Estimating the frequency and extent of bloodworm digging in Maine from aerial photography

The extent and frequency of bloodworm digging in northeast North America has never been investigated, making bloodworm fishery management difficult and an assessment of ecological impact impossible. We examined the spatial and temporal patterns of bloodworm digging by surveying 13 mudflats in mid-coast Maine using monthly aerial photography. We used ArcGIS to “georeference” spatial information into the photographs, allowing us to quantify the area of the mudflat disturbed by digging. Of the 122 ha of flats we monitored, almost 48% was dug in 2006, and 24% was dug in 2007, corresponding to 57.9 ha and 24.8 ha, respectively. Individual flats in this study were dug as much as 155% and as little as 0% annually, but rarely were they dug more than once a year as studies assessing the impact of digging on soft-sediment communities have assumed. Digging activity peaked in early spring with a smaller peak in August. On average, 3% of the visual evidence of digging faded each month. Except for those erased by winter storms and ice scour, no dug strip faded before 5 months of age, therefore flats could be monitored every 4–5 months for an accurate estimate of digging activity. Our results allow us, for the first time, to estimate the impact of digging on the bloodworm resource and to design realistic experiments to measure the impact of digging on the soft-sediment community and non-target species.     

Production of ethylene by fungi

Ethylene was detected by gas chromatography, and verified by chemical means, as a metabolic product of 22 species of fungi. Because 58 of 228 species of fungi produced a gaseous compound with retention time identical to that of authentic ethylene, we believe that this compound is a common metabolic product of fungi.     

Rainfall interception and fog precipitation in a tropical montane cloud forest of Guatemala

Tropical montane cloud forest hydrology is complex because of the presence of epiphytic life-forms that increase canopy surfaces and fog persistency. Fog precipitation is a hydrological input common to cloud forests, and forms when fog droplets are intercepted by the canopy and fall to the forest floor. Interception and fog precipitation was determined for a 2100 m site and a 2550 m site in a first-order tributary of the Sierra de las Minas Biosphere Reserve, Guatemala by calculating the difference between throughfall and gross precipitation for a 44-week period. Both sites were situated within closed-canopy cloud forests. The 2100 m site was on the windward slope of Montaña de Miranda near the lower boundary of the cloud forest and the 2550 m site was at the summit. Fog precipitation was found during periods in which throughfall exceeds gross precipitation. Fog precipitation was greater at 2550 m than at 2100 m. Data collected by precipitation and throughfall gauges demonstrate the existence of seasonal fog precipitation with the greatest fog precipitation occurring in the dry season (November–April). Fog precipitation contributes approximately 1 mm per day to the hydrological budget of the cloud forest at 2550 m during the dry season, and 0.5 mm per day during the rainy season (May–October).     

Rapeseed meal and egg taint: Treatment of rapeseed meal to reduce tainting potential

1. Treatment of rapeseed meal with calcium hydroxide suspension decreased the sinapine content by up to 90%. Smaller decreases were obtained by autolysis, steaming and treatment with ammonia.

2. When this treated meal was fed to susceptible ("tainting") hens the concentration of trimethylamine in the eggs was decreased to much less than that required to cause taint.     

Regional mixed-effects height–diameter models for loblolly pine (<Emphasis Type="Italic">Pinus taeda</Emphasis> L.) plantations

A height–diameter mixed-effects model was developed for loblolly pine (Pinus taeda L.) plantations in the southeastern US. Data were obtained from a region-wide thinning study established by the Loblolly Pine Growth and Yield Research Cooperative at Virginia Tech. The height–diameter model was based on an allometric function, which was linearized to include both fixed- and random-effects parameters. A test of regional-specific fixed-effects parameters indicated that separate equations were needed to estimate total tree heights in the Piedmont and Coastal Plain physiographic regions. The effect of sample size on the ability to estimate random-effects parameters in a new plot was analyzed. For both regions, an increase in the number of sample trees decreased the bias when the equation was applied to independent data. This investigation showed that the use of a calibrated response using one sample tree per plot makes the inclusion of additional predictor variables (e.g., stand density) unnecessary. A numerical example demonstrates the methodology used to predict random effects parameters, and thus, to estimate plot specific height–diameter relationships.     

A multi-method approach to delineate and validate migratory corridors

Context

Managers are faced with numerous methods for delineating wildlife movement corridors, and often must make decisions with limited data. Delineated corridors should be robust to different data and models.

Objectives

We present a multi-method approach for delineating and validating wildlife corridors using multiple data sources, which can be used conserve landscape connectivity. We used this approach to delineate and validate migration corridors for wildebeest (Connochaetes taurinus) in the Tarangire Ecosystem of northern Tanzania.

Methods

We used two types of locational data (distance sampling detections and GPS collar locations), and three modeling methods (negative binomial regression, logistic regression, and Maxent), to generate resource selection functions (RSFs) and define resistance surfaces. We compared two corridor detection algorithms (cost-distance and circuit theory), to delineate corridors. We validated corridors by comparing random and wildebeest locations that fell within corridors, and cross-validated by data type.

Results

Both data types produced similar RSFs. Wildebeest consistently selected migration habitat in flatter terrain farther from human settlements. Validation indicated three of the combinations of data type, modeling, and corridor detection algorithms (detection data with Maxent modeling, GPS collar data with logistic regression modeling, and GPS collar data with Maxent modeling, all using cost-distance) far outperformed the other seven. We merged the predictive corridors from these three data-method combinations to reveal habitat with highest probability of use.

Conclusions

The use of multiple methods ensures that planning is able to prioritize conservation of migration corridors based on all available information.

     

The influence of scale and the spatial characteristics of landscapes on land-cover mapping using remote sensing

Statistical analyses provide a means for assessing relationships between landscape spatial pattern and errors in the estimates of cover-type proportions as land-cover data are aggregated to coarser scales. Results from a multiple-linear regression model suggest that as patch sizes, variance/mean ratio, and initial proportions of cover types increase, the proportion error moves in a positive direction and is governed by the interaction of the spatial characteristics and the scale of aggregation. However, the standard linear model does not account for the different directions of scale-dependent proportion error since some classes become larger and others become smaller as the scene is aggregated. Addition of indicator variables representing class-type significantly improves the performance by allowing the model to respond differently to different classes. A regression tree model provides a much simpler fit to the complex scaling behavior through an interaction between patch size and aggregation scale. An understanding of the relationships between landscape pattern, scale, and proportion error may advance methods for correcting land-cover area estimates. Such methods could also facilitate high-resolution calibration and validation of coarse-scale remote-sensing-based land-cover mapping algorithms. Ongoing initiatives to produce global land-cover datasets from remote sensing, such as efforts within the IGBP and the EOS MODIS Land-Team, include significant emphasis on high level calibration and validation activities of this nature.     

Factors Associated with Grassland Bird Species Richness: The Relative Roles of Grassland Area, Landscape Structure, and Prey

The factors responsible for widespread declines of grassland birds in the United States are not well understood. This study, conducted in the short-grass prairie of eastern Wyoming, was designed to investigate the relationship between variation in habitat amount, landscape heterogeneity, prey resources, and spatial variation in grassland bird species richness. We estimated bird richness over a 5-year period (1994–1998) from 29 Breeding Bird Survey locations. Estimated bird richness was modeled as a function of landscape structure surrounding survey routes using satellite-based imagery (1996) and grasshopper density and richness, a potentially important prey of grassland birds. Model specification progressed from simple to complex explanations for spatial variation in bird richness. An information-theoretic approach was used to rank and select candidate models. Our best model included measurements of habitat amount, habitat arrangement, landscape matrix, and prey diversity. Grassland bird richness was positively associated with grassland habitat; was negatively associated with habitat dispersion; positively associated with edge habitats; negatively associated with landscape matrix attributes that may restrict movement of grassland bird; and positively related to grasshopper richness. Collectively, 62% of the spatial variation in grassland bird richness was accounted for by the model (adj-R² = 0.514). These results suggest that the distribution of grassland bird species is influenced by a complex mixture of factors that include habitat area affects, landscape pattern and composition, and the availability of prey.     

Climate-forced variability of ocean hypoxia

Oxygen (O(2)) is a critical constraint on marine ecosystems. As oceanic O(2) falls to hypoxic concentrations, habitability for aerobic organisms decreases rapidly. We show that the spatial extent of hypoxia is highly sensitive to small changes in the ocean's O(2) content, with maximum responses at suboxic concentrations where anaerobic metabolisms predominate. In model-based reconstructions of historical oxygen changes, the world's largest suboxic zone, in the Pacific Ocean, varies in size by a factor of 2. This is attributable to climate-driven changes in the depth of the tropical and subtropical thermocline that have multiplicative effects on respiration rates in low-O(2) water. The same mechanism yields even larger fluctuations in the rate of nitrogen removal by denitrification, creating a link between decadal climate oscillations and the nutrient limitation of marine photosynthesis.