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Ongoing urban development has significant effects on ecosystems, including changes to land cover, environmental conditions, and species' distributions. These various impacts may have opposing or interacting effects on plant communities, making it difficult to predict how plant biodiversity will respond to urban development. A frequently cited conceptual framework predicts how urban development influences plant taxonomic, functional, and phylogenetic diversity by simplifying multiple coincident effects of urbanization into four primary filters of biodiversity: habitat transformation, fragmentation, the urban environment, and human preferences. Each filter prevents some plant species from persisting in urban areas while promoting others, but species introductions according to human preferences are expected to cause a net increase in biodiversity while the other filters limit diversity. In this study, we used structural equation modeling to test these predictions and examine the relative importance of each filter on the taxonomic, functional, and phylogenetic diversity of riparian forest plant species sampled along an urban-to-rural gradient in the Research Triangle area of North Carolina. Most diversity measures declined with urbanization, but some (e.g., functional Rao's Q) increased with urbanization. We found support for some of the predicted relationships between urbanization filters and biodiversity, as well as some unexpected relationships, including positive effects of urban environments. Overall, urban environments and human preferences were stronger predictors than habitat transformation and fragmentation. Our approach could be used to test a general framework predicting the effects of urbanization on plant diversity across multiple cities and contribute to a more synthetic understanding of urban biodiversity.

In light of the need to operationalize the mapping of forest composition at landscape scales, this study uses multi-scale nested vegetation sampling in conjunction with LiDAR-hyperspectral remotely sensed data from the G-LiHT airborne sensor to map vascular plant compositional turnover in a compositionally and structurally complex North Carolina Piedmont forest. Reflecting a shift in emphasis from remotely sensing individual crowns to detecting aggregate optical-structural properties of forest stands, predictive maps reflect the composition of entire vascular plant communities, inclusive of those species smaller than the resolution of the remotely sensed imagery, intertwined with proximate taxa, or otherwise obscured from optical sensors by dense upper canopies. Stand-scale vascular plant composition is modeled as community continua: where discrete community-unit classes at different compositional resolutions provide interpretable context for continuous gradient maps that depict n-dimensional compositional complexity as a single, consistent RGB color combination. In total, derived remotely sensed predictors explain 71%, 54%, and 48% of the variation in the first three components of vascular plant composition, respectively. Among all remotely sensed environmental gradients, topography derived from LiDAR ground returns, forest structure estimated from LiDAR all returns, and morphological-biochemical traits determined from hyperspectral imagery each significantly correspond to the three primary axes of floristic composition in the study site. Results confirm the complementarity of LiDAR and hyperspectral sensors for modeling the environmental gradients constraining landscape turnover in vascular plant composition and hold promise for predictive mapping applications spanning local land management to global ecosystem modeling.

Spatially explicit population models (SEPMs) are often considered the best way to predict and manage species distributions in spatially heterogeneous landscapes. However, they are computationally intensive and require extensive knowledge of species' biology and behavior, limiting their application in many cases. An alternative to SEPMs is graph theory, which has minimal data requirements and efficient algorithms. Although only recently introduced to landscape ecology, graph theory is well suited to ecological applications concerned with connectivity or movement. This paper compares the performance of graph theory to a SEPM in selecting important habitat patches for Wood Thrush (Hylocichla mustelina) conservation. We use both models to identify habitat patches that act as population sources and persistent patches and also use graph theory to identify patches that act as stepping stones for dispersal. Correlations of patch rankings were very high between the two models. In addition, graph theory offers the ability to identify patches that are very important to habitat connectivity and thus long-term population persistence across the landscape. We show that graph theory makes very similar predictions in most cases and in other cases offers insight not available from the SEPM, and we conclude that graph theory is a suitable and possibly preferable alternative to SEPMs for species conservation in heterogeneous landscapes.

While ecologists have long recognized the key role of monitoring programs in natural-resource management, we have only recently come to appreciate the logistical difficulties of designing powerful yet efficient schemes for monitoring large, heterogeneous landscapes. Such designs are especially challenging if the signal to be monitored is uncertain, such as in the case of ecosystem response to climate change. I illustrate an approach in which a simulation model is used to design a monitoring scheme that focuses on application-specific sensitivities or uncertainties. Formal model analysis defines these sensitivities in the model's parameter space. These parametric domains are then mapped into geographic space by regressing model sensitivity on terrain variables in a geographic information system. Specific sites for monitoring are then selected by sampling with a two-stage stratified-cluster design from these parametrically sensitive or uncertain locations. As an example, I use a forest simulation model to d...

Decades of fire exclusion have led to hazardous fuel accumulations and the deterioration of fire-dependent ecosystems, particularly in the American West. Managers are striving to return the ecological role of fire to many ecosystems and would benefit from a much better understanding of how forest structure and composition might change if fires were reintroduced. We used a forest gap model, developed for forests in the Sierra Nevada, California, USA, that integrates climate, fire, and forest dynamics to investigate forest response to changes in the fire regime. The model simulates a spatially heterogeneous fuel bed that is responsive to changes in forest condition, making it well suited for examining alternative management approaches for restoring Sierra Nevada forests after a century of fire exclusion. Presuppression forest basal area, species composition, and spatial autocor- relation structure were restored quickly, if simulated disturbances that caused substantial tree mortality were reintroduced. Simulations of harvest induced the highest levels of mortality and, thus, most effectively restored forest structure and composition. However, prescribed fires were just as effective in restoring forest structure and composition if they were sufficiently severe.

We describe and apply a correspondence between two major modeling ap- proaches to forest dynamics: transition markovian models and gap models or JABOWA- FORET type simulators. A transition model can be derived from a gap model by defining states on the basis of species, functional roles, vertical structure, or other convenient cover types. A gap-size plot can be assigned to one state according to dominance of one of these cover types. A semi-Markov framework is used for the transition model by considering not only the transition probabilities among the states, but also the holding times in each transition. The holding times are considered to be a combination of distributed and fixed time delays. Spatial extensions are possible by considering collections of gap-size plots and the proportions of these plots occupied by each state. The advantages of this approach include: reducing simulation time, analytica-l guidance to the simulations, direct analytical exploration of hypothesis and the possibility of fast computation from closed-form solutions and formulae. These advantages can be useful in the simulation of landscape dynamics and of species-rich forests, as well as in designing management strategies. A preliminary ap- plication to the H. J. Andrews forest in the Oregon Cascades is presented for demonstration.

New silvicultural strategies to sustain both ecological and human communities are being developed and implemented on federal forest lands in the Pacific Northwest (PNW) United States. Two important stand-level components of the new silviculture regimes are rotation age and retention level of live trees in harvest units. Ecologists have suggested that canopy tree retention and longer rotations will create patterns of stand structure in managed forest that are similar to those in natural forests, and promote long-term ecological productivity and biodiversity. Forest economists, however, are concerned that canopy tree retention and long rotations may reduce wood production, although the high value of large logs produced by these new silvicultural regimes may compensate for reduced growth rates. We used the forest model ZELIG to perform a factorial simulation experiment on long-term responses of ecological and economic variables to nine retention levels and four rotation lengths. ZELIG output on forest structure and composition was input to a forest economics model that calculated net value of wood products in 1989 dollars. The simulated stand data were also linked with regression equations to predict the densities of 17 bird species as a function of tree size class distribution. Five replicates of each treatment were run for the 240-yr simulation period. Results indicated that stand structure under each of the canopy tree retention levels was more similar to the pre-treatment natural forest than following clear-cutting. Variation in tree size under intermediate levels of retention, however, did not reach the level of the natural forest during the simulation period. Tree species composition was strongly related to retention level and rotation age. Shade-intolerant Douglas- fir (Pseudotsuga menziesii) lost dominance to shade-tolerant species under intermediate retention levels and longer rotations. Wood production decreased significantly with increasing retention level and rotation age, with a notable threshold between retention levels of 0 and 5 trees per hectare. Net wood products value did not decrease as rapidly with retention level, and did not differ much among rotation ages, because of the high value of large logs. Bird species responded individualistically to retention level and rotation age. Some had peak densities under short-rotation clear-cutting, but most were associated with structurally complex, closed-canopy forest. Consequently, bird species richness increased significantly with retention level and rotation age. Within the assumptions and limitations of our models, this application provided knowledge on trends and thresholds that can help land managers to choose silvicultural regimes that best balance their management objectives. We concluded that retention level and rotation age strongly influence ecological and economic responses in PNW forests; efforts are needed to reduce uncertainty about these effects.