TRACE ELEMENTS IN NORTHERN GREAT PLAINS STRIP MINE AND LIVESTOCK IMPOUNDMENT WATER1

ABSTRACT: The water from 32 strip mine water impoundments and nine livestock watering ponds in North Dakota, South Dakota, and Wyoming were analyzed for trace elements. Because of the high concentrations of trace elements in coal and bentonite clay, the possibility exists for these elements to dissolve or be suspended in the water. Strip mine ponds were not significantly higher in trace element concentrations compared to livestock ponds. All but one of the 41 ponds sampled contained elemental concentrations that would be detrimental for livestock use or aquatic life use. Cadmium and lead were the elements most frequently in excess of water quality crieria. Lead was found in the study ponds about 35 times the median concentration of North American rivers. Manganese concentrations were found to exceed iron in many ponds, which is unusual in natural waters. The potential for detrimental concentrations of trace elements in pond water must be evaluated when designing land use management plans for ponds intended to be used by livestock or aquatic life.     

Using Public Participation Geographic Information Systems to Identify Places of Watershed Service Provisioning

In this study, we used public participation geographic information systems methods to collect spatial data identifying places that stakeholders in Mobile Bay, Alabama think are important providers of watershed services. These methods allowed us to spatially analyze participatory data from general public respondents and directly compare them with other scientific data in a geographic information systems database. This study identified which places in the region participants believe are important providers of specific watershed services, including fish nurseries, storm protection, flood protection, and water quality protection, which would likely have public support for conservation. Additionally, we assessed the accuracy of participant watershed service identification using land cover data to identify inconsistencies and participant knowledge gaps. This information can be used to target outreach education efforts. We found that the accuracy with which participants correctly identified places with the necessary land cover to provide each service varied considerably. We believe this to be a useful tool for managers to elicit stakeholder input and to identify knowledge gaps regarding the provisioning of watershed services.     

Empirical Estimation of Stream Discharge Using Channel Geometry in Low‐Gradient,Sand‐Bed Streams of the Southeastern Plains

Manning's equation is used widely to predict stream discharge (Q) from hydraulic variables when logistics constrain empirical measurements of in‐bank flow events. Uncertainty in Manning's roughness (n_M) is the major source of error in natural channels, and sand‐bed streams pose difficulties because flow resistance is affected by flow‐dependent bed configuration. Our study was designed to develop and validate models for estimating Q from channel geometry easily derived from cross‐sectional surveys and available GIS data. A database was compiled consisting of 484 Q measurements from 75 sand‐bed streams in Alabama, Georgia, South Carolina, North Carolina (Southeastern Plains), and Florida (Southern Coastal Plain), with six New Zealand streams included to develop statistical models to predict Q from hydraulic variables. Model error characteristics were estimated with leave‐one‐site‐out jackknifing. Independent data of 317 Q measurements from 55 Southeastern Plains streams indicated the model (Q = A_cR_H^0.6906S^0.1216; where A_c is the channel area, R_H is the hydraulic radius, and S is the bed slope) best predicted Q, based on Akaike's information criterion and root mean square error. Models also were developed from smaller Q range subsets to explore if subsets increased predictive ability, but error fit statistics suggested that these were not reasonable alternatives to the above equation. Thus, we recommend the above equation for predicting in‐bank Q of unbraided, sandy streams of the Southeastern Plains.     

Maximum entropy modeling of species geographic distributions

The availability of detailed environmental data, together with inexpensive and powerful computers, has fueled a rapid increase in predictive modeling of species environmental requirements and geographic distributions. For some species, detailed presence/absence occurrence data are available, allowing the use of a variety of standard statistical techniques. However, absence data are not available for most species. In this paper, we introduce the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data. Maxent is a general-purpose machine learning method with a simple and precise mathematical formulation, and it has a number of aspects that make it well-suited for species distribution modeling. In order to investigate the efficacy of the method, here we perform a continental-scale case study using two Neotropical mammals: a lowland species of sloth, Bradypus variegatus, and a small montane murid rodent, Microryzomys minutus. We compared Maxent predictions with those of a commonly used presence-only modeling method, the Genetic Algorithm for Rule-Set Prediction (GARP). We made predictions on 10 random subsets of the occurrence records for both species, and then used the remaining localities for testing. Both algorithms provided reasonable estimates of the species’ range, far superior to the shaded outline maps available in field guides. All models were significantly better than random in both binomial tests of omission and receiver operating characteristic (ROC) analyses. The area under the ROC curve (AUC) was almost always higher for Maxent, indicating better discrimination of suitable versus unsuitable areas for the species. The Maxent modeling approach can be used in its present form for many applications with presence-only datasets, and merits further research and development.     

Cloud chemistry in the eastern United States, as sampled from three high-elevation sites along the Appalachian Mountains

Atmospheric deposition of acidic cloud water is thought to be one of the causes for the recent forest decline in industrialized areas of the world. The present paper presents results from the Mountain Acid Deposition Program (MADPro), a part of EPA's Clean Air Status and Trends Network, (CASTnet). We used automated cloud water collectors at three selected mountain sites (Whiteface Mt., NY; Whitetop Mt., VA; and Clingman's Dome, TN) to take hourly samples from non-precipitating clouds during temperate (non- freezing) seasons of each year from 1994 to 1997. Samples were promptly analyzed for pH, conductivity, and concentration of dissolved ions. Cloud liquid water content (LWC) and meteorological parameters were measured at each site. Mean cloud frequencies and LWC of clouds were higher at Whiteface Mt., NY, than in the Southern Appalachians. The four most prevalent ions found in cloud water samples were usually, in order of decreasing concentration: sulfate (SO²⁻₄) hydrogen (H⁺), ammonium (NH₄⁺), and nitrate (NO₃⁻). Within cloud events the concentration of these major ions tended to co-vary. Typically there was an inverse relationship between LWC of the cloud and ionic concentration of the cloud water. During the sampling season, the highest ionic concentrations were seen during mid-summer. Ionic concentrations of samples from the southern sites were significantly higher than samples from Whiteface Mt., but further analysis indicates that this is at least partially due to the north–south difference in the LWC of clouds. MADPro results are shown to be comparable with previous studies of cloud chemistry in North America.     

Trait groups as management entities in a complex,multispecies reef fishery

Localized stressors compound the ongoing climate-driven decline of coral reefs, requiring natural resource managers to work with rapidly shifting paradigms. Trait-based adaptive management (TBAM) is a new framework to help address changing conditions by choosing and implementing management actions specific to species groups that share key traits, vulnerabilities, and management responses. In TBAM maintenance of functioning ecosystems is balanced with provisioning for human subsistence and livelihoods. We first identified trait-based groups of food fish in a Pacific coral reef with hierarchical clustering. Positing that trait-based groups performing comparable functions respond similarly to both stressors and management actions, we ascertained biophysical and socioeconomic drivers of trait-group biomass and evaluated their vulnerabilities with generalized additive models. Clustering identified 7 trait groups from 131 species. Groups responded to different drivers and displayed divergent vulnerabilities; human activities emerged as important predictors of community structuring. Biomass of small, solitary reef-associated species increased with distance from key fishing ports, and large, solitary piscivores exhibited a decline in biomass with distance from a port. Group biomass also varied in response to different habitat types, the presence or absence of reported dynamite fishing activity, and exposure to wave energy. The differential vulnerabilities of trait groups revealed how the community structure of food fishes is driven by different aspects of resource use and habitat. This inherent variability in the responses of trait-based groups presents opportunities to apply selective TBAM strategies for complex, multispecies fisheries. This approach can be widely adjusted to suit local contexts and priorities.     

Does invasion by Pteridium aquilinum (Dennstaedtiaceae) affect the ecological succession in Atlantic Forest areas after a fire?

Environmental Science and Pollution Research - Pteridium aquilinum (Dennstaedtiaceae) colonization affects ecological and restoration processes. The knowledge of the impacts on the ecological...