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Graph-theoretic evaluations of habitat connectivity often rely upon least-cost path analyses to evaluate connectedness of habitat patches, based on an underlying cost surface. We present two improvements upon these methods. As a case study to test these methods, we evaluated habitat connectivity for the endangered San Martin titi monkey (Plecturocebus oenanthe) in north-central Peru, to prioritize habitat patches for conservation. First, rather than using a single least-cost path between habitat patches, we analyzed multigraphs made up of multiple low-cost paths. This allows us to differentiate between patches connected through a single narrow corridor, and patches connected by a wide swath of traversable land. We evaluate potential movement pathways by iteratively removing paths and recomputing connectivity metrics. Second, instead of performing a sensitivity analysis by varying costs uniformly across the landscape, we generated landscapes with spatially varying costs. This approach produced a more informative assessment of connectivity than standard graph analyses. Of the 4340 habitat patches considered across the landscape, we identified the most important 100, those frequently ranked highly through repeated network modifications, for multiple metrics and cost surfaces. These methods represent a novel approach for assessing connectivity, better accounting for spatial configurations of habitat patches and uncertainty in cost surfaces. The ability to identify habitat patches with more possible routes to other patches is of interest for resiliency planning and prioritization in the face of continued habitat loss and climate change. These methods should be broadly applicable to conservation planning for other wildlife species.

Watershed urbanization increases the concentrations of major ions in downstream freshwater ecosystems. Non-point source ions from human activities and the chemical weathering of infrastructure are efficiently transported by stormwater runoff through subsurface pipes directly into streams. While the increase in mean streamwater solute concentrations in urban watersheds because of non-point source loading is a commonly documented phenomenon, the temporal dynamics of urban streamwater solute chemistry and its relationship to development patterns is less well understood. We continuously monitored streamflow, temperature, and conductivity as well as bimonthly variation in the solute chemistry of 24 watershed outlets in the Raleigh-Durham-Chapel Hill metropolitan area in North Carolina, USA for two years. Watersheds were selected to have similar proportions of development (10–36% development) while spanning the full measured range in road and stormwater pipe density for urban watersheds in this metropolitan area. We found remarkable temporal variation in the chemical regimes of the urban streams draining this set of watersheds, despite their similar proportion of development. For multiple major ions (SO42−, Cl−, NO3−), bimonthly concentrations varied tenfold across the study streams and temporal variation in streamwater chemical composition increased across watershed road and stormwater pipe density gradients. Total temporal variation in baseflow and stormflow dissolved ions, as measured by the coefficient of variation of specific conductance, was highly correlated with road density after accounting for underlying geology (R2 > 0.60). Using structural equation modeling, we found that subsurface piping mediates the relationship between roads and stream chemistry. In watersheds with high stormwater pipe density, streamwater ionic strength was low at baseflow while high and highly variable during event flow. This ‘flashy’ chemical signal in those watersheds, mirrors their more responsive hydrographs. In contrast, in our urban watersheds with the lowest pipe density, baseflow ionic strengths were higher, and event flow ionic strengths were lower and less variable. These results suggest that the extent to which pavement is drained by pipes creates a tradeoff between routing urban salts directly to streams via pipes versus indirectly to streams via loading to groundwater. Results of this study demonstrate the importance of considering altered temporal chemical variability, not just elevated solute concentrations, as a key feature of the impact of urbanization on streams. Future urban development design which minimizes the extent of roads and stormwater pipes in watersheds through denser development may be an effective strategy to abate impacts on downstream water quality.

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.

Windthrow plays a critical role in maintaining species diversity in temperate forests. Do large-scale strong wind events (i.e., tropical cyclones, including hurricanes, typhoons and severe cyclonic storms) increase tree diversity in severely damaged forest areas? Do hurricanes (tropical cyclones that occurs in the Atlantic Ocean and northeastern Pacific Ocean) lead to altered relative abundance of shade-tolerant and shade-intolerant species? Did historic hurricanes alter the succession trajectory of the damaged forests? We used nearly 70-year tree demographic data to assess the effects of two major hurricanes on woody species diversity in Piedmont forests, North Carolina, USA. Species richness (S) and Shannon–Wiener’s diversity index (H′) were used to evaluate the changes in tree diversity. The changes in composition were assessed with Nonmetric Multidimensional Scaling. The pre-hurricane successional phase can strongly influence both the damage severity and subsequent responses. Although there is often an immediate drop in diversity following a hurricane, understory tree diversity quickly increases to levels that exceed those prior to the disturbance. This leads to an increase in diversity in stands that were substantially damaged. Hurricanes significantly decrease the dominance of shade-intolerant canopy species while increasing pre-established, more shade-tolerant species. We conclude that large, and infrequent hurricanes help to maintain local tree diversity, but also accelerate the increase in dominance of understory species such as red maple and beech.

Watershed urbanization leads to chemical and thermal pollution of urban streams and significant declines in aquatic biodiversity. Most investigators have focused on variation in total water...

Watershed urbanization introduces a variety of physical, chemical, and thermal stressors to receiving streams and leads to well-documented declines in the diversity of fish and macroinverte...

The persistence of freshwater degradation has necessitated the growth of an expansive stream and wetland restoration industry, yet restoration prioritization at broad spatial extents is still limited and ad-hoc restoration prevails. The River Basin Restoration Prioritization tool has been developed to incorporate vetted, distributed data models into a catchment scale restoration prioritization framework. Catchment baseline condition and potential improvement with restoration activity is calculated for all National Hydrography Dataset stream reaches and catchments in North Carolina and compared to other catchments within the river subbasin to assess where restoration efforts may best be focused. Hydrologic, water quality, and aquatic habitat quality conditions are assessed with peak flood flow, nitrogen and phosphorus loading, and aquatic species distribution models. The modular nature of the tool leaves ample opportunity for future incorporation of novel and improved datasets to better represent the holistic health of a watershed, and the nature of the datasets used herein allow this framework to be applied at much broader scales than North Carolina.

Identifying adaptive loci can provide insight into the mechanisms underlying local adaptation. Genotype-environment association (GEA) methods, which identify these loci based on correlations between genetic and environmental data, are particularly promising. Univariate methods have dominated GEA, despite the high dimensional nature of genotype and environment. Multivariate methods, which analyze many loci simultaneously, may be better suited to these data since they consider how sets of markers covary in response to environment. These methods may also be more effective at detecting adaptive processes that result in weak, multilocus signatures. Here, we evaluate four multivariate methods, and five univariate and differentiation-based approaches, using published simulations of multilocus selection. We found that Random Forest performed poorly for GEA. Univariate GEAs performed better, but had low detection rates for loci under weak selection. Constrained ordinations showed a superior combination of low false positive and high true positive rates across all levels of selection. These results were robust across the demographic histories, sampling designs, sample sizes, and levels of population structure tested. The value of combining detections from different methods was variable, and depended on study goals and knowledge of the drivers of selection. Reanalysis of genomic data from gray wolves highlighted the unique, covarying sets of adaptive loci that could be identified using redundancy analysis, a constrained ordination. Although additional testing is needed, this study indicates that constrained ordinations are an effective means of detecting adaptation, including signatures of weak, multilocus selection, providing a powerful tool for investigating the genetic basis of local adaptation.

There is a growing movement in government, environmental non-governmental organizations and the private sector to include ecosystem services in decision making. Adding ecosystem services into assessments implies measuring how much a change in ecological conditions affects people, social benefit, or value to society. Despite consensus around the general merit of accounting for ecosystem services, systematic guidance on what to measure and how is lacking. Current ecosystem services assessments often resort to biophysical proxies (e.g., area of wetland in a floodplain) or even disregard services that seem difficult to measure. Valuation, an important tool for assessing trade-offs and comparing outcomes, is also frequently omitted due to lack of data on social preferences, lack of expertise with valuation methods, or mistrust of valuation methods for non-market services. To address these shortcomings, we propose the use of a new type of indicator that explicitly reflects an ecosystem’s capacity to provide benefits to society, ensuring that ecosystem services assessments measure outcomes that are demonstrably and directly relevant to human welfare. We call these Benefit-relevant indicators (BRIs) and describe a process for developing them using causal chains that link management decisions through ecological responses to effects on human well-being. BRIs identify what is valued and by whom, but stop short of valuation. A BRI for the ability of wetlands to ameliorate flooding would connect measures of the quantity and quality of wetland in a floodplain, as affected by wetlands management decisions, to the number of people or properties downstream that are vulnerable to flooding. BRIs can support monetary or non-monetary valuation, but are particularly useful when valuation will not be conducted; in such cases they serve as stand-alone measures of “what is valued” by particular beneficiaries. BRIs are valid measures of ecosystem services in that they are directly linked to human well-being. Flexibility in the development of BRIs helps to ensure that they are broadly applicable across practitioner and stakeholder communities and decision contexts.

Sustainability challenges for nature and people are complex and interconnected, such that effective solutions require approaches and a common theory of change that bridge disparate disciplines and sectors. Causal chains offer promising approaches to achieving an integrated understanding of how actions affect ecosystems, the goods and services they provide, and ultimately, human well-being. Although causal chains and their variants are common tools across disciplines, their use remains highly inconsistent, limiting their ability to support and create a shared evidence base for joint actions. In this article, we present the foundational concepts and guidance of causal chains linking disciplines and sectors that do not often intersect to elucidate the effects of actions on ecosystems and society. We further discuss considerations for establishing and implementing causal chains, including nonlinearity, trade-offs and synergies, heterogeneity, scale, and confounding factors. Finally, we highlight the science, practice, and policy implications of causal chains to address real-world linked human–nature challenges.

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.

Fire exclusion in the United States over the last century has had major impacts on forest ecosystems and landscapes. Out of a desire to reverse or mitigate the impacts of fire exclusion, some managers conduct prescribed fires meant to mimic the historic ecological role of fire and restore ecosystem properties. In the Southern Appalachians, fire exclusion in pine- and oak-dominated xeric ridge forests has allowed fire-sensitive hardwood species to establish, filling in the canopy and creating shady, moist conditions that are unfavorable for reproduction of fire-dependent pines and oaks. Managers of natural areas use prescribed fire to restore pine and oak dominance, promote pine and oak regeneration, and reduce stand densities. Here, we use multivariate analysis of monitoring data collected before and after 21 fires over 16 years in fire-suppressed xeric pine-oak forests in the Great Smoky Mountains National Park to assess how community composition and structure change after prescribed fire, to what degree changes after fire persist over time, and how the impacts of prescribed fire vary with fire severity and site environment. Fire consistently reduces stand density and shifts plots towards lower shrub cover and higher herbaceous cover. On the other hand, compositional shifts, i.e. changes in relative abundances of species, were highly variable in both magnitude and direction. Fire severity, measured as total fuel reduction and litter and duff reduction, was important for predicting the magnitude of change after fire. The magnitude of fire effects also varied with elevation, likely reflecting variation in local moisture conditions. Our results indicate that while fires do reduce stand density, they have not yet been successful in consistently restoring pine and oak dominance in the canopy. Restoring pine- and oak-dominance in xeric ridge forests in the Southern Appalachians will thus require extended management focus with flexible, adaptive, long-term planning and continued monitoring and research.

Storms in urban areas route heat and other pollutants from impervious surfaces, via drainage networks, into streams with well-described negative consequences on physical structure and biological integrity. We used heat pulses associated with urban storms as a tracer for pavement-derived stormwater inputs, providing a conservative estimate of the frequency with which these pollutants are transported into and through protected stream reaches. Our study was conducted within a 1.5-km reach in Durham, North Carolina, whose headwaters begin in suburban stormwater pipes before flowing through 1 km of protected, 100-year-old forest. We recorded heat-pulse magnitudes and distances travelled downstream, analyzing how they varied with storm and antecedent flow conditions. We found heat pulses >1°C traveled more than 1 km downstream of urban inputs in 11 storms over one year. This best-case management scenario of a reach within a protected forest shows that urban impacts can travel far downstream of inputs. Air temperature and flow intensity controlled heat-pulse magnitude, while heat-pulse size, mean flow, and total precipitation controlled dissipation distance. As temperatures and sudden storms intensify with climate change, heat-pulse magnitude and dissipation distance will likely increase. Streams in urbanized landscapes, such as Durham municipality, where 98.9% of streams are within 1 downstream km of stormwater outfalls, will be increasingly impacted by urban stormwaters.

Ecological systems often operate on time scales significantly longer or shorter than the time scales typical of human decision making, which causes substantial difficulty for conservation and management in socioecological systems. For example, invasive species may move faster than humans can diagnose problems and initiate solutions, and climate systems may exhibit long-term inertia and short-term fluctuations that obscure learning about the efficacy of management efforts in many ecological systems. We adopted a management-decision framework that distinguishes decision makers within public institutions from individual actors within the social system, calls attention to the ways socioecological systems respond to decision makers' actions, and notes institutional learning that accrues from observing these responses. We used this framework, along with insights from bedeviling conservation problems, to create a typology that identifies problematic time-scale mismatches occurring between individual decision makers in public institutions and between individual actors in the social or ecological system. We also considered solutions that involve modifying human perception and behavior at the individual level as a means of resolving these problematic mismatches. The potential solutions are derived from the behavioral economics and psychology literature on temporal challenges in decision making, such as the human tendency to discount future outcomes at irrationally high rates. These solutions range from framing environmental decisions to enhance the salience of long-term consequences, to using structured decision processes that make time scales of actions and consequences more explicit, to structural solutions aimed at altering the consequences of short-sighted behavior to make it less appealing. Additional application of these tools and long-term evaluation measures that assess not just behavioral changes but also associated changes in ecological systems are needed.

Models of landscape change are important tools for understanding the forces that shape landscapes. One motivation for modeling is to examine the implications of extrapolating short-term landscape dynamics over the longer term. This extrapolation of the status quo can serve as a frame of reference against which to assess alternative management scenarios or test hypotheses. There is a spectrum of ways to consider landscape change, ranging from simple and readily interpretable, to more realistic and less tractable. The goals of this lab are to.

Conversion of lands to agriculture and development within remaining natural habitats have fragmented ecosystems and reduced wildlife populations. The US Fish and Wildlife Service has adopted an incentive-based conservation strategy known as the Safe Harbor Program (SHP) to reduce the vulnerability of federally protected species located on private properties. In addition to protecting populations, the SHP also strives to enhance species viability by devising strategies to (re)connect populations among habitat patches. We empirically evaluated the effectiveness of the initial Safe Harbor agreement, developed for Red-cockaded Woodpeckers (Picoides borealis, hereafter RCW) in the North Carolina Sandhills, in enhancing connectivity for that species. According to our results, breeding territories located on private properties enrolled in the SHP promoted dispersal of RCWs and enhanced overall population connectivity relative to otherwise similar territories located on non-SHP properties. Moreover, the SHP created extensive stepping-stone corridors throughout the region. Our analysis also showed that RCW connectivity will be negatively impacted directly and indirectly by encroaching urban growth. By combining an urban growth model with estimated connectivity, managers and conservation planners can identify which properties critical for connectivity are most threatened by urban encroachment. These results can help conservation planners develop strategic actions on private land based on the species specific movement ability, current landscapes and projected urban growth.

Streams draining urban heat islands tend to be hotter than rural and forested streams at baseflow because of warmer urban air and ground temperatures, paved surfaces, and decreased riparian canopy. Urban infrastructure efficiently routes runoff over hot impervious surfaces and through storm drains directly into streams and can lead to rapid, dramatic increases in temperature. Thermal regimes affect habitat quality and biogeochemical processes, and changes can be lethal if temperatures exceed upper tolerance limits of aquatic fauna. In summer 2009, we collected continuous (10-min interval) temperature data in 60 streams spanning a range of development intensity in the Piedmont of North Carolina, USA. The 5 most urbanized streams averaged 21.1°C at baseflow, compared to 19.5°C in the 5 most forested streams. Temperatures in urban streams rose as much as 4°C during a small regional storm, whereas the same storm led to extremely small to no changes in temperature in forested streams. Over a kilometer ...

article i nfo Supervised classification of land cover across space and time is a long-standing goal of the Earth Science com- munity. Although most past and current analyses focus on detecting changes between two or more times, the opening of the USGS Landsat archive in 2009 has enabled exploration of methods for higher-frequency, time-serial monitoring of land cover dynamics. Modifying the protocols used to develop the 2001 National Land Cover Database (NLCD 2001), we fit a single classifier to a spatio-temporally distributed reference sam- ple and applied the model to 55 Landsat-5 images covering a section of the North Carolina Piedmont Plateau from 1984 to 2007. A generalized classification scheme, multi-temporal sampling design, supervised classifi- cation based on intra-annual spectral indices, and design-based accuracy assessment yielded a time-series of 16 land cover maps from 1985 to 2006 with a spatial extent of 1.7×10 6 ha, minimum mapping unit of 1 ha, and mean temporal interval of b2 years. Comparable to accuracy of the NLCD 2001 Land Cover Layer for the region, overall accuracy for a spatio-temporally independent test sample was 75%, with κ=0.7. When weighted by class proportions, percent correctly classified and kappa rose to 88% and 0.84, respectively. The resulting map series shows spatially and temporally complex changes in water, urban, forest, and herba- ceous cover resulting from natural and anthropogenic processes that would not be observable in either uni- or bi-temporal maps. Agricultural crop area dropped from ~45% in the 1980s to ~36% in the 1990s and then rose slightly to ~38% at the end of the period. Forest area increased to a maximum of ~55% in the 1990s and then dropped to ~53% in 2005. Urban growth appeared to be most rapid in the 1980s and 1990s and slowed thereafter. With continued focus on the semantics, causation, sampling, and uncertainty underlying spectral land cover classification, long-term series of Landsat images will provide increasingly robust, reliable records for a growing scientific user community. These multi-temporal datasets will be indispensable for understand- ing past land cover dynamics and predicting the implications of future change on the provision and manage- ment of ecosystem services.

At global and regional scales, tree mortality rates are positively correlated with forest net primary productivity (NPP). Yet causes of the correlation are unknown, in spite of potentially profound implications for our understanding of environmental controls of forest structure and dynamics and, more generally, our understanding of broad-scale environmental controls of population dynamics and ecosystem processes. Here we seek to shed light on the causes of geographic patterns in tree mortality rates, and we consider some implications of the positive correlation between mortality rates and NPP. To reach these ends, we present seven hypotheses potentially explaining the correlation, develop an approach to help distinguish among the hypotheses, and apply the approach in a case study comparing a tropical and temperate forest. Based on our case study and literature synthesis, we conclude that no single mechanism controls geographic patterns of tree mortality rates. At least four different mechanisms may be at play, with the dominant mechanisms depending on whether the underlying productivity gradients are caused by climate or soil fertility. Two of the mechanisms are consequences of environmental selection for certain combinations of life-history traits, reflecting trade-offs between growth and defense (along edaphic productivity gradients) and between reproduction and persistence (as manifested in the adult tree stature continuum along climatic and edaphic gradients). The remaining two mechanisms are consequences of environmental influences on the nature and strength of ecological interactions: competition (along edaphic gradients) and pressure from plant enemies (along climatic gradients). For only one of these four mechanisms, competition, can high mortality rates be considered to be a relatively direct consequence of high NPP. The remaining mechanisms force us to adopt a different view of causality, in which tree growth rates and probability of mortality can vary with at least a degree of independence along productivity gradients. In many cases, rather than being a direct cause of high mortality rates, NPP may remain high in spite of high mortality rates. The independent influence of plant enemies and other factors helps explain why forest biomass can show little correlation, or even negative correlation, with forest NPP.

This study examined native bird communities in forest patches across a gradient of urbanization. We used field data and multivariate statistical techniques to examine the effects of landscape context, roads, traffic noise, and vegetation characteristics on bird community composition in the North Carolina Piedmont (U.S.A.). Landscape-level variables, particularly those related to urbanization, were most important in structuring forest bird communities. Specifically, we found that road density and amount of urban land cover were the best predictors of species composition. We found that urban and rural bird communities were quite distinct from each other. Rural communities had more long-distance migrants and forest interior species but species richness did not differ between the communities. Our results suggest some specific guidelines to target bird species of interest both inside and outside of urban areas. For example, if increasing numbers of migratory species is of primary concern, then conservation areas should be located outside of urban boundaries or in areas with low road density. However, if maximizing species richness is the focus, location of the conservation area may not be as important if the conservation area is surrounded by at least 50 m of forest habitat in all directions.

Graph theory is a body of mathematics dealing with problems of connectivity, flow, and routing in networks ranging from social groups to computer networks. Recently, network applications have erupted in many fields, and graph models are now being applied in landscape ecology and conservation biology, particularly for applications couched in metapopulation theory. In these applications, graph nodes represent habitat patches or local populations and links indicate functional connections among populations (i.e. via dispersal). Graphs are models of more complicated real systems, and so it is appropriate to review these applications from the perspective of modelling in general. Here we review recent applications of network theory to habitat patches in landscape mosaics. We consider (1) the conceptual model underlying these applications; (2) formalization and implementation of the graph model; (3) model parameterization; (4) model testing, insights, and predictions available through graph analyses; and (5) potential implications for conservation biology and related applications. In general, and for a variety of ecological systems, we find the graph model a remarkably robust framework for applications concerned with habitat connectivity. We close with suggestions for further work on the parameterization and validation of graph models, and point to some promising analytic insights.

Spatially explicit population models (SEPMs) are often used in conservation planning. However, confidence intervals around predictions of spatially explicit population models can greatly underestimate model uncertainty. This is partly because some sources of uncertainty are not amenable to the classic methods of uncertainty analysis. Here, we present a method that can be used to include multiple sources of uncertainty into more realistic confidence intervals. To illustrate our approach, we use a case study of the wood thrush (Hylocichla mustelina) in the fragmented forest of the North Carolina Piedmont. We examine 6 important sources of uncertainty in our spatially explicit population model: (1) the habitat map, (2) the dispersal algorithm, (3) clutch size, (4) edge effects, (5) dispersal distance, and (6) the intrinsic variability in our model. We found that uncertainty in the habitat map had the largest effect on model output, but each of the six factors had a significant effect and most had significant interactions with the other factors as well. We also found that our method of incorporating multiple sources of uncertainty created much larger confidence intervals than the projections that incorporated only sources of uncertainty included in most spatially explicit population model predictions.

Aims Large hurricanes have profound impacts on temperate forests, but owing to their infrequent nature these effects have rarely been examined in detail. In 1996, Hurricane Fran significantly damaged many long-term tree census plots in the Duke Forest on the North Carolina Piedmont, thereby providing an exceptional opportunity to examine pre- and post-hurricane forest compositional trajectories. Our goal was to examine immediate, short-term (0–4 years) and longer term (;5 year) hurricane-induced structural, spatial and compositional changes in the tree population (stem d.b.h > 1 cm) in the context of our detailed, long-term knowledge of the dynamics of these forests. Methods We surveyed stem damage and tree mortality in 34 long-term permanent plots (ca. 70-year record; 404–1 012 m 2 ) and 7 large mapped tree stands (ca. 20-year record; 5 250–65 000 m 2 ) representing both transition-phase, even-aged pine stands and uneven-aged upland hardwood forests. We employed three types of damage measures to quantify stand-level damage severity: percentage of stems damaged, percentage of basal area lost and a ‘stand-level damage index’. Second-order spatial analysis (Ripley’s K-function) was used to investigate patterns in tree mortality. Important findings Our study found hurricane effects on the structural attributes of Piedmont forests to be variable and patchy. Changes in tree species composition, however, were modest. Uprooting was the major damage type for the overstory trees [diameter at breast height (d.b.h.) >10 cm] apparently due to the exposure of the crowns to high wind combined with heavy rainfall prior to and during the storm. Saplings, juvenile trees and small trees (1–10 cm d.b.h.) of the understory and midstory were mainly damaged by being pinned or bent by their damaged large neighbors. Hurricane-induced tree mortality varied weakly among species, was positively correlated with pre-hurricane tree size and remained up to 2-fold higher than pre-hurricane background mortality 5 years after the hurricane. Spatial point pattern analysis revealed a patchy distribution of tree mortality during the hurricane sampling interval. Hurricane Fran resulted in a dramatic increase in average gap size from ca. 400 m 2 pre-hurricane to ca 1100 m 2 after the hurricane, whereas maximum gap sizes reached 18–34 times larger than the pre-hurricane levels.

Connectivity of habitat patches is thought to be important for movement of genes, individuals, populations, and species over multiple temporal and spatial scales. We used graph theory to characterize multiple aspects of landscape connectivity in a habitat network in the North Carolina Piedmont (U.S.A). We compared this landscape with simulated networks with known topology, resistance to disturbance, and rate of movement. We introduced graph measures such as compartmentalization and clustering, which can be used to identify locations on the landscape that may be especially resilient to human development or areas that may be most suitable for conservation. Our analyses indicated that for songbirds the Piedmont habitat network was well connected. Furthermore, the habitat network had commonalities with planar networks, which exhibit slow movement, and scale-free networks, which are resistant to random disturbances. These results suggest that connectivity in the habitat network was high enough to prevent the negative consequences of isolation but not so high as to allow rapid spread of disease. Our graph-theory framework provided insight into regional and emergent global network properties in an intuitive and visual way and allowed us to make inferences about rates and paths of species movements and vulnerability to disturbance. This approach can be applied easily to assessing habitat connectivity in any fragmented or patchy landscape.

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.

The dispersal of individuals among marine populations is of great importance to meta- population dynamics, population persistence, and species expansion. Understanding this connectivity between distant populations is key to their effective conservation and management. For many marine species, population connectivity is determined largely by ocean currents transporting larvae and juveniles between distant patches of suitable habitat. Recent work has focused on the biophysics of marine larval dispersal and its importance to population dynamics, although few studies have evaluated the spatial and temporal patterns of this potential dispersal. Here, we show how an Eulerian advection-diffusion approach can be used to model the dispersal of coral larvae between reefs throughout the Tropical Pacific. We illustrate how this connectivity can be analyzed using graph theory—an effective approach for exploring patterns in spatial connections, as well as for determining the importance of each site and pathway to local and regional connectivity. Results indicate that the scale (average distance) of dispersal in the Pacific is on the order of 50-150 km, consistent with recent studies in the Caribbean (Cowen, et al. 2006). Patterns in the dispersal graphs highlight pathways for larval dispersal along major ocean currents and through island chains. A series of critical island 'stepping stones' are discovered providing potential pathways across the equatorial currents and connect- ing distant island groups. Patterns in these dispersal graphs highlight possible pathways for species expansions, reveal connected upstream/downstream populations, and suggest areas that might be priori- tized for marine conservation efforts.

We assessed changes in vegetative structure of 49 impoundments at Moosehorn National Wildlife Refuge (MNWR), Maine, USA, between the periods 1984–1985 to 2002 with a multivariate, adaptive approach that may be useful in a variety of wetland and other habitat management situations. We used Mahalanobis Distance (MD) analysis to classify the refuge's wetlands as poor or good waterbird habitat based on five variables: percent emergent vegetation, percent shrub, percent open water, relative richness of vegetative types, and an interspersion juxtaposition index that measures adjacency of vegetation patches. Mahalanobis Distance is a multivariate statistic that examines whether a particular data point is an outlier or a member of a data cluster while accounting for correlations among inputs. For each wetland, we used MD analysis to quantify a distance from a reference condition defined a priori by habitat conditions measured in MNWR wetlands used by waterbirds. Twenty-five wetlands declined in quality b...

Ecological systems often operate on time scales significantly longer or shorter than the time scales typical of human decision making, which causes substantial difficulty for conservation and management in socioecological systems. For example, invasive species may move faster than humans can diagnose problems and initiate solutions, and climate systems may exhibit long-term inertia and short-term fluctuations that obscure learning about the efficacy of management efforts in many ecological systems. We adopted a management-decision framework that distinguishes decision makers within public institutions from individual actors within the social system, calls attention to the ways socioecological systems respond to decision makers' actions, and notes institutional learning that accrues from observing these responses. We used this framework, along with insights from bedeviling conservation problems, to create a typology that identifies problematic time-scale mismatches occurring between individual decision makers in public institutions and between individual actors in the social or ecological system. We also considered solutions that involve modifying human perception and behavior at the individual level as a means of resolving these problematic mismatches. The potential solutions are derived from the behavioral economics and psychology literature on temporal challenges in decision making, such as the human tendency to discount future outcomes at irrationally high rates. These solutions range from framing environmental decisions to enhance the salience of long-term consequences, to using structured decision processes that make time scales of actions and consequences more explicit, to structural solutions aimed at altering the consequences of short-sighted behavior to make it less appealing. Additional application of these tools and long-term evaluation measures that assess not just behavioral changes but also associated changes in ecological systems are needed.

Our society faces the pressing challenge of increasing agricultural production while minimizing negative consequences on ecosystems and the global climate. Indonesia, which has pledged to reduce greenhouse gas (GHG) emissions from deforestation while doubling production of several major agricultural commodities, exemplifies this challenge. Here we focus on palm oil, the world's most abundant vegetable oil and a commodity that has contributed significantly to Indonesia's economy. Most oil palm expansion in the country has occurred at the expense of forests, resulting in significant GHG emissions. We examine the extent to which land management policies can resolve the apparently conflicting goals of oil palm expansion and GHG mitigation in Kalimantan, a major oil palm growing region of Indonesia. Using a logistic regression model to predict the locations of new oil palm between 2010 and 2020 we evaluate the impacts of six alternative policy scenarios on future emissions. We estimate net emissions of 128.4-211.4 MtCO2 yr(-1) under business as usual expansion of oil palm plantations. The impact of diverting new plantations to low carbon stock land depends on the design of the policy. We estimate that emissions can be reduced by 9-10% by extending the current moratorium on new concessions in primary forests and peat lands, 35% by limiting expansion on all peat and forestlands, 46% by limiting expansion to areas with moderate carbon stocks, and 55-60% by limiting expansion to areas with low carbon stocks. Our results suggest that these policies would reduce oil palm profits only moderately but would vary greatly in terms of cost-effectiveness of emissions reductions. We conclude that a carefully designed and implemented oil palm expansion plan can contribute significantly towards Indonesia's national emissions mitigation goal, while allowing oil palm area to double.

Neutral landscape models were originally developed to test the hypothesis that human-induced fragmentation produces patterns distinctly different from those associated with random processes. Other uses for neutral models have become apparent, including the development and testing of landscape metrics to characterize landscape pattern. Although metric development proved to be significant, the focus on metrics obscured the need for iterative hypothesis testing fundamental to the advancement of the discipline. We present here an example of an alternative neutral model and hypothesis designed to relate the process of landscape change to observed landscape patterns. The methods and program, QRULE, are described and options for statistical testing outlined. The results show that human fragmentation of landscapes results in a non-random association of land-cover types that can be describe by simple statistical methods. Options for additional landscape studies are discussed and access to QRULE described in the hope that these methods will be employed to advance our understanding of the processes that affect the structure and function in human dominated landscapes.

Edges between forest and non-forest habitats often have significant effects on forest microclimate and resource availability, with corresponding effects on species composition and abundance. Exotic species are often increased in abundance near forest edges. This increase in abundance could be either because of the increase in resource availability near edges, or because of increased dispersal into forest edges. We measured species composition and a set of geospatial variables on transects at 66 edges in the North Carolina Piedmont in an attempt to distinguish between these two factors. Mantel tests show that species composition is significantly different in forest edges than in the forest interior, but that this effect only penetrates about 5 m into the forest. Indicator species analysis finds several species that are indicative of edge communities, including trumpet vine (Campsis radicans), two drought-tolerant oak species (Quercus stellata and Q. falcata), a serviceberry (Amelanchier arboreum), and a common exotic species, tree-of-heaven (Ailanthus altissima). Poisson regression techniques showed that in both the seedling and tree strata of the forest, exotic species increased in abundance on flat sites with a high potential seed source. Mapping predicted exotic species abundance onto the landscape. We find that large-scale variation in exotic species abundance is due mostly to variation in potential seed sources, while small-scale variation relates more to edaphic factors. Our results stress that both dispersal and environmental filters are important for determining exotic species abundance, but potentially the filters operate at different spatial scales.

Land-cover and land-use change modeling have become increasingly common, and myriad different modeling techniques are now available. Many techniques assume that the rules of landscape change are the same everywhere within the study area, an assumption that contrasts with reality in many municipal regions, which have spatially varying development restrictions. In this paper, we provide a case study from the Raleigh–Durham area of North Carolina (USA) showing the consequences of using a model with a spatially homogeneous form when the rules of landscape change are spatially heterogeneous. Using classified Thematic Mapper images of 1990 and 2000, we fit two models relating probability of deforestation to a large set of potentially explanatory variables. Potential autocorrelation in the error term of our models was avoided by sampling outside the zone of spatial autocorrelation. The first model, a logistic regression (GLM), was used as an example of a simple, spatially homogeneous model, where the probability of deforestation is a function of a set of explanatory variables. The second model was a classification and regression tree analysis (CART), a spatially heterogeneous model in which the data were recursively partitioned on the same explanatory variables plus spatially explicit indicator variables, to create a binary decision tree that adequately captured the pattern in deforestation. Overall, the CART model (15.2% misclassification rate) performed significantly better than the GLM model (33.1% misclassification rate). When the residuals of both models were examined spatially, the CART model appears to perform better, more accurately predicting hotspots of development and predicting the baseline proportion of deforested pixels more accurately. Our results lend support to the importance of spatial heterogeneity in the rules of landscape change, and suggest that models that attend local variability in the forces driving landscape change can provide more useful predictions than models that assume these forces operate similarly throughout the landscape.