Enhanced Migratory Waterfowl Distribution Modeling by Inclusion of Depth to Water Table Data

In addition to being used as a tool for ecological understanding, management and conservation of migratory waterfowl rely heavily on distribution models; yet these models have poor accuracy when compared to models of other bird groups. The goal of this study is to offer methods to enhance our ability to accurately model the spatial distributions of six migratory waterfowl species. This goal is accomplished by creating models based on species-specific annual cycles and introducing a depth to water table (DWT) data set. The DWT data set, a wetland proxy, is a simulated long-term measure of the point either at or below the surface where climate and geological/topographic water fluxes balance. For species occurrences, the USGS' banding bird data for six relatively common species was used. Distribution models are constructed using Random Forest and MaxEnt. Random Forest classification of habitat and non-habitat provided a measure of DWT variable importance, which indicated that DWT is as important, and often more important, to model accuracy as temperature, precipitation, elevation, and an alternative wetland measure. MaxEnt models that included DWT in addition to traditional predictor variables had a considerable increase in classification accuracy. Also, MaxEnt models created with DWT often had higher accuracy when compared with models created with an alternative measure of wetland habitat. By comparing maps of predicted probability of occurrence and response curves, it is possible to explore how different species respond to water table depth and how a species responds in different seasons. The results of this analysis also illustrate that, as expected, all waterfowl species are tightly affiliated with shallow water table habitat. However, this study illustrates that the intensity of affiliation is not constant between seasons for a species, nor is it consistent between species

Associations between chlorophyll a and various microcystin health advisory concentrations [version 2; referees: 1 approved, 2 approved with reservations]

Cyanobacteria harmful algal blooms (cHABs) are associated with a wide range of adverse health effects that stem mostly from the presence of cyanotoxins. To help protect against these impacts, several health advisory levels have been set for some toxins. In particular, one of the more common toxins, microcystin, has several advisory levels set for drinking water and recreational use. However, compared to other water quality measures, field measurements of microcystin are not commonly available due to cost and advanced understanding required to interpret results. Addressing these issues will take time and resources. Thus, there is utility in finding indicators of microcystin that are already widely available, can be estimated quickly and in situ, and used as a first defense against high levels of microcystin. Chlorophyll a is commonly measured, can be estimated in situ, and has been shown to be positively associated with microcystin. In this paper, we use this association to provide estimates of chlorophyll a concentrations that are indicative of a higher probability of exceeding select health advisory concentrations for microcystin. Using the 2007 National Lakes Assessment and a conditional probability approach, we identify chlorophyll a concentrations that are more likely than not to be associated with an exceedance of a microcystin health advisory level. We look at the recent US EPA health advisories for drinking water as well as the World Health Organization levels for drinking water and recreational use and identify a range of chlorophyll a thresholds. A 50% chance of exceeding one of the specific advisory microcystin concentrations of 0.3, 1, 1.6, and 2 μg/L is associated with chlorophyll a concentration thresholds of 23, 68, 84, and 104 μg/L, respectively. When managing for these various microcystin levels, exceeding these reported chlorophyll a concentrations should be a trigger for further testing and possible management action

Associations between chlorophyll a and various microcystin-LR health advisory concentrations [version 1; referees: 1 approved, 2 approved with reservations]

Cyanobacteria harmful algal blooms (cHABs) are associated with a wide range of adverse health effects that stem mostly from the presence of cyanotoxins. To help protect against these impacts, several health advisory levels have been set for some toxins. In particular, one of the more common toxins, microcystin-LR, has several advisory levels set for drinking water and recreational use. However, compared to other water quality measures, field measurements of microcystin-LR are not commonly available due to cost and advanced understanding required to interpret results. Addressing these issues will take time and resources. Thus, there is utility in finding indicators of microcystin-LR that are already widely available, can be estimated quickly and in situ, and used as a first defense against high concentrations of microcystin-LR. Chlorophyll a is commonly measured, can be estimated in situ, and has been shown to be positively associated with microcystin-LR. In this paper, we use this association to provide estimates of chlorophyll a concentrations that are indicative of a higher probability of exceeding select health advisory concentrations for microcystin-LR. Using the 2007 National Lakes Assessment and a conditional probability approach, we identify chlorophyll a concentrations that are more likely than not to be associated with an exceedance of a microcystin-LR health advisory level. We look at the recent US EPA health advisories for drinking water as well as the World Health Organization levels for drinking water and recreational use and identify a range of chlorophyll a thresholds. A 50% chance of exceeding one of the microcystin-LR advisory concentrations of 0.3, 1, 1.6, and 2 g/L is associated with chlorophyll a concentration thresholds of 23.4, 67.0, 83.5, and 105.8, respectively. When managing for these various microcystin-LR levels, exceeding these reported chlorophyll a concentrations should be a trigger for further testing and possible management action

Resident Perceptions of Natural Resources between Cities and across Scales in the Pacific Northwest

As the global population becomes increasingly urban, research is needed to explore how local culture, land use, and policy will influence urban natural resource management. We used a broad-scale comparative approach and survey of residents within the Portland (Oregon)-Vancouver (Washington) metropolitan areas, USA, two states with similar geographical and ecological characteristics, but different approaches to land-use planning, to explore resident perceptions about natural resources at three scales of analysis: property level (“at or near my house”), neighborhood (“within a 20-minute walk from my house”), and metro level (“across the metro area”). At the metro-level scale, nonmetric multidimensional scaling revealed that the two cities were quite similar. However, affinity for particular landscape characteristics existed within each city with the greatest difference generally at the property-level scale. Portland respondents expressed affinity for large mature trees, tree-lined streets, public transportation, and proximity to stores and services. Vancouver respondents expressed affinity for plentiful accessible parking. We suggest three explanations that likely are not mutually exclusive. First, respondents are segmented based on preferences for particular amenities, such as convenience versus commuter needs. Second, historical land-use and tax policy legacies may influence individual decisions. Third, more environmentally attuned worldviews may influence an individual’s desire to produce environmentally friendly outcomes. Our findings highlight the importance of acknowledging variations in residents’ affinities for landscape characteristics across different scales and locations because these differences may influence future land-use policies about urban natural resources

Developing a wintering waterfowl community baseline for environmental monitoring of Narragansett Bay, Rhode Island [version 3; referees: 1 approved, 2 approved with reservations]

In 2004, the Atlantic Ecology Division of the US Environmental Protection Agency’s Office of Research and Development began an annual winter waterfowl survey of Rhode Island’s Narragansett Bay. Herein, we explore the survey data gathered from 2004 to 2011 in order to establish a benchmark understanding of our waterfowl communities and to establish a statistical framework for future environmental monitoring. The abundance and diversity of wintering waterfowl were relatively stable during the initial years of this survey, except in 2010 when there was a large spike in abundance and a reciprocal fall in diversity. There was no significant change in ranked abundance of most waterfowl species, with only Bufflehead (Bucephala albeola) and Hooded Merganser (Lophodytes cucllatus) showing a slight yet significant upward trend during the course of our survey period. Nonmetric multidimensional scaling (NMDS) was used to examine the community structure of wintering waterfowl. The results of the NMDS indicate that there is a spatial structure to the waterfowl communities of Narragansett Bay and this structure has remained relatively stable since the survey began. Our NMDS analysis helps to solidify what is known anecdotally about the bay’s waterfowl ecology, and provides a formalized benchmark for long-term monitoring of Narragansett Bay’s waterfowl communities. Birds, including waterfowl, are preferred bioindicators and we propose using our multivariate approach to monitor the future health of the bay. While this research focuses on a specific area of New England, these methods can be easily applied to novel areas of concern and provide a straightforward nonparametric approach to community-level monitoring. The methods provide a statistic test to examine potential drivers of community turnover and well-suited visualization tools

Maps of predicted probability of occurrence for all study species' winter habitat.

<p>Predictions were created using MaxEnt with 100% of known presence locations to increase accuracy of the visual representation. Temperature, precipitation, elevation, and water table depth were the predicted variables used to construct the probability surfaces.</p

Barplot of annual cycle timing for study species.

<p>Barplot of annual cycle timing for study species.</p

Plot of relationship between water table depth (m) and occurrence probability for species in winter.

<p>The plots were constructed by selecting 1,000 random points from the predicted probability of occurrence surface. The red curve is a smoothing spline fit to the mean of the data points, and is meant only to visually illustrate the trend of the data and the upper threshold of DWT.</p

A Random Forest approach to predict the spatial distribution of sediment pollution in an estuarine system.

Modeling the magnitude and distribution of sediment-bound pollutants in estuaries is often limited by incomplete knowledge of the site and inadequate sample density. To address these modeling limitations, a decision-support tool framework was conceived that predicts sediment contamination from the sub-estuary to broader estuary extent. For this study, a Random Forest (RF) model was implemented to predict the distribution of a model contaminant, triclosan (5-chloro-2-(2,4-dichlorophenoxy)phenol) (TCS), in Narragansett Bay, Rhode Island, USA. TCS is an unregulated contaminant used in many personal care products. The RF explanatory variables were associated with TCS transport and fate (proxies) and direct and indirect environmental entry. The continuous RF TCS concentration predictions were discretized into three levels of contamination (low, medium, and high) for three different quantile thresholds. The RF model explained 63% of the variance with a minimum number of variables. Total organic carbon (TOC) (transport and fate proxy) was a strong predictor of TCS contamination causing a mean squared error increase of 59% when compared to permutations of randomized values of TOC. Additionally, combined sewer overflow discharge (environmental entry) and sand (transport and fate proxy) were strong predictors. The discretization models identified a TCS area of greatest concern in the northern reach of Narragansett Bay (Providence River sub-estuary), which was validated with independent test samples. This decision-support tool performed well at the sub-estuary extent and provided the means to identify areas of concern and prioritize bay-wide sampling

Season specific MaxEnt AUC scores for each study species.

<p>The “base” variables are temperature, precipitation, and elevation. Models were constructed using the two different measures of wetland: average water table depth (DWT) from dynamically-driven hydrology model and percent wetland (PW) based on land cover classification. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030142#pone-0030142-g003" target="_blank">Figure 3</a> for species abbreviations.</p