Bee species visiting Medicago sativa differ in pollen deposition curves with consequences for gene flow.

PREMISE: Pollinator foraging behavior can influence pollen dispersal and gene flow. In many plant species a pollinator trips a flower by applying pressure to release its sexual organs. We propose that differences in tripping rate among grooming pollinators could generate distinct pollen deposition curves, the pattern of pollen deposition over successive flowers visited. This study compares the pollen deposition curves of two grooming pollinators, a social bumble bee and a solitary leafcutting bee, with distinct tripping rates on Medicago sativa flowers. We predict a steeper deposition curve for pollen moved by leafcutting bees, the pollinator with the higher tripping rate. METHODS: Medicago sativa plants carrying a gene (GUS) whose product is easily detected by staining, were used as pollen donors. After visiting the GUS plants, a bee was released on a linear array of conventional M. sativa plants. The number of GUS pollen grains deposited over successive flowers visited or over cumulative distances was examined. Distinct mixed effect Poisson regression models, illustrating different rates of decay in pollen deposition, were fitted to the pollen data for each bee species. RESULTS: Pollen decay was steeper for leafcutting bees relative to bumble bees for both models of flowers visited and cumulative distance, as predicted by their higher tripping rate. CONCLUSIONS: This is the first report of a difference in pollen deposition curves between two bee species, both grooming pollinators. Such differences could lead to distinct impacts of bee species on gene flow, genetic differentiation, introgression, and ultimately speciation.

Floral traits influencing plant attractiveness to three bee species: Consequences for plant reproductive success.

PREMISE OF THE STUDY: The ability to attract pollinators is crucial to plants that rely on insects for pollination. We contrasted the roles of floral display size and flower color in attracting three bee species and determined the relationships between plant attractiveness (number of pollinator visits) and seed set for each bee species. METHODS: We recorded pollinator visits to plants, measured plant traits, and quantified plant reproductive success. A zero-inflated Poisson regression model indicated plant traits associated with pollinator attraction. It identified traits that increased the number of bee visits and traits that increased the probability of a plant not receiving any visits. Different components of floral display size were examined and two models of flower color contrasted. Relationships between plant attractiveness and seed set were determined using regression analyses. KEY RESULTS: Plants with more racemes received more bee visits from all three bee species. Plants with few racemes were more likely not to receive any bee visits. The role of flower color varied with bee species and was influenced by the choice of the flower color model. Increasing bee visits increased seed set for all three bee species, with the steepest slope for leafcutting bees, followed by bumble bees, and finally honey bees. CONCLUSIONS: Floral display size influenced pollinator attraction more consistently than flower color. The same plant traits affected the probability of not being visited and the number of pollinator visits received. The impact of plant attractiveness on female reproductive success varied, together with pollinator effectiveness, by pollinator species.

Factors related to building loss due to wildfires in the conterminous United States.

Wildfire is globally an important ecological disturbance affecting biochemical cycles and vegetation composition, but also puts people and their homes at risk. Suppressing wildfires has detrimental ecological effects and can promote larger and more intense wildfires when fuels accumulate, which increases the threat to buildings in the wildland-urban interface (WUI). Yet, when wildfires occur, typically only a small proportion of the buildings within the fire perimeter are lost, and the question is what determines which buildings burn. Our goal was to examine which factors are related to building loss when a wildfire occurs throughout the United States. We were particularly interested in the relative roles of vegetation, topography, and the spatial arrangement of buildings, and how their respective roles vary among ecoregions. We analyzed all fires that occurred within the conterminous United States from 2000 to 2010 and digitized which buildings were lost and which survived according to Google Earth historical imagery. We modeled the occurrence as well as the percentage of buildings lost within clusters using logistic and linear regression. Overall, variables related to topography and the spatial arrangement of buildings were more frequently present in the best 20 regression models than vegetation-related variables. In other words, specific locations in the landscape have a higher fire risk, and certain development patterns can exacerbate that risk. Fire policies and prevention efforts focused on vegetation management are important, but insufficient to solve current wildfire problems. Furthermore, the factors associated with building loss varied considerably among ecoregions suggesting that fire policy applied uniformly across the United States will not work equally well in all regions and that efforts to adapt communities to wildfires must be regionally tailored.

Human and biophysical influences on fire occurrence in the United States.

National-scale analyses of fire occurrence are needed to prioritize fire policy and management activities across the United States. However, the drivers of national-scale patterns of fire occurrence are not well understood, and how the relative importance of human or biophysical factors varies across the country is unclear. Our research goal was to model the drivers of fire occurrence within ecoregions across the conterminous United States. We used generalized linear models to compare the relative influence of human, vegetation, climate, and topographic variables on fire occurrence in the United States, as measured by MODIS active fire detections collected between 2000 and 2006. We constructed models for all fires and for large fires only and generated predictive maps to quantify fire occurrence probabilities. Areas with high fire occurrence probabilities were widespread in the Southeast, and localized in the Mountain West, particularly in southern California, Arizona, and New Mexico. Probabilities for large-fire occurrence were generally lower, but hot spots existed in the western and south-central United States The probability of fire occurrence is a critical component of fire risk assessments, in addition to vegetation type, fire behavior, and the values at risk. Many of the hot spots we identified have extensive development in the wildland--urban interface and are near large metropolitan areas. Our results demonstrated that human variables were important predictors of both all fires and large fires and frequently exhibited nonlinear relationships. However, vegetation, climate, and topography were also significant variables in most ecoregions. If recent housing growth trends and fire occurrence patterns continue, these areas will continue to challenge policies and management efforts seeking to balance the risks generated by wildfires with the ecological benefits of fire.

Bee species perform distinct foraging behaviors that are best described by different movement models.

In insect-pollinated plants, the foraging behavior of pollinators affects their pattern of movement. If distinct bee species vary in their foraging behaviors, different models may best describe their movement. In this study, we quantified and compared the fine scale movement of three bee species foraging on patches of Medicago sativa. Bee movement was described using distances and directions traveled between consecutive racemes. Bumble bees and honey bees traveled shorter distances after visiting many flowers on a raceme, while the distance traveled by leafcutting bees was independent of flower number. Transition matrices and vectors were calculated for bumble bees and honey bees to reflect their directionality of movement within foraging bouts; leafcutting bees were as likely to move in any direction. Bee species varied in their foraging behaviors, and for each bee species, we tested four movement models that differed in how distances and directions were selected, and identified the model that best explained the movement data. The fine-scale, within-patch movement of bees could not always be explained by a random movement model, and a general model of movement could not be applied to all bee species.

Glycomacropeptide, a low-phenylalanine protein isolated from cheese whey, supports growth and attenuates metabolic stress in the murine model of phenylketonuria.

Phenylketonuria (PKU) is caused by a mutation in the phenylalanine (phe) hydroxylase gene and requires a low-phe diet plus amino acid (AA) formula to prevent cognitive impairment. Glycomacropeptide (GMP) contains minimal phe and provides a palatable alternative to AA formula. Our objective was to compare growth, body composition, and energy balance in Pah(enu2) (PKU) and wild-type mice fed low-phe GMP, low-phe AA, or high-phe casein diets from 3-23 wk of age. The 2 × 2 × 3 design included main effects of genotype, sex, and diet. Fat and lean mass were assessed by dual-energy X-ray absorptiometry, and acute energy balance was assessed by indirect calorimetry. PKU mice showed growth and lean mass similar to wild-type littermates fed the GMP or AA diets; however, they exhibited a 3-15% increase in energy expenditure, as reflected in oxygen consumption, and a 3-30% increase in food intake. The GMP diet significantly reduced energy expenditure, food intake, and plasma phe concentration in PKU mice compared with the casein diet. The high-phe casein diet or the low-phe AA diet induced metabolic stress in PKU mice, as reflected in increased energy expenditure and intake of food and water, increased renal and spleen mass, and elevated plasma cytokine concentrations consistent with systemic inflammation. The low-phe GMP diet significantly attenuated these adverse effects. Moreover, total fat mass, %body fat, and the respiratory exchange ratio (CO(2) produced/O(2) consumed) were significantly lower in PKU mice fed GMP compared with AA diets. In summary, GMP provides a physiological source of low-phe dietary protein that promotes growth and attenuates the metabolic stress induced by a high-phe casein or low-phe AA diet in PKU mice.

Land-cover change and avian diversity in the conterminous United States.

Changes in land use and land cover have affected and will continue to affect biological diversity worldwide. Yet, understanding the spatially extensive effects of land-cover change has been challenging because data that are consistent over space and time are lacking. We used the U.S. National Land Cover Dataset Land Cover Change Retrofit Product and North American Breeding Bird Survey data to examine land-cover change and its associations with diversity of birds with principally terrestrial life cycles (landbirds) in the conterminous United States. We used mixed-effects models and model selection to rank associations by ecoregion. Land cover in 3.22% of the area considered in our analyses changed from 1992 to 2001, and changes in species richness and abundance of birds were strongly associated with land-cover changes. Changes in species richness and abundance were primarily associated with changes in nondominant types of land cover, yet in many ecoregions different types of land cover were associated with species richness than were associated with abundance. Conversion of natural land cover to anthropogenic land cover was more strongly associated with changes in bird species richness and abundance than persistence of natural land cover in nearly all ecoregions and different covariates were most strongly associated with species richness than with abundance in 11 of 17 ecoregions. Loss of grassland and shrubland affected bird species richness and abundance in forested ecoregions. Loss of wetland was associated with bird abundance in forested ecoregions. Our findings highlight the value of understanding changes in nondominant land cover types and their association with bird diversity in the United States.

An evaluation of prior influence on the predictive ability of Bayesian model averaging.

Model averaging is gaining popularity among ecologists for making inference and predictions. Methods for combining models include Bayesian model averaging (BMA) and Akaike's Information Criterion (AIC) model averaging. BMA can be implemented with different prior model weights, including the Kullback-Leibler prior associated with AIC model averaging, but it is unclear how the prior model weight affects model results in a predictive context. Here, we implemented BMA using the Bayesian Information Criterion (BIC) approximation to Bayes factors for building predictive models of bird abundance and occurrence in the Chihuahuan Desert of New Mexico. We examined how model predictive ability differed across four prior model weights, and how averaged coefficient estimates, standard errors and coefficients' posterior probabilities varied for 16 bird species. We also compared the predictive ability of BMA models to a best single-model approach. Overall, Occam's prior of parsimony provided the best predictive models. In general, the Kullback-Leibler prior, however, favored complex models of lower predictive ability. BMA performed better than a best single-model approach independently of the prior model weight for 6 out of 16 species. For 6 other species, the choice of the prior model weight affected whether BMA was better than the best single-model approach. Our results demonstrate that parsimonious priors may be favorable over priors that favor complexity for making predictions. The approach we present has direct applications in ecology for better predicting patterns of species' abundance and occurrence.

Historical forest baselines reveal potential for continued carbon sequestration.

One-third of net CO(2) emissions to the atmosphere since 1850 are the result of land-use change, primarily from the clearing of forests for timber and agriculture, but quantifying these changes is complicated by the lack of historical data on both former ecosystem conditions and the extent and spatial configuration of subsequent land use. Using fine-resolution historical survey records, we reconstruct pre-EuroAmerican settlement (1850s) forest carbon in the state of Wisconsin, examine changes in carbon after logging and agricultural conversion, and assess the potential for future sequestration through forest recovery. Results suggest that total above-ground live forest carbon (AGC) fell from 434 TgC before settlement to 120 TgC at the peak of agricultural clearing in the 1930s and has since recovered to approximately 276 TgC. The spatial distribution of AGC, however, has shifted significantly. Former savanna ecosystems in the south now store more AGC because of fire suppression and forest ingrowth, despite the fact that most of the region remains in agriculture, whereas northern forests still store much less carbon than before settlement. Across the state, continued sequestration in existing forests has the potential to contribute an additional 69 TgC. Reforestation of agricultural lands, in particular, the formerly high C-density forests in the north-central region that are now agricultural lands less optimal than those in the south, could contribute 150 TgC. Restoring historical carbon stocks across the landscape will therefore require reassessing overall land-use choices, but a range of options can be ranked and considered under changing needs for ecosystem services.

Terrestrial, benthic, and pelagic resource use in lakes: results from a three-isotope Bayesian mixing model.

Fluxes of organic matter across habitat boundaries are common in food webs. These fluxes may strongly influence community dynamics, depending on the extent to which they are used by consumers. Yet understanding of basal resource use by consumers is limited, because describing trophic pathways in complex food webs is difficult. We quantified resource use for zooplankton, zoobenthos, and fishes in four low-productivity lakes, using a Bayesian mixing model and measurements of hydrogen, carbon, and nitrogen stable isotope ratios. Multiple sources of uncertainty were explicitly incorporated into the model. As a result, posterior estimates of resource use were often broad distributions; nevertheless, clear patterns were evident. Zooplankton relied on terrestrial and pelagic primary production, while zoobenthos and fishes relied on terrestrial and benthic primary production. Across all consumer groups terrestrial reliance tended to be higher, and benthic reliance lower, in lakes where light penetration was low due to inputs of terrestrial dissolved organic carbon. These results support and refine an emerging consensus that terrestrial and benthic support of lake food webs can be substantial, and they imply that changes in the relative availability of basal resources drive the strength of cross-habitat trophic connections.

Patch selection by bumble bees navigating discontinuous landscapes.

Pollen and nectar resources are unevenly distributed over space and bees must make routing decisions when navigating patchy resources. Determining the patch selection process used by bees is crucial to understanding bee foraging over discontinuous landscapes. To elucidate this process, we developed four distinct probability models of bee movement where the size and the distance to the patch determined the attractiveness of a patch. A field experiment with a center patch and four peripheral patches of two distinct sizes and distances from the center was set up in two configurations. Empirical transition probabilities from the center to each peripheral patch were obtained at two sites and two years. The best model was identified by comparing observed and predicted transition probabilities, where predicted values were obtained by incorporating the spatial dimensions of the field experiment into each model's mathematical expression. Bumble bees used both patch size and isolation distance when selecting a patch and could assess the total amount of resources available in a patch. Bumble bees prefer large, nearby patches. This information will facilitate the development of a predictive framework to the study of bee movement and of models that predict the movement of genetically engineered pollen in bee-pollinated crops.

Breakfast with glycomacropeptide compared with amino acids suppresses plasma ghrelin levels in individuals with phenylketonuria.

Phenylketonuria (PKU) requires a lifelong low-phenylalanine (phe) diet where protein needs are met by consumption of a phe-free amino acid (AA) formula; complaints of persistent hunger are common. Foods made with glycomacropeptide (GMP), an intact protein that contains minimal phe and may promote satiety, provide an alternative to AA formula. The objective was to assess the ability of a GMP breakfast to promote satiety and affect plasma concentrations of AAs, insulin, and the appetite stimulating hormone ghrelin in those with PKU, when compared to an AA-based breakfast. Eleven PKU subjects (8 adults and 3 boys ages 11-14) served as their own controls in an inpatient metabolic study with two 4-day treatments: an AA-based diet followed by a diet replacing all AA formula with GMP foods. Plasma concentrations of AAs, insulin and ghrelin were obtained before and/or 180 min after breakfast. Satiety was assessed using a visual analog scale before, immediately after and 150 min after breakfast. Postprandial ghrelin concentration was significantly lower (p=0.03) with GMP compared to an AA-based breakfast, with no difference in fasting ghrelin. Lower postprandial ghrelin concentrations were associated with greater feelings of fullness after breakfast suggesting greater satiety with GMP compared to AAs. Postprandial concentrations of insulin and total plasma AAs were higher after a GMP breakfast compared to an AA-based breakfast consistent with slower absorption and less degradation of AAs from GMP. These results show sustained ghrelin suppression, and suggest greater satiety with ingestion of a meal containing GMP compared with AAs.

Vegetation productivity summarized by the Dynamic Habitat Indices explains broad-scale patterns of moose abundance across Russia.

Identifying the factors that determine habitat suitability and hence patterns of wildlife abundances over broad spatial scales is important for conservation. Ecosystem productivity is a key aspect of habitat suitability, especially for large mammals. Our goals were to a) explain patterns of moose (Alces alces) abundance across Russia based on remotely sensed measures of vegetation productivity using Dynamic Habitat Indices (DHIs), and b) examine if patterns of moose abundance and productivity differed before and after the collapse of the Soviet Union. We evaluated the utility of the DHIs using multiple regression models predicting moose abundance by administrative regions. Univariate models of the individual DHIs had lower predictive power than all three combined. The three DHIs together with environmental variables, explained 79% of variation in moose abundance. Interestingly, the predictive power of the models was highest for the 1980s, and decreased for the two subsequent decades. We speculate that the lower predictive power of our environmental variables in the later decades may be due to increasing human influence on moose densities. Overall, we were able to explain patterns in moose abundance in Russia well, which can inform wildlife managers on the long-term patterns of habitat use of the species.

Legacies of historical land use on regional forest composition and structure in Wisconsin, USA (mid-1800s-1930s-2000s).

Historical land use can influence forest species composition and structure for centuries after direct use has ceased. In Wisconsin, USA, Euro-American settlement in the mid- to late 1800s was accompanied by widespread logging, agricultural conversion, and fire suppression. To determine the maximum magnitude of change in forest ecosystems at the height of the agricultural period and the degree of recovery since that time, we assessed changes in forest species composition and structure among the (1) mid-1800s, at the onset of Euro-American settlement; (2) 1930s, at the height of the agricultural period; and (3) 2000s, following forest regrowth. Data sources included the original U.S. Public Land Survey records (mid-1800s), the Wisconsin Land Economic Inventory (1930s), and U.S. Forest Service Forest Inventory and Analysis data (2000s). We derived maps of relative species dominance and tree diameters for the three dates and assessed change using spatial error models, nonmetric multidimensional scaling ordination, and Sørenson distance measures. Our results suggest that since the mid-1800s, hemlock and white pine have declined in absolute area from 22% to 1%, and the proportion of medium (25-<50 cm) and large-diameter (> or = 50 cm) trees of all species has decreased from 71% to 27% across the entire state. Early-successional aspen-birch is three times more common than in the mid-1800s (9% vs. 3%), and maple and other shade-tolerant species are increasing in southern areas formerly dominated by oak forests and savannas. Since the peak agricultural extent in the 1930s, species composition and tree size in northern forests have shown some recovery, while southern forests appear to be on a novel trajectory of change. There is evidence of regional homogenization, but the broad north-south environmental gradient in Wisconsin constrains overall species composition. Although the nature of the future forests will be determined in part by climate change and other exogenous variables, land use is likely to remain the driving factor.

Linking the foraging behavior of three bee species to pollen dispersal and gene flow.

Foraging behaviors that impact gene flow can guide the design of pollinator strategies to mitigate gene flow. Reduced gene flow is expected to minimize the impact of genetically engineered (GE) crops on feral and natural populations and to facilitate the coexistence of different agricultural markets. The goal of this study is to link foraging behavior to gene flow and identify behaviors that can help predict gene flow for different bee species. To reach this goal, we first examined and compared the foraging behaviors of three distinct bee species, the European honey bee, Apis mellifera L., the common eastern bumble bee, Bombus impatiens Cr., and the alfalfa leafcutting bee, Megachile rotundata F., foraging on Medicago sativa flowers. Each foraging behavior investigated differed among bee species. Both social bees exhibited directionality of movement and had similar residence, in contrast to the random movement and shorter residence of the solitary bee. Tripping rate and net distance traveled differed among the three bee species. We ranked each behavior among bee species and used the relative ranking as gene flow predictor before testing the predictions against empirical gene flow data. Tripping rate and net distance traveled, but not residence, predicted relative gene dispersal among bee species. Linking specific behaviors to gene flow provides mechanisms to explain differences in gene flow among bee species and guides the development of management practices to reduce gene flow. Although developed in one system, the approach developed here can be generalized to different plant/pollinator systems.

Comparing Genome-Wide Association Study Results from Different Measurements of an Underlying Phenotype.

Increasing popularity of high-throughput phenotyping technologies, such as image-based phenotyping, offer novel ways for quantifying plant growth and morphology. These new methods can be more or less accurate and precise than traditional, manual measurements. Many large-scale phenotyping efforts are conducted to enable genome-wide association studies (GWAS), but it is unclear exactly how alternative methods of phenotyping will affect GWAS results. In this study we simulate phenotypes that are controlled by the same set of causal loci but have differing heritability, similar to two different measurements of the same morphological character. We then perform GWAS with the simulated traits and create receiver operating characteristic (ROC) curves from the results. The areas under the ROC curves (AUCs) provide a metric that allows direct comparisons of GWAS results from different simulated traits. We use this framework to evaluate the effects of heritability and the number of causative loci on the AUCs of simulated traits; we also test the differences between AUCs of traits with differing heritability. We find that both increasing the number of causative loci and decreasing the heritability reduce a trait's AUC. We also find that when two traits are controlled by a greater number of causative loci, they are more likely to have significantly different AUCs as the difference between their heritabilities increases. When simulation results are applied to measures of tassel morphology, we find no significant difference between AUCs from GWAS using manual and image-based measurements of typical maize tassel characters. This finding indicates that both measurement methods have similar ability to identify genetic associations. These results provide a framework for deciding between competing phenotyping strategies when the ultimate goal is to generate and use phenotype-genotype associations from GWAS.

Human influence on California fire regimes.

Periodic wildfire maintains the integrity and species composition of many ecosystems, including the mediterranean-climate shrublands of California. However, human activities alter natural fire regimes, which can lead to cascading ecological effects. Increased human ignitions at the wildland-urban interface (WUI) have recently gained attention, but fire activity and risk are typically estimated using only biophysical variables. Our goal was to determine how humans influence fire in California and to examine whether this influence was linear, by relating contemporary (2000) and historic (1960-2000) fire data to both human and biophysical variables. Data for the human variables included fine-resolution maps of the WUI produced using housing density and land cover data. Interface WUI, where development abuts wildland vegetation, was differentiated from intermix WUI, where development intermingles with wildland vegetation. Additional explanatory variables included distance to WUI, population density, road density, vegetation type, and ecoregion. All data were summarized at the county level and analyzed using bivariate and multiple regression methods. We found highly significant relationships between humans and fire on the contemporary landscape, and our models explained fire frequency (R2 = 0.72) better than area burned (R2 = 0.50). Population density, intermix WUI, and distance to WUI explained the most variability in fire frequency, suggesting that the spatial pattern of development may be an important variable to consider when estimating fire risk. We found nonlinear effects such that fire frequency and area burned were highest at intermediate levels of human activity, but declined beyond certain thresholds. Human activities also explained change in fire frequency and area burned (1960-2000), but our models had greater explanatory power during the years 1960-1980, when there was more dramatic change in fire frequency. Understanding wildfire as a function of the spatial arrangement of ignitions and fuels on the landscape, in addition to nonlinear relationships, will be important to fire managers and conservation planners because fire risk may be related to specific levels of housing density that can be accounted for in land use planning. With more fires occurring in close proximity to human infrastructure, there may also be devastating ecological impacts if development continues to grow farther into wildland vegetation.

Patterns of houses and habitat loss from 1937 to 1999 in northern Wisconsin, USA.

Rural America is witnessing widespread housing development, which is to the detriment of the environment. It has been suggested to cluster houses so that their disturbance zones overlap and thus cause less habitat loss than is the case for dispersed development. Clustering houses makes intuitive sense, but few empirical studies have quantified the spatial pattern of houses in real landscapes, assessed changes in their patterns over time, and quantified the resulting habitat loss. We addressed three basic questions: (1) What are the spatial patterns of houses and how do they change over time; (2) How much habitat is lost due to houses, and how is this affected by spatial pattern of houses; and (3) What type of habitat is most affected by housing development. We mapped 27 419 houses from aerial photos for five time periods in 17 townships in northern Wisconsin and calculated the terrestrial land area remaining after buffering each house using 100- and 500-m disturbance zones. The number of houses increased by 353% between 1937 and 1999. Ripley's K test showed that houses were significantly clustered at all time periods and at all scales. Due to the clustering, the rate at which habitat was lost (176% and 55% for 100- and 500-m buffers, respectively) was substantially lower than housing growth rates, and most land area was undisturbed (95% and 61% for 100-m and 500-m buffers, respectively). Houses were strongly clustered within 100 m of lakes. Habitat loss was lowest in wetlands but reached up to 60% in deciduous forests. Our results are encouraging in that clustered development is common in northern Wisconsin, and habitat loss is thus limited. However, the concentration of development along lakeshores causes concern, because these may be critical habitats for many species. Conservation goals can only be met if policies promote clustered development and simultaneously steer development away from sensitive ecosystems.

Road development, housing growth, and landscape fragmentation in northern Wisconsin: 1937-1999.

Roads remove habitat, alter adjacent areas, and interrupt and redirect ecological flows. They subdivide wildlife populations, foster invasive species spread, change the hydrologic network, and increase human use of adjacent areas. At broad scales, these impacts cumulate and define landscape patterns. The goal of this study was to improve our understanding of the dynamics of road networks over time, and their effects on landscape patterns, and identify significant relationships between road changes and other land-use changes. We mapped roads from aerial photographs from five dates between 1937 and 1999 in 17 townships in predominantly forested landscapes in northern Wisconsin, U.S.A. Patch-level landscape metrics were calculated on terrestrial area outside of a 15-m road-effect zone. We used generalized least-squares regression models to relate changes in road density and landscape pattern to concurrent changes in housing density. Rates of change and relationships were compared among three ecological regions. Our results showed substantial increases in both road density and landscape fragmentation during the study period. Road density more than doubled, and median, mean, and largest patch size were reduced by a factor of four, while patch shape became more regular. Increases in road density varied significantly among ecological subsections and were positively related to increases in housing density. Fragmentation was largely driven by increases in road density, but housing density had a significantly positive relationship with largest patch area and patch shape. Without protection of roadless areas, our results suggest road development is likely to continue in the future, even in areas where road construction is constrained by the physical environment. Recognizing the dynamic nature of road networks is important for understanding and predicting their ecological impacts over time and understanding where other types of development are likely to occur in the future. Historical perspectives of development can provide guidance in prioritizing management efforts to defragment landscapes and mitigate the ecological impacts of past road development.

Glycomacropeptide for nutritional management of phenylketonuria: a randomized, controlled, crossover trial.

BACKGROUND: To prevent cognitive impairment, phenylketonuria requires lifelong management of blood phenylalanine (Phe) concentration with a low-Phe diet. The diet restricts intake of Phe from natural proteins in combination with traditional amino acid medical foods (AA-MFs) or glycomacropeptide medical foods (GMP-MFs) that contain primarily intact protein and a small amount of Phe. OBJECTIVE: We investigated the efficacy and safety of a low-Phe diet combined with GMP-MFs or AA-MFs providing the same quantity of protein equivalents in free-living subjects with phenylketonuria. DESIGN: This 2-stage, randomized crossover trial included 30 early-treated phenylketonuria subjects (aged 15-49 y), 20 with classical and 10 with variant phenylketonuria. Subjects consumed, in random order for 3 wk each, their usual low-Phe diet combined with AA-MFs or GMP-MFs. The treatments were separated by a 3-wk washout with AA-MFs. Fasting plasma amino acid profiles, blood Phe concentrations, food records, and neuropsychological tests were obtained. RESULTS: The frequency of medical food intake was higher with GMP-MFs than with AA-MFs. Subjects rated GMP-MFs as more acceptable than AA-MFs and noted improved gastrointestinal symptoms and less hunger with GMP-MFs. ANCOVA indicated no significant mean ± SE increase in plasma Phe (62 ± 40 µmol/L, P = 0.136), despite a significant increase in Phe intake from GMP-MFs (88 ± 6 mg Phe/d, P = 0.026). AA-MFs decreased plasma Phe (-85 ± 40 µmol/L, P = 0.044) with stable Phe intake. Blood concentrations of Phe across time were not significantly different (AA-MFs = 444 ± 34 µmol/L, GMP-MFs = 497 ± 34 µmol/L), suggesting similar Phe control. Results of the Behavior Rating Inventory of Executive Function were not significantly different. CONCLUSIONS: GMP-MFs provide a safe and acceptable option for the nutritional management of phenylketonuria. The greater acceptability and fewer side effects noted with GMP-MFs than with AA-MFs may enhance dietary adherence for individuals with phenylketonuria. This trial was registered at www.clinicaltrials.gov as NCT01428258.