Evaluating The Roles Of Visual Openness And Edge Effects On Nest-Site Selection And Reproductive Success In Grassland Birds

In some species, habitat edges (ecotones) affect nest-site selection and nesting success. Openness, or how visually open a habitat is, has recently been shown to influence grassland bird density and may affect nest-site selection, possibly by reducing the risk of predation on adults, nests, or both. Because edge and openness are correlated, it is possible that effects of openness have been overlooked or inappropriately ascribed to edge effects. We tested the roles of edges and visual openness in nest-site selection and nesting success of two grassland passerines, the Bobolink (Dolichonyx oryzivorus) and Savannah Sparrow (Passerculus sandwichensis), in the Champlain Valley, Vermont. We also evaluated the sensitivity of our results to alternative definitions of edge on our landscape. Bobolink (n = 580) and Savannah Sparrow nests (n = 922) were located on seven hay fields and three pastures from 2002 to 2010. Both species avoided placing nests near edges and in less open habitat compared with expectations based on random placement. When the effects of openness and edge were separated, less open habitats were still avoided, but edge responses were less clear. These results were robust to different definitions of habitat edge. We found no strong relationships between either openness or edges and reproductive success (numbers of eggs and fledglings, percentage of eggs producing fledglings, and nest success), although there may be an edge-specific openness effect on timing of reproduction (clutch completion date). Our results support openness as an important factor in nest-site selection by grassland birds

A proposed framework for the development and qualitative evaluation of West Nile virus models and their application to local public health decision-making

West Nile virus(WNV) is a globally distributed mosquito-borne virus of great public health concern. The number of WNV human cases and mosquito infection patterns vary in space and time. Many statistical models have been developed to understand and predict WNV geographic and temporal dynamics. However, these modeling efforts have been disjointed with little model comparison and inconsistent validation. In this paper, we describe a framework to unify and standardize WNV modeling efforts nationwide. WNV risk, detection, or warning models for this review were solicited from active research groups working in different regions of the United States. A total of 13 models were selected and described. The spatial and temporal scales of each model were compared to guide the timing and the locations for mosquito and virus surveillance, to support mosquito vector control decisions, and to assist in conducting public health outreach campaigns at multiple scales of decision-making. Our overarching goal is to bridge the existing gap between model development, which is usually conducted as an academic exercise, and practical model applications, which occur at state, tribal, local, or territorial public health and mosquito control agency levels. The proposed model assessment and comparison framework helps clarify the value of individual models for decision-making and identifies the appropriate temporal and spatial scope of each model. This qualitative evaluation clearly identifies gaps in linking models to applied decisions and sets the stage for a quantitative comparison of models. Specifically, whereas many coarse-grained models (county resolution or greater) have been developed, the greatest need is for fine-grained, short-term planning models (m–km, days–weeks) that remain scarce. We further recommend quantifying the value of information for each decision to identify decisions that would benefit most from model input

Patterns of West Nile Virus in the Northeastern United States Using Negative Binomial and Mechanistic Trait‐Based Models

Abstract West Nile virus (WNV) primarily infects birds and mosquitoes but has also caused over 2,000 human deaths, and >50,000 reported human cases in the United States. Expected numbers of WNV neuroinvasive cases for the present were described for the Northeastern United States, using a negative binomial model. Changes in temperature‐based suitability for WNV due to climate change were examined for the next decade using a temperature‐trait model. WNV suitability was generally expected to increase over the next decade due to changes in temperature, but the changes in suitability were generally small. Many, but not all, populous counties in the northeast are already near peak suitability. Several years in a row of low case numbers is consistent with a negative binomial, and should not be interpreted as a change in disease dynamics. Public health budgets need to be prepared for the expected infrequent years with higher‐than‐average cases. Low‐population counties that have not yet had a case are expected to have similar probabilities of having a new case as nearby low‐population counties with cases, as these absences are consistent with a single statistical distribution and random chance

Better null models for assessing predictive accuracy of disease models.

Null models provide a critical baseline for the evaluation of predictive disease models. Many studies consider only the grand mean null model (i.e. R2) when evaluating the predictive ability of a model, which is insufficient to convey the predictive power of a model. We evaluated ten null models for human cases of West Nile virus (WNV), a zoonotic mosquito-borne disease introduced to the United States in 1999. The Negative Binomial, Historical (i.e. using previous cases to predict future cases) and Always Absent null models were the strongest overall, and the majority of null models significantly outperformed the grand mean. The length of the training timeseries increased the performance of most null models in US counties where WNV cases were frequent, but improvements were similar for most null models, so relative scores remained unchanged. We argue that a combination of null models is needed to evaluate the forecasting performance of predictive models for infectious diseases and the grand mean is the lowest bar

Modeling Anthropogenic Noise Impacts on Animals in Natural Areas

Noise is a globally pervasive pollutant that can be detrimental to a range of animal species, with cascading effects on ecosystem functioning. As a result, concern about the impacts and expanding footprint of anthropogenic noise is increasing along with interest in approaches for how to mitigate its negative effects. A variety of modeling tools have been developed to quantify the spatial distribution and intensity of noise across landscapes, but these tools are under-utilized in landscape planning and noise mitigation. Here, we apply the Sound Mapping Tools toolbox to evaluate mitigation approaches to reduce the anthropogenic noise footprint of gas development, summer all-terrain vehicle recreation, and winter snowmobile use. Sound Mapping Tools uses models of the physics of noise propagation to convert measured source levels to landscape predictions of relevant sound levels. We found that relatively minor changes to the location of noise-producing activities could dramatically reduce the extent and intensity of noise in focal areas, indicating that site planning can be a cost-effective approach to noise mitigation. In addition, our snowmobile results, which focus on a specific frequency band important to the focal species, are consistent with previous research demonstrating that source noise level reductions are an effective means to reduce noise footprints. We recommend the use of quantitative, spatially-explicit maps of expected noise levels that include alternative options for noise source placement. These maps can be used to guide management decisions, allow for species-specific insights, and to reduce noise impacts on animals and ecosystems

Seasonal temperatures and hydrological conditions improve the prediction of West Nile virus infection rates in Culex mosquitoes and human case counts in New York and Connecticut.

West Nile virus (WNV; Flaviviridae: Flavivirus) is a widely distributed arthropod-borne virus that has negatively affected human health and animal populations. WNV infection rates of mosquitoes and human cases have been shown to be correlated with climate. However, previous studies have been conducted at a variety of spatial and temporal scales, and the scale-dependence of these relationships has been understudied. We tested the hypothesis that climate variables are important to understand these relationships at all spatial scales. We analyzed the influence of climate on WNV infection rate of mosquitoes and number of human cases in New York and Connecticut using Random Forests, a machine learning technique. During model development, 66 climate-related variables based on temperature, precipitation and soil moisture were tested for predictive skill. We also included 20-21 non-climatic variables to account for known environmental effects (e.g., land cover and human population), surveillance related information (e.g., relative mosquito abundance), and to assess the potential explanatory power of other relevant factors (e.g., presence of wastewater treatment plants). Random forest models were used to identify the most important climate variables for explaining spatial-temporal variation in mosquito infection rates (abbreviated as MLE). The results of the cross-validation support our hypothesis that climate variables improve the predictive skill for MLE at county- and trap-scales and for human cases at the county-scale. Of the climate-related variables selected, mean minimum temperature from July-September was selected in all analyses, and soil moisture was selected for the mosquito county-scale analysis. Models demonstrated predictive skill, but still over- and under-estimated WNV MLE and numbers of human cases. Models at fine spatial scales had lower absolute errors but had greater errors relative to the mean infection rates

A proposed framework for the development and qualitative evaluation of West Nile virus models and their application to local public health decision-making.

West Nile virus (WNV) is a globally distributed mosquito-borne virus of great public health concern. The number of WNV human cases and mosquito infection patterns vary in space and time. Many statistical models have been developed to understand and predict WNV geographic and temporal dynamics. However, these modeling efforts have been disjointed with little model comparison and inconsistent validation. In this paper, we describe a framework to unify and standardize WNV modeling efforts nationwide. WNV risk, detection, or warning models for this review were solicited from active research groups working in different regions of the United States. A total of 13 models were selected and described. The spatial and temporal scales of each model were compared to guide the timing and the locations for mosquito and virus surveillance, to support mosquito vector control decisions, and to assist in conducting public health outreach campaigns at multiple scales of decision-making. Our overarching goal is to bridge the existing gap between model development, which is usually conducted as an academic exercise, and practical model applications, which occur at state, tribal, local, or territorial public health and mosquito control agency levels. The proposed model assessment and comparison framework helps clarify the value of individual models for decision-making and identifies the appropriate temporal and spatial scope of each model. This qualitative evaluation clearly identifies gaps in linking models to applied decisions and sets the stage for a quantitative comparison of models. Specifically, whereas many coarse-grained models (county resolution or greater) have been developed, the greatest need is for fine-grained, short-term planning models (m-km, days-weeks) that remain scarce. We further recommend quantifying the value of information for each decision to identify decisions that would benefit most from model input

Evaluation of an open forecasting challenge to assess skill of West Nile virus neuroinvasive disease prediction

Abstract Background West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. Methods We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. Results Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. Conclusions Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases). Graphical Abstrac