Football Game Program 3 1965

Football Game Program 3 196

Interaction of riparian wetlands and average March runoff (mm) on RichTOL for N.E Highlands BRT model.

<p>Boosted regression tree partial dependency plot shows the response form of average taxa tolerance (y-axis  =  fitted function of RichTOL) based on the effect of the interaction of two individual explanatory variables along the response variable (all other variable responses removed). There is a relatively large interaction at high values of average March runoff when there are also high values of percent riparian wetland thus resulting in higher values of tolerant taxa (RichTOL) than would be expected. We believe that high values of riparian wetland are acting as a surrogate for high values of percent urban land use.</p

Interaction of manmade streams and mean elevation on RichTOL for Northern Piedmont BRT model.

<p>Boosted regression tree partial dependency plot shows the response form of average taxa tolerance (y-axis  =  fitted function of RichTOL) based on the effect of the interaction of two individual explanatory variables along the response variable (all other variable responses removed). There is a relatively strong interaction acting on RichTOL at low values of mean elevation and high values of percent manmade streams that cause high values of tolerant taxa to occur. This is a common pattern, higher urbanization occurring in the lower elevation valleys.</p

Coupled Downscaled Climate Models and Ecophysiological Metrics Forecast Habitat Compression for an Endangered Estuarine Fish

<div><p>Climate change is driving rapid changes in environmental conditions and affecting population and species’ persistence across spatial and temporal scales. Integrating climate change assessments into biological resource management, such as conserving endangered species, is a substantial challenge, partly due to a mismatch between global climate forecasts and local or regional conservation planning. Here, we demonstrate how outputs of global climate change models can be downscaled to the watershed scale, and then coupled with ecophysiological metrics to assess climate change effects on organisms of conservation concern. We employed models to estimate future water temperatures (2010–2099) under several climate change scenarios within the large heterogeneous San Francisco Estuary. We then assessed the warming effects on the endangered, endemic Delta Smelt, <i>Hypomesus transpacificus</i>, by integrating localized projected water temperatures with thermal sensitivity metrics (tolerance, spawning and maturation windows, and sublethal stress thresholds) across life stages. Lethal temperatures occurred under several scenarios, but sublethal effects resulting from chronic stressful temperatures were more common across the estuary (median >60 days above threshold for >50% locations by the end of the century). Behavioral avoidance of such stressful temperatures would make a large portion of the potential range of Delta Smelt unavailable during the summer and fall. Since Delta Smelt are not likely to migrate to other estuaries, these changes are likely to result in substantial habitat compression. Additionally, the Delta Smelt maturation window was shortened by 18–85 days, revealing cumulative effects of stressful summer and fall temperatures with early initiation of spring spawning that may negatively impact fitness. Our findings highlight the value of integrating sublethal thresholds, life history, and <i>in situ</i> thermal heterogeneity into global change impact assessments. As downscaled climate models are becoming widely available, we conclude that similar assessments at management-relevant scales will improve the scientific basis for resource management decisions.</p></div

Partial dependency plots for variables in BRT model for RichTOL for North Central Appalachian Region.

<p>Boosted regression tree partial dependency plots show the response form of average taxa tolerance (y-axis  =  fitted function of RichTOL) based on the effect of individual explanatory variables with the response of all other variables removed (development data set). Shown in order of model importance: (A) percent riparian forest, (B) riparian population density (#/km²), (C) percent riparian agriculture and (D) population density (#/km²). The relative contribution of each explanatory variable is reported in parentheses. Refer to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090944#pone-0090944-t001" target="_blank">Table 1</a> for variable definitions. Three of the four variables can be interpreted as disturbance variables, two directly assessing urban land use (population density) and the third, riparian forest, which measures the amount of disturbance in the riparian zone was the top variable modeled. However, this region had the shortest disturbance gradient and the lowest modeled R² (0.67), though still relatively strong.</p

Observed versus predicted plots for BRT models for development (left) and validation (right) data sets.

<p>The observed versus predicted plots are based on the boosted regression models developed for average taxa tolerance (RichTOL) for five models: Full Region and four individual ecoregions (NC Appalachian, Ridge and Valley, NE Highlands, and N. Piedmont). The Full Region and N. Piedmont region plot relatively tight to the 1∶1 line for both the development and validation models indicating a good predictive fit with only slight bias at high and low values of RichTOL. The other regions in general showed more scatter and the N.C. Appalachian region which had the lowest modeled R², had had the shortest disturbance gradient (narrow range of RichTOL values) compared to the other regions.</p

Comparison of model evaluation statistics for boosted regression tree models (BRT) for four macroinvertebrate metrics for development (develop) and validation (valid) data sets at two spatial scales (full region and four ecoregions), number of variables in final model in parentheses.

<p>Validation models run with the same variables as the final development model.*</p><p>*R²–adjusted R-squared, CV R²-Cross validation R²; EPTR–Total taxa richness of Ephemeroptera (mayflies), Plecoptera (stoneflies) and Trichoptera (caddisflies); RichTOL-Average tolerance of all taxa; INTOL_RICH-Richness of intolerant taxa; NonInsectR-Noninsect taxa richness.</p

Plots of median, maximum, and minimum for number of days each year with calculated average daily water temperature within the maturation window for juvenile Delta Smelt at selected sites during the indicated decades during the 2000s.

<p>Results from the warmest scenario (GFDL-A2) and coolest scenario (PCM-B1) considered are shown.</p

Partial dependency plots for variables in BRT model for RichTOL for Ridge and Valley Region.

<p>Boosted regression tree partial dependency plots show the response form of average taxa tolerance (y-axis  =  fitted function of RichTOL) based on the effect of individual explanatory variables with the response of all other variables removed (development data set). Shown in order of model importance: (A) percent manmade channels, (B) percent riparian forests, (C) maximum November runoff (mm) and (D) population density (#/km²), model R² = 0.81. The relative contribution of each explanatory variable is reported in parentheses. Refer to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090944#pone-0090944-t001" target="_blank">Table 1</a> for variable definitions. Three of the four variables measure the effects of disturbance, two measure the response to urban land use and the other disturbance in the riparian zone due to either agriculture or urbanization. The fourth variable shows the response due to maximum November runoff.</p

Plot of calculated water temperatures for March 2090 through March 2091.

<p>The black bars show the range of days used to calculate the metrics described in the text.</p