Only ca. 6% of this decline can be accounted for by an increase in maintenance respiration of woody tissues. For both sets of plots relative constancy of biomass in the long term appears likely, due to a short time lag in tree regeneration, asynchronous tree mortality and little overall decline in NPP of bole wood in recent decades. However, since tree mortality as a fraction of standing biomass is higher on the low‐biomass plots, and NPP of bole wood is slightly lower, the difference in biomass between the two sets of plots should increase if current rates of production and mortality persist.

Canopy macrolichens were sampled using the \"litter pickup\" technique in four forest stands in the mixed conifer forests of Sequoia/Kings Canyon National Park. The purpose was to provide a basis for assessing lichen abundance trends in permanent forest plots, and to compare differences in lichen communities between four forest types typical of the southern Sierra Nevada. Each stand was characterized by a different conifer: sugar pine (Pinus lambertiana Dougl.), white fir (Abies concolor Gord. & Glend.), giant Sequoia (Sequoiadendron giganteum (Lindl.) Buchh.) and Jeffrey pine (Pinus jeffreyi Grev. & Balf.). The standing crop of lichen litterfall was estimated at 33.6 kg/ha, 14.8 kg/ha, 6.9 kg/ha, and 7.6 kg/ha respectively. Seven macrolichens were present in the litterfall, in decreasing order of overall abundance: Letharia vulpina (L.) Hue, Hypogymnia imshaugii Krog, L. columbiana (Nutt.) J. W. Thomson, Bryoria fremontii (Tuck.) Brodo & D. Hawksw. and Melanelia exasperatula (Nyl.) Essl., M. subolivacea (Nyl.) Essl., and Lobaria (Schreber) Hoffm. sp. A single factor ANO VA indicated that L. vulpina was equally distributed throughout the four stands, while H. imshaugii and L. columbiana were not. H. imshaugii was the most abundant lichen in the White Fir stand, although L. vulpina closely approximated it there. L. vulpina was most abundant in the Sugar Pine, Giant Sequoia and Jeffrey Pine stands, and all other lichens were much less abundant. A complex of factors explains the differences in lichen abundance; stand density, stand structure, and tree species composition appear most important, although site environmental conditions cannot be ruled out due to the lack of replication and small sample size in this study. The White Fir and Sugar Pine stands had 2–3 times the tree density as the Giant Sequoia and Jeffrey Pine stands. Giant sequoia and incense cedar (Calocedrus decurrens (Torr.) Florin) shed bark and therefore do not have abundant epiphytes on branches and tree boles. White fir appears to have a generally positive effect on lichen abundance, except in extremely dense stands. The abundance of H. imshaugii and L. columbiana were highly correlated with abundance of sugar pine. Although species diversity is low, standing crop of lichen litterfall is high, and may exceed many other forests in North America.

A general conceptual model of vegetation based on hierarchy theory is presented. The model emphasizes that prediction of vegetation requires consideration of both mechanisms of vegetation change and the constraints within which it occurs. The mechanisms of vegetation change are the responses to and effects upon their surroundings of individual plants. The most general constraints upon vegetation are aspects of the environment not affected by vegetation over successional time, and the pool of species within dispersal range. Examples of such environmental factors include macroclimate and soil parent material. In some cases, vegetation may alter important labile environmental factors such as soil nutrient and water availability. Some vegetation compositions appear to be resistant to changes in the general constraints. Due to both sources, there are multiple possible vegetation compositions given the same general constraints. Disturbance is defined as an abrupt change in the constraints on the vegetation resulting in a change in the vegetation's state or dynamics. Both the recognition of disturbance and the distinction between independent and labile environmental factors depend on the spatial and temporal scale of observation. For example, a particular wildfire at a given stand may be a disturbance, whereas at a larger scale of observation the same event may contribute to the wildfire regime, part of the constraints at that scale. Similarly, levels of soil organic matter may constrain vegetation over short time scales, due to influencing availability of water and nutrients. Over long time scales, the vegetation itself is a primary determinant of soil organic matter content. This model contains elements of both the initial, holistic theory of vegetation and recent, reductionistic approaches. It reiterates the need to consider both mechanisms and constraints, stressed by contemporary and earlier workers. Hierarchy theory provides new insights concerning sufficient conditions for prediction, possible limits on predictability, and appropriate research strategy.