Extending coverage and thematic resolution of compositional land cover maps in a hierarchical Bayesian framework.

Ecological models are constrained by the availability of high-quality data at biologically appropriate resolutions and extents. Modeling a species' affinity or aversion with a particular land cover class requires data detailing that class across the full study area. Data sets with detailed legends (i.e., high thematic resolution) and/or high accuracy often sacrifice geographic extent, while large-area data sets often compromise on the number of classes and local accuracy. Consequently, ecologists must often restrict their study extent to match that of the more precise data set, or ignore potentially key land cover associations to study a larger area. We introduce a hierarchical Bayesian model to capitalize on the thematic resolution and accuracy of a regional land cover data set, and on the geographic breadth of a large area land cover data set. For the full extent (i.e., beyond the regional data set), the model predicts systematic discrepancies of the large-area data set with the regional data set, and divides an aggregated class into two more specific classes detailed by the regional data set. We illustrate the application of our model for mapping eastern white pine (Pinus strobus) forests, an important timber species that also provides habitat for an invasive shrub in the northeastern United States. We use the National Land Cover Database (NLCD), which covers the full study area but includes only generalized forest classes, and the NH GRANIT land cover data set, which maps White Pine Forest and has high accuracy, but only exists within New Hampshire. We evaluate the model at coarse (20 km2 ) and fine (2 km2 ) resolutions, with and without spatial random effects. The hierarchical model produced improved maps of compositional land cover for the full extent, reducing inaccuracy relative to NLCD while partitioning a White Pine Forest class out of the Evergreen Forest class. Accuracy was higher with spatial random effects and at the coarse resolution. All models improved upon simply partitioning Evergreen Forest in NLCD based on the predicted distribution of white pine. This flexible statistical method helps ecologists leverage localized mapping efforts to expand models of species distributions, population dynamics, and management strategies beyond the political boundaries that frequently delineate land cover data sets.

The Influence of Human Demography on Land Cover Change in the Great Lakes States, USA.

The Great Lakes region contains productive agricultural and forest lands, but it is also highly urbanized, with 32 of its 52 million residents living in nine large metropolitan areas. Urbanization of undeveloped areas may adversely affect the productivity of agricultural and forest lands, and the provision of ecosystem services. We combine demographic and remote sensing data to evaluate land cover change in the region using a two-phase statistical modeling approach that predicts the incidence and extent of land cover change for each of the region's 10,579 county subdivisions. Observed patterns are spatially uneven, and the probability of land cover change is influenced by current land use, human habitation, industry, and demographic change. Pseudo R2 values varied from 0.053 to 0.338 for the first-phase logistic models predicting the presence of land cover change; second-stage beta models predicting the rate of change were more reliable, with pseudo R2 ranging from 0.225 to 0.675. Overall, changes from agriculture or greenspace to development were much more predictable than changes from agriculture to greenspace or vice versa, and demographic variables were much more important in models predicting change to development. Although models successfully predicted the general location of land cover change, and models from before the Great Recession were useful for predicting the location but not the amount of change during the recession, fine-grained prediction remained challenging. Understanding where future changes are most probable can inform planning and policy-making, which may reduce the impact of development on resource production, environmental health, and ecosystem services.

Unearthing the hidden world of roots: Root biomass and architecture differ among species within the same guild.

The potential benefits of planting trees have generated significant interest with respect to sequestering carbon and restoring other forest based ecosystem services. Reliable estimates of carbon stocks are pivotal for understanding the global carbon balance and for promoting initiatives to mitigate CO2 emissions through forest management. There are numerous studies employing allometric regression models that convert inventory into aboveground biomass (AGB) and carbon (C). Yet the majority of allometric regression models do not consider the root system nor do these equations provide detail on the architecture and shape of different species. The root system is a vital piece toward understanding the hidden form and function roots play in carbon accumulation, nutrient and plant water uptake, and groundwater infiltration. Work that estimates C in forests as well as models that are used to better understand the hydrologic function of trees need better characterization of tree roots. We harvested 40 trees of six different species, including their roots down to 2 mm in diameter and created species-specific and multi-species models to calculate aboveground (AGB), coarse root belowground biomass (BGB), and total biomass (TB). We also explore the relationship between crown structure and root structure. We found that BGB contributes ~27.6% of a tree's TB, lateral roots extend over 1.25 times the distance of crown extent, root allocation patterns varied among species, and that AGB is a strong predictor of TB. These findings highlight the potential importance of including the root system in C estimates and lend important insights into the function roots play in water cycling.

Modeling Raccoon (Procyon lotor) Habitat Connectivity to Identify Potential Corridors for Rabies Spread.

The United States Department of Agriculture (USDA), Animal and Plant Health Inspection Service (APHIS), Wildlife Services National Rabies Management Program has conducted cooperative oral rabies vaccination (ORV) programs since 1997. Understanding the eco-epidemiology of raccoon (Procyon lotor) variant rabies (raccoon rabies) is critical to successful management. Pine (Pinus spp.)-dominated landscapes generally support low relative raccoon densities that may inhibit rabies spread. However, confounding landscape features, such as wetlands and human development, represent potentially elevated risk corridors for rabies spread, possibly imperiling enhanced rabies surveillance and ORV planning. Raccoon habitat suitability in pine-dominated landscapes in Massachusetts, Florida, and Alabama was modeled by the maximum entropy (Maxent) procedure using raccoon presence, and landscape and environmental data. Replicated (n = 100/state) bootstrapped Maxent models based on raccoon sampling locations from 2012⁻2014 indicated that soil type was the most influential variable in Alabama (permutation importance PI = 38.3), which, based on its relation to landcover type and resource distribution and abundance, was unsurprising. Precipitation (PI = 46.9) and temperature (PI = 52.1) were the most important variables in Massachusetts and Florida, but these possibly spurious results require further investigation. The Alabama Maxent probability surface map was ingested into Circuitscape for conductance visualizations of potential areas of habitat connectivity. Incorporating these and future results into raccoon rabies containment and elimination strategies could result in significant cost-savings for rabies management here and elsewhere.

Estimating Tropical Forest Structure Using a Terrestrial Lidar.

Forest structure comprises numerous quantifiable biometric components and characteristics, which include tree geometry and stand architecture. These structural components are important in the understanding of the past and future trajectories of these biomes. Tropical forests are often considered the most structurally complex and yet least understood of forested ecosystems. New technologies have provided novel avenues for quantifying biometric properties of forested ecosystems, one of which is LIght Detection And Ranging (lidar). This sensor can be deployed on satellite, aircraft, unmanned aerial vehicles, and terrestrial platforms. In this study we examined the efficacy of a terrestrial lidar scanner (TLS) system in a tropical forest to estimate forest structure. Our study was conducted in January 2012 at La Selva, Costa Rica at twenty locations in a predominantly undisturbed forest. At these locations we collected field measured biometric attributes using a variable plot design. We also collected TLS data from the center of each plot. Using this data we developed relative vegetation profiles (RVPs) and calculated a series of parameters including entropy, Fast Fourier Transform (FFT), number of layers and plant area index to develop statistical relationships with field data. We developed statistical models using a series of multiple linear regressions, all of which converged on significant relationships with the strongest relationship being for mean crown depth (r2 = 0.88, p < 0.001, RMSE = 1.04 m). Tree density was found to have the poorest significant relationship (r2 = 0.50, p < 0.01, RMSE = 153.28 n ha-1). We found a significant relationship between basal area and lidar metrics (r2 = 0.75, p < 0.001, RMSE = 3.76 number ha-1). Parameters selected in our models varied, thus indicating the potential relevance of multiple features in canopy profiles and geometry that are related to field-measured structure. Models for biomass estimation included structural canopy variables in addition to height metrics. Our work indicates that vegetation profiles from TLS data can provide useful information on forest structure.

Structural Dynamics of Tropical Moist Forest Gaps.

Gap phase dynamics are the dominant mode of forest turnover in tropical forests. However, gap processes are infrequently studied at the landscape scale. Airborne lidar data offer detailed information on three-dimensional forest structure, providing a means to characterize fine-scale (1 m) processes in tropical forests over large areas. Lidar-based estimates of forest structure (top down) differ from traditional field measurements (bottom up), and necessitate clear-cut definitions unencumbered by the wisdom of a field observer. We offer a new definition of a forest gap that is driven by forest dynamics and consistent with precise ranging measurements from airborne lidar data and tall, multi-layered tropical forest structure. We used 1000 ha of multi-temporal lidar data (2008, 2012) at two sites, the Tapajos National Forest and Ducke Reserve, to study gap dynamics in the Brazilian Amazon. Here, we identified dynamic gaps as contiguous areas of significant growth, that correspond to areas > 10 m2, with height <10 m. Applying the dynamic definition at both sites, we found over twice as much area in gap at Tapajos National Forest (4.8%) as compared to Ducke Reserve (2.0%). On average, gaps were smaller at Ducke Reserve and closed slightly more rapidly, with estimated height gains of 1.2 m y-1 versus 1.1 m y-1 at Tapajos. At the Tapajos site, height growth in gap centers was greater than the average height gain in gaps (1.3 m y-1 versus 1.1 m y-1). Rates of height growth between lidar acquisitions reflect the interplay between gap edge mortality, horizontal ingrowth and gap size at the two sites. We estimated that approximately 10% of gap area closed via horizontal ingrowth at Ducke Reserve as opposed to 6% at Tapajos National Forest. Height loss (interpreted as repeat damage and/or mortality) and horizontal ingrowth accounted for similar proportions of gap area at Ducke Reserve (13% and 10%, respectively). At Tapajos, height loss had a much stronger signal (23% versus 6%) within gaps. Both sites demonstrate limited gap contagiousness defined by an increase in the likelihood of mortality in the immediate vicinity (~6 m) of existing gaps.

Modelling associations between public understanding, engagement and forest conditions in the Inland Northwest, USA.

Opinions about public lands and the actions of private non-industrial forest owners in the western United States play important roles in forested landscape management as both public and private forests face increasing risks from large wildfires, pests and disease. This work presents the responses from two surveys, a random-sample telephone survey of more than 1500 residents and a mail survey targeting owners of parcels with 10 or more acres of forest. These surveys were conducted in three counties (Wallowa, Union, and Baker) in northeast Oregon, USA. We analyze these survey data using structural equation models in order to assess how individual characteristics and understanding of forest management issues affect perceptions about forest conditions and risks associated with declining forest health on public lands. We test whether forest understanding is informed by background, beliefs, and experiences, and whether as an intervening variable it is associated with views about forest conditions on publicly managed forests. Individual background characteristics such as age, gender and county of residence have significant direct or indirect effects on our measurement of understanding. Controlling for background factors, we found that forest owners with higher self-assessed understanding, and more education about forest management, tend to hold more pessimistic views about forest conditions. Based on our results we argue that self-assessed understanding, interest in learning, and willingness to engage in extension activities together have leverage to affect perceptions about the risks posed by declining forest conditions on public lands, influence land owner actions, and affect support for public policies. These results also have broader implications for management of forested landscapes on public and private lands amidst changing demographics in rural communities across the Inland Northwest where migration may significantly alter the composition of forest owner goals, understanding, and support for various management actions.

Effects of Invasive Winter Moth Defoliation on Tree Radial Growth in Eastern Massachusetts, USA.

Winter moth, Operophtera brumata L. (Lepidoptera: Geometridae), has been defoliating hardwood trees in eastern Massachusetts since the 1990s. Native to Europe, winter moth has also been detected in Rhode Island, Connecticut, eastern Long Island (NY), New Hampshire, and Maine. Individual tree impacts of winter moth defoliation in New England are currently unknown. Using dendroecological techniques, this study related annual radial growth of individual host (Quercus spp. and Acer spp.) trees to detailed defoliation estimates. Winter moth defoliation was associated with up to a 47% reduction in annual radial growth of Quercus trees. Latewood production of Quercus was reduced by up to 67% in the same year as defoliation, while earlywood production was reduced by up to 24% in the year following defoliation. Winter moth defoliation was not a strong predictor of radial growth in Acer species. This study is the first to document impacts of novel invasions of winter moth into New England.

Exploring critical uncertainties in pathway assessments of human-assisted introductions of alien forest species in Canada.

Long-distance introductions of alien species are often driven by socioeconomic factors, such that conventional "biological" invasion models may not be capable of estimating spread fully and reliably. In this study, we demonstrate a new technique for assessing and reconstructing human-mediated pathways of alien forest species entries to major settlements in Canada via commercial road transportation and domestic trade. We undertook our analysis in three steps. First, we used existing data on movement of commodities associated with bark- and wood-boring forest pests to build a probabilistic model of how the organisms may be moved from one location to another through a transportation network. We then used this model to generate multiple sets of predictions of species arrival rates at every location in the transportation network, and to identify the locations with the highest likelihood of new incursions. Finally, we evaluated the sensitivity of the species arrival rates to uncertainty in key model assumptions by testing the impact of additive and multiplicative errors (by respectively adding a uniform random variate or symmetric variation bounds to the arrival rate values) on the probabilities of pest transmission from one location to another, as well as the impact of the removal of one or more nodes and all connecting links to other nodes from the underlying transportation network. Overall, the identification of potential pest arrival hotspots is moderately robust to uncertainties in key modeling assumptions. Large urban areas and major border crossings that have the highest predicted species arrival rates have the lowest sensitivities to uncertainty in the pest transmission potential and to random changes in the structure of the transportation network. The roadside survey data appears to be sufficient to delineate major hubs and hotspots where pests are likely to arrive from other locations in the network via commercial truck transport. However, "pass-through" locations with few incoming and outgoing routes can be identified with lower precision. The arrival rates of alien forest pests appear to be highly sensitive to additive errors. Surprisingly, the impact of random changes in the structure of the transportation network was relatively low.

Carbon benefits from protected areas in the conterminous United States.

BACKGROUND: Conversion of forests to other land cover or land use releases the carbon stored in the forests and reduces carbon sequestration potential of the land. The rate of forest conversion could be reduced by establishing protected areas for biological diversity and other conservation goals. The purpose of this study is to quantify the efficiency and potential of forest land protection for mitigating GHG emissions. RESULTS: The analysis of related national-level datasets shows that during the period of 1992-2001 net forest losses in protected areas were small as compared to those in unprotected areas: -0.74% and -4.07%, respectively. If forest loss rates in protected and unprotected area had been similar, then forest losses in the protected forestlands would be larger by 870 km2/yr forests, that corresponds to release of 7 Tg C/yr (1 Tg=1012 g). Conversely, and continuing to assume no leakage effects or interactions of prices and harvest levels, about 1,200 km2/yr forests could have remained forest during the period of 1992-2001 if net area loss rate in the forestland outside protected areas was reduced by 20%. Not counting carbon in harvested wood products, this is equivalent to reducing fossil-fuel based carbon emissions by 10 Tg C/yr during this period. The South and West had much higher potentials to mitigate GHG emission from reducing loss rates in unprotected forests than that of North region. Spatially, rates of forest loss were higher across the coastal states in the southeastern US than would be expected from their population change, while interior states in the northern US experienced less forest area loss than would have been expected given their demographic characteristics. CONCLUSIONS: The estimated carbon benefit from the reduced forest loss based on current protected areas is 7 Tg C/yr, equivalent to the average carbon benefit per year for a previously proposed ten-year $110 million per year tree planting program scenario in the US. If there had been a program that could have reduced forest area loss by 20% in unprotected forestlands during 1992-2001, collectively the benefits from reduced forest loss would be equal to 9.4% of current net forest ecosystem carbon sequestration in the conterminous US.

Mercury in breeding saltmarsh sparrows (Ammodramus caudacutus caudacutus).

Environmental mercury exposure of birds through atmospheric deposition and watershed point-source contamination is an issue of increasing concern globally. The saltmarsh sparrow (Ammodramus caudacutus) is of high conservation concern throughout its range and the potential threat of mercury exposure adds to other anthropogenic stressors, including sea level rise. To assess methylmercury exposure we sampled blood of the northern nominal subspecies of saltmarsh sparrows (A. c. caudacutus) nesting in 21 tidal marshes throughout most of the species' breeding range. Blood of tree swallows (Tachycineta bicolor) was sampled concurrently at three of these sites to provide a comparison with a well-studied songbird that is a model species in ecotoxicology. Arithmetic means (±1 SD) ranged from 0.24 ± 0.06 µg g(-1) wet weight (ww) in Connecticut to 1.80 ± 0.14 µg g(-1) ww in Massachusetts, differing significantly among sites. Comparison to tree swallows indicates that mercury exposure is significantly higher in saltmarsh sparrows, making them a more appropriate bioindicator for assessing risk to methylmercury toxicity in tidal marsh ecosystems.

Relationships between major ownerships, forest aboveground biomass distributions, and landscape dynamics in the New England region of USA.

This study utilizes remote sensing derived forest aboveground biomass (AGB) estimates and ownership information obtained from the Protected Areas Database (PAD), combining landscape analyses and GIS techniques to demonstrate how different ownerships (public, regulated private, and other private) relate to the spatial distribution of AGB in New England states of the USA. "Regulated private" lands were dominated by lands in Maine covered by a Land Use Regulatory Commission. The AGB means between all pairs of the identified ownership categories were significantly different (P < 0.05). Mean AGB observed in public lands (156 Mg/ha) was 43% higher than that in regulated private lands (109 Mg/ha), or 30% higher than that of private lands as a whole. Seventy-seven percent of the regional forests (or about 9,300 km(2)) with AGB >200 Mg/ha were located outside the area designated in the PAD and concentrated in western MA, southern VT, southwestern NH, and northwestern CT. While relatively unfragmented and high-AGB forests (>200 Mg/ha) accounted for about 8% of total forested land, they were unevenly proportioned among the three major ownership groups across the region: 19.6% of the public land, 0.8% of the regulated private land, and 11.0% of the other private land. Mean disturbance rates (in absolute value) between 1992 and 2001 were 16, 66, and 19 percent, respectively, on public, regulated private, and other private land. This indicates that management practices from different ownerships have a strong impact on dynamic changes of landscape structures and AGB distributions. Our results may provide insight information for policy makers on issues regarding forest carbon management, conservation biology, and biodiversity studies at regional level.

Biomass equations for forest regrowth in the eastern Amazon using randomized branch sampling / Equações alométricas para estimativa de biomassa de floresta secundária na Amazônia Oriental usando amostragem aleatória de ramos

Forest regrowth occupies an extensive and increasing area in the Amazon basin, but accurate assessment of the impact of regrowth on carbon and nutrient cycles has been hampered by a paucity of available allometric equations. We develop pooled and species-specific equations for total aboveground biomass for a study site in the eastern Amazon that had been abandoned for 15 years. Field work was conducted using randomized branch sampling, a rapid technique that has seen little use in tropical forests. High consistency of sample paths in randomized branch sampling, as measured by the standard error of individual paths (14 percent), suggests the method may provide substantial efficiencies when compared to traditional procedures. The best fitting equations in this study used the traditional form Y=a×DBHb, where Y is biomass, DBH is diameter at breast height, and a and b are both species-specific parameters. Species-specific equations of the form Y=a(BA×H), where Y is biomass, BA is tree basal area, H is tree height, and a is a species-specific parameter, fit almost as well. Comparison with previously published equations indicated errors from -33 percent to +29 percent would have occurred using off-site relationships. We also present equations for stemwood, twigs, and foliage as biomass components.

Florestas secundárias ocupam uma área extensa e crescente na bacia Amazônica, porém determinações acuradas do impacto dessas florestas nos ciclos de carbono e nutrientes têm sido dificultadas pelo número reduzido de equações alométricas. Neste estudo, nós desenvolvemos equações em nível de comunidade e espécies individuais para estimar a biomassa total da parte aérea de uma floresta secundária com 15 anos de idade na Amazônia oriental. O trabalho de campo utilizou amostragem aleatória de ramos, que é uma técnica rápida, porém pouco utilizada em florestas tropicais. Baseada no erro padrão da série de segmentos individuais (14 por cento), a consistência da série de segmentos totais amostrados foi considerada elevada, sugerindo que o método pode ser eficiente em comparação com procedimentos tradicionais. Os melhores ajustes foram obtidos com a equação tradicional Y=a×DBHb, onde Y é a biomassa, DBH é o diâmetro à altura do peito, e a e b são parâmetros para cada espécie arbórea. Ajustes razoáveis também foram alcançados com equações da forma Y=a(BA×H), onde Y é a biomassa, BA é a área basal, H é a altura e a é um parâmetro específico para cada espécie arbórea. Comparações com equações disponíveis na literatura indicaram uma faixa de erro provável de -33 por cento a +29 por cento usando-se relações desenvolvidas para outros sítios. Nós também apresentamos equações para os seguintes componentes da biomassa da parte aérea: tronco, ramos e folhas.

Satellite detection of land-use change and effects on regional forest aboveground biomass estimates.

We used remote-sensing-driven models to detect land-cover change effects on forest aboveground biomass (AGB) density (Mg.ha(-1), dry weight) and total AGB (Tg) in Minnesota, Wisconsin, and Michigan USA, between the years 1992-2001, and conducted an evaluation of the approach. Inputs included remotely-sensed 1992 reflectance data and land-cover map (University of Maryland) from Advanced Very High Resolution Radiometer (AVHRR) and 2001 products from Moderate Resolution Imaging Spectroradiometer (MODIS) at 1-km resolution for the region; and 30-m resolution land-cover maps from the National Land Cover Data (NLCD) for a subarea to conduct nine simulations to address our questions. Sensitivity analysis showed that (1) AVHRR data tended to underestimate AGB density by 11%, on average, compared to that estimated using MODIS data; (2) regional mean AGB density increased slightly from 124 (1992) to 126 Mg ha(-1) (2001) by 1.6%; (3) a substantial decrease in total forest AGB across the region was detected, from 2,507 (1992) to 1,961 Tg (2001), an annual rate of -2.4%; and (4) in the subarea, while NLCD-based estimates suggested a 26% decrease in total AGB from 1992 to 2001, AVHRR/MODIS-based estimates indicated a 36% increase. The major source of uncertainty in change detection of total forest AGB over large areas was due to area differences from using land-cover maps produced by different sources. Scaling up 30-m land-cover map to 1-km resolution caused a mean difference of 8% (in absolute value) in forest area estimates at the county-level ranging from 0 to 17% within a 95% confidence interval.

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.