Change point analysis of phosphorus trends in the Illinois River (Oklahoma) demonstrates the effects of watershed management

Detecting water quality improvements following watershed management changes is complicated by flow-dependent concentrations and nonlinear or threshold responses that are difficult to detect with traditional statistical techniques. In this study, we evaluated the long-term trends (1997-2009) in total P (TP) concentrations in the Illinois River of Oklahoma, and some of its major tributaries, using flow-adjusted TP concentrations and regression tree analysis to identify specific calendar dates in which change points in P trends may have occurred. Phosphorus concentrations at all locations were strongly correlated with stream flow. Flow-adjusted TP concentrations increased at all study locations in the late 1990s, but this trend was related to a change in monitoring practices where storm flow samples were specifically targeted after 1998. Flow-adjusted TP concentrations decreased in the two Illinois River sites after 2003. This change coincided with a significant decrease in effluent TP concentrations originating with one of the largest municipal wastewater treatment facilities in the basin. Conversely, flow-adjusted TP concentrations in one tributary increased, but this stream received treated effluent from a wastewater facility where effluent TP did not decrease significantly over the study period. Results of this study demonstrate how long-term trends in stream TP concentrations are difficult to quantify without consistent long-term monitoring strategies and how flow adjustment is likely mandatory for examining these trends. Furthermore, the study demonstrates how detecting changes in long-term water quality data sets requires statistical methods capable of identifying change point and nonlinear responses.     

Biophysical‐Regulatory Classification and Profiling of Streams Across Management Units and Ecoregions1

Caruso, Brian S. and Joshua Haynes, 2011. Biophysical‐Regulatory Classification and Profiling of Streams Across Management Units and Ecoregions. Journal of the American Water Resources Association (JAWRA) 00(0):1‐22. DOI: 10.1111/j.1752‐1688.2010.00522.x Abstract: Aquatic resources management in the United States (U.S.) under Clean Water Act Section 404 has become more complex after recent Supreme Court decisions and U.S. Army Corps of Engineers and Environmental Protection Agency (USEPA) guidance. Many intermittent/ephemeral and headwater streams may not be jurisdictional if they lack a significant nexus with navigable waters. Streams in semiarid USEPA Region 8 were classified based on hydrologic permanence and stream order using National Hydrography Dataset (NHD) Plus and GIS to provide information across broad spatial scales to aid with jurisdictional determinations (JDs). Four classes were developed for profiling across management units and ecoregions. Based on medium‐resolution NHDPlus data, intermittent streams comprise >¾, and first order streams constitute >½ of the total stream length in Region 8. Mountain states and ecoregions have the largest percentage of perennial first order streams, whereas the Dakotas, plains, and desert ecoregions have the greatest percentages of intermittent first order and intermittent higher order streams. In the Upper Colorado River Basin, >50% of reaches are intermittent first order, and 9% are perennial first order. NHDPlus data can significantly underestimate the length of headwater and intermittent streams, but can still be a valuable tool to help develop stream classes and for regional JD planning and analysis. Refinement of the stream classes using high resolution NHD data and other key catchment parameters can improve their utility for JDs.     

Species' traits predict phenological responses to climate change in butterflies

How do species' traits help identify which species will respond most strongly to future climate change? We examine the relationship between species' traits and phenology in a well-established model system for climate change, the U.K. Butterfly Monitoring Scheme (UKBMS). Most resident U.K. butterfly species have significantly advanced their dates of first appearance during the past 30 years. We show that species with narrower larval diet breadth and more advanced overwintering stages have experienced relatively greater advances in their date of first appearance. In addition, species with smaller range sizes have experienced greater phenological advancement. Our results demonstrate that species' traits can be important predictors of responses to climate change, and they suggest that further investigation of the mechanisms by which these traits influence phenology may aid in understanding species' responses to current and future climate change.     

Variation in plasma levels of insulin-like growth factor-I (IGF-I) in lingcod: relationships among season,size, and gonadal steroids

The physiological response of a fish to its environment is mediated through the endocrine axis controlling growth. Therefore, growth-regulating hormone levels can serve as ecologically relevant indicators of fish growth rate. We quantified variation in plasma insulin-like growth factor-I (IGF-I) to evaluate its potential as an indicator of growth in lingcod, an economically and ecologically important bottomfish species in the northeast Pacific. An information-theoretic model selection approach was used to test the hypothesis that variation in lingcod IGF-I is related to season, body size, and gonadal steroid concentration. Season and a length × season interaction were the most important predictors of plasma IGF-I among the variables we evaluated, suggesting that season and body size should be explicitly accounted for when interpreting endocrine patterns in wild fish populations. This is among the few studies that have measured and interpreted patterns of IGF-I in wild fish and the first to describe seasonal endocrine profiles in lingcod.     

Genetic structure of the southeastern United States loggerhead turtle nesting aggregation: evidence of additional structure within the peninsular Florida recovery unit

The southeastern United States supports one of two large loggerhead turtle (Caretta caretta) nesting aggregations worldwide and is therefore critical to global conservation and recovery efforts for the species. Previous studies have established the presence of four demographically distinct nesting populations (management units) corresponding to beaches from (1) North Carolina through northeastern Florida, (2) peninsular Florida, (3) the Dry Tortugas, and (4) northwest Florida. Temporal and geographic genetic structure of the nesting aggregation was examined utilizing partial mitochondrial control region haplotype frequencies from 834 samples collected over the 2002 through 2008 nesting seasons from 19 beaches as well as previously published haplotype data. Most rookeries did not exhibit interannual genetic variation. However, the interannual variation detected did significantly impact the interpretation of spatial genetic structure in northeastern Florida. Based on pairwise F _ST comparisons, exact tests of population differentiation, and analysis of molecular variance, the present study upholds the distinctiveness of the four currently recognized management units and further supports recognition of discrete central eastern, southern (southeastern and southwestern), and central western Florida management units. Further subdivision may be warranted, but more intensive genetic sampling is required. In addition, tools such as telemetry and mark-recapture are needed to complement genetic data and overcome limitations of genetic markers in resolving loggerhead turtle rookery connectivity in the southeastern USA.     

A distributed modelling system for simulation of monthly runoff and nitrogen sources, loads and sinks for ungauged catchments in Denmark

We validated an existing physically based 3D MIKE SHE groundwater resource model (DK-model) at 175 Danish gauging stations covering different catchment sizes in order to calculate monthly water runoff in the 50% ungauged part of Denmark. Model performance was in most cases good (61% of gauging stations had a Nash-Sutcliffe (NS) coefficient >0.60) but nevertheless showed a large seasonal and georegion specific bias. Therefore, bias correction factors had to be developed before applying the DK-model simulations of runoff in the ungauged areas. Simulated monthly runoff from ungauged areas and the measured monthly runoff from 178 gauging stations were distributed to 2663 smaller Hydrological Units (ca. 15 km(2)) and linked with a new empirical model for flow-weighted monthly total nitrogen (TN) concentrations (R(2) = 0.43; P < 0.0001) developed based on 20 years of observations (1990-2009) in 83 small catchments for calculation of monthly gross diffuse TN-loads from HU's. Nitrogen retention was calculated in streams, lakes and wetlands utilising both lake specific models and rate coefficients to calculate N retention in surface water bodies. The whole model complex was linked in the DK-QN concept for simulation of monthly TN losses from point sources and diffuse sources, TN retention and resulting loadings to Danish coastal waters. The DK-QN model was validated in 118 gauged catchments and the model simulations had for >25% of the observations of monthly discharge weighted TN concentrations a NS larger than 0.26. Catchment specific monthly TN-loadings were modelled with a higher performance as 50% of the catchments had a NS greater than 0.75. The model concept allows calculation of N retention in streams, lakes and wetlands and the average annual model calculated N retention amounted to 21% of the modelled gross riverine TN loadings. The average annual gross TN loading to surface freshwater in Denmark derived from diffuse sources amounted to 97 000 tonnes N (91% of gross TN loadings) which is 54% of the total estimated N-leaching from the root zone on the Danish land area (212 000 tonnes N) during the period 1990-2009.     

A multiple additive regression tree analysis of three exposure measures during Hurricane Katrina

This paper analyses structural and personal exposure to Hurricane Katrina. Structural exposure is measured by flood height and building damage; personal exposure is measured by the locations of 911 calls made during the response. Using these variables, this paper characterises the geography of exposure and also demonstrates the utility of a robust analytical approach in understanding health‐related challenges to disadvantaged populations during recovery. Analysis is conducted using a contemporary statistical approach, a multiple additive regression tree (MART), which displays considerable improvement over traditional regression analysis. By using MART, the percentage of improvement in R‐squares over standard multiple linear regression ranges from about 62 to more than 100 per cent. The most revealing finding is the modelled verification that African Americans experienced disproportionate exposure in both structural and personal contexts. Given the impact of exposure to health outcomes, this finding has implications for understanding the long‐term health challenges facing this population.     

Sampling state and process variables on coral reefs

Contemporary coral reefs are forced to survive through and recover from disturbances at a variety of spatial and temporal scales. Understanding disturbances in the context of ecological processes may lead to accurate predictive models of population trajectories. Most coral-reef studies and monitoring programs examine state variables, which include the percentage coverage of major benthic organisms, but few studies examine the key ecological processes that drive the state variables. Here we outline a sampling strategy that captures both state and process variables, at a spatial scale of tens of kilometers. Specifically, we are interested in (1) examining spatial and temporal patterns in coral population size-frequency distributions, (2) determining major population processes, including rates of recruitment and mortality, and (3) examining relationships between processes and state variables. Our effective sampling units are randomly selected 75 × 25 m stations, spaced approximately 250?C500 m apart, representing a 10³ m spatial scale. Stations are nested within sites, spaced approximately 2 km apart, representing a 10⁴ m spatial scale. Three randomly selected 16 m² quadrats placed in each station and marked for relocation are used to assess processes across time, while random belt-transects, re-randomized at each sampling event, are used to sample state variables. Both quadrats and belt-transects are effectively sub-samples from which we will derive estimates of means for each station at each sampling event. This nested sampling strategy allows us to determine critical stages in populations, examine population performance, and compare processes through disturbance events and across regions.