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\"Coming of age in 1960s Paris, Bernard Appelbaum exists in the hazy shadow of the Holocaust and on the electric cusp of the French New Wave. We find the narrator of Wide awake as he wanders the city streets in search of signs of his father, who was deported by the Nazis in 1942. Bernard's chance encounter with a former acquaintance who has become filmmaker François Truffaut's assistant leads to a spot as an extra on the set of Jules and Jim--setting into motion a series of discoveries and lost memories that crack open a hidden past. On seeing Jules and Jim, Bernard's mother is moved to divulge the secrets of her own past as a Jewish-Polish immigrant to France, which curiously mirrors that of the film's heroine. When revelations about his mother's two loves lead Bernard on a fateful journey through Paris, to Germany, and then to Auschwitz itself, he must plumb haunting depths in order to recover his own identity.\"--Publisher's description.

Beaver have been referred to as ecosystem engineers because of the large impacts their dam building activities have on the landscape; however, the benefits they may provide to fluvial fish species has been debated. We conducted a watershed-scale experiment to test how increasing beaver dam and colony persistence in a highly degraded incised stream affects the freshwater production of steelhead ( Oncorhynchus mykiss ). Following the installation of beaver dam analogs (BDAs), we observed significant increases in the density, survival and production of juvenile steelhead without impacting upstream and downstream migrations. The steelhead response occurred as the quantity and complexity of their habitat increased. This study is the first large-scale experiment to quantify the benefits of beavers and BDAs to a fish population and its habitat. Beaver mediated restoration may be a viable and efficient strategy to recover ecosystem function of previously incised streams and to increase the production of imperiled fish populations.

Beaver are an integral component of hydrologic, geomorphic, and biotic processes within North American stream systems, and their propensity to build dams alters stream and riparian structure and function to the benefit of many aquatic and terrestrial species. Recognizing this, beaver relocation efforts and/or application of structures designed to mimic the function of beaver dams are increasingly being utilized as effective and cost-efficient stream and riparian restoration approaches. Despite these verities, the notion that beaver dams negatively impact stream habitat remains common, specifically the assumption that beaver dams increase stream temperatures during summer to the detriment of sensitive biota such as salmonids. In this study, we tracked beaver dam distributions and monitored water temperature throughout 34 km of stream for an eight-year period between 2007 and 2014. During this time the number of natural beaver dams within the study area increased by an order of magnitude, and an additional 4 km of stream were subject to a restoration manipulation that included installing a high-density of Beaver Dam Analog (BDA) structures designed to mimic the function of natural beaver dams. Our observations reveal several mechanisms by which beaver dam development may influence stream temperature regimes; including longitudinal buffering of diel summer temperature extrema at the reach scale due to increased surface water storage, and creation of cool-water channel scale temperature refugia through enhanced groundwater-surface water connectivity. Our results suggest that creation of natural and/or artificial beaver dams could be used to mitigate the impact of human induced thermal degradation that may threaten sensitive species.

Increasingly, research and management in natural resource science rely on very large datasets compiled from multiple sources. While it is generally good to have more data, utilizing large, complex datasets has introduced challenges in data sharing, especially for collaborating researchers in disparate locations (“distributed research teams”). We surveyed natural resource scientists about common data-sharing problems. The major issues identified by our survey respondents (n = 118) when providing data were lack of clarity in the data request (including format of data requested). When receiving data, survey respondents reported various insufficiencies in documentation describing the data (e.g., no data collection description/no protocol, data aggregated, or summarized without explanation). Since metadata, or “information about the data,” is a central obstacle in efficient data handling, we suggest documenting metadata through data dictionaries, protocols, read-me files, explicit null value documentation, and process metadata as essential to any large-scale research program. We advocate for all researchers, but especially those involved in distributed teams to alleviate these problems with the use of several readily available communication strategies including the use of organizational charts to define roles, data flow diagrams to outline procedures and timelines, and data update cycles to guide data-handling expectations. In particular, we argue that distributed research teams magnify data-sharing challenges making data management training even more crucial for natural resource scientists. If natural resource scientists fail to overcome communication and metadata documentation issues, then negative data-sharing experiences will likely continue to undermine the success of many large-scale collaborative projects.

Climate change will likely have profound effects on cold-water species of freshwater fishes. As temperatures rise, cold-water fish distributions may shift and contract in response. Predicting the effects of projected stream warming in stream networks is complicated by the generally poor correlation between water temperature and air temperature. Spatial dependencies in stream networks are complex because the geography of stream processes is governed by dimensions of flow direction and network structure. Therefore, forecasting climate-driven range shifts of stream biota has lagged behind similar terrestrial modeling efforts. We predicted climate-induced changes in summer thermal habitat for 3 cold-water fish species—juvenile Chinook salmon, rainbow trout, and bull trout (Oncorhynchus tshawytscha, O. mykiss, and Salvelinus confluentus, respectively)—in the John Day River basin, northwestern United States. We used a spatially explicit statistical model designed to predict water temperature in stream networks on the basis of flow and spatial connectivity. The spatial distribution of stream temperature extremes during summers from 1993 through 2009 was largely governed by solar radiation and interannual extremes of air temperature. For a moderate climate change scenario, estimated declines by 2100 in the volume of habitat for Chinook salmon, rainbow trout, and bull trout were 69-95%, 51-87%, and 86-100%, respectively. Although some restoration strategies may be able to offset these projected effects, such forecasts point to how and where restoration and management efforts might focus. Es probable que el cambio climático tenga profundo efectos sobre especies de peces dulceacuícolas de agua fría. A medida que incrementa la temperatura, la distribución de peces de agua fría puede cambiar y contraerse en respuesta. La predicción de efectos del calentamiento proyectado en redes de arroyos es complicada debido a la baja correlación entre la temperatura del agua y la temperatura del aire. Las dependencias espaciales en las redes de arroyos son complejas porque la geografía de los procesos en los arroyos esta determinada por las dimensiones en la dirección del flujo y por la estructura de la red. Por lo tanto, la predicción de cambios dirigidos por el clima en la biota de arroyos está rezagada en comparación con lo esfuerzos de modelado terrestre. Pronosticamos cambios inducidos por el clima en el hábitat térmico de 3 especies de peces de agua fría - Oncorhynchus tshawytscha, O. mykiss y Salvelinus confluentus - en la Cuenca del Río John Day, en el noroeste de Estados Unidos. Utilizamos un modelo estadístico espacialmente explícito diseñado para pronosticar la temperatura del agua en redes de arroyos con base en el flujo y la conectividad espacial. La distribución espacial de los extremos de temperatura en los arroyos durante los veranos de 1993 a 2009 estuvo determinada principalmente por la radiación solar y los extremos interanuales de la temperatura del aire. En un escenario de cambio climático moderado, estimamos que las declinaciones en 2100 en el volumen de hábitat de Oncorhynchus tshawytscha, O. mykiss y Salvelinus confluentus fueron de 39-95%, 51-87% y 86-100%, respectivamente. Aunque algunas estrategias de restauración pueden ser capaces de compensar estos efectos proyectados, tales predicciones apuntan hacia como y donde se pueden enfocar los esfuerzos de restauración y manejo.

Spatial and temporal patterns in stream temperature are primary factors determining species composition, diversity and productivity in stream ecosystems. The availability of spatially and temporally continuous estimates of stream temperature would improve the ability of biologists to fully explore the effects of stream temperature on biota. Most statistical stream temperature modeling techniques are limited in their ability to account for the influence of variables changing across spatial and temporal gradients. We identified and described important interactions between climate and spatial variables that approximate mechanistic controls on spatiotemporal patterns in stream temperature. With identified relationships we formed models to generate reach-scale basin-wide spatially and temporally continuous predictions of daily mean stream temperature in four Columbia River tributaries watersheds of the Pacific Northwest, USA. Models were validated with a testing dataset composed of completely distinct sites and measurements from different years. While some patterns in residuals remained, testing dataset predictions of selected models demonstrated high accuracy and precision (averaged RMSE for each watershed ranged from 0.85–1.54 °C) and was only 17% higher on average than training dataset prediction error. Aggregating daily predictions to monthly predictions of mean stream temperature reduced prediction error by an average of 23%. The accuracy of predictions was largely consistent across diverse climate years, demonstrating the ability of the models to capture the influences of interannual climatic variability and extend predictions to timeframes with limited temperature logger data. Results suggest that the inclusion of a range of interactions between spatial and climatic variables can approximate dynamic mechanistic controls on stream temperatures.

Invertebrate drift is a key process in riverine ecosystems controlling aquatic invertebrate distribution and availability to fish as prey. However, accurately quantifying drifting invertebrates of all sizes is difficult because the fine-mesh nets required to capture the smallest specimens clog easily, which reduces filtration efficiency and measurement accuracy. To address this problem, we developed a gas-powered pump system that delivers 20 m3/hour of river water through nested 80- and 750-μm-mesh nets suspended in the air. We compared 17 pumped samples with those obtained by adjacent, conventional deployment of a 250-μm drift net in a clear-water Alaskan river during both low and high flows. Our drift pump system sampled a geometric mean drift concentration of 467 invertebrates m−3 (maximum 5637 m−3) – eleven times the mean concentration of 42 m−3 estimated using the drift net. Invertebrates ≤ 3 mm long, primarily chironomids, comprised the entire difference between methods. Investigators for whom the drift of 0.5–3 mm invertebrates might be relevant (e.g., those applying foraging models for juvenile drift-feeding fishes) should consider using a pump or similar aerial filtration method to quantify small invertebrate drift, lest they underestimate it by an order of magnitude.

Although water temperature is important to stream biota, it is difficult to collect in a spatially and temporally continuous fashion. We used remotely-sensed Land Surface Temperature (LST) data to estimate mean daily stream temperature for every confluence-to-confluence reach in the John Day River, OR, USA for a ten year period. Models were built at three spatial scales: site-specific, subwatershed, and basin-wide. Model quality was assessed using jackknife and cross-validation. Model metrics for linear regressions of the predicted vs. observed data across all sites and years: site-specific r2 = 0.95, Root Mean Squared Error (RMSE) = 1.25 °C; subwatershed r2 = 0.88, RMSE = 2.02 °C; and basin-wide r2 = 0.87, RMSE = 2.12 °C. Similar analyses were conducted using 2012 eight-day composite LST and eight-day mean stream temperature in five watersheds in the interior Columbia River basin. Mean model metrics across all basins: r2 = 0.91, RMSE = 1.29 °C. Sensitivity analyses indicated accurate basin-wide models can be parameterized using data from as few as four temperature logger sites. This approach generates robust estimates of stream temperature through time for broad spatial regions for which there is only spatially and temporally patchy observational data, and may be useful for managers and researchers interested in stream biota.

In ecology, as in other research fields, efficient sampling for population estimation often drives sample designs toward unequal probability sampling, such as in stratified sampling. Design based statistical analysis tools are appropriate for seamless integration of sample design into the statistical analysis. However, it is also common and necessary, after a sampling design has been implemented, to use datasets to address questions that, in many cases, were not considered during the sampling design phase. Questions may arise requiring the use of model based statistical tools such as multiple regression, quantile regression, or regression tree analysis. However, such model based tools may require, for ensuring unbiased estimation, data from simple random samples, which can be problematic when analyzing data from unequal probability designs. Despite numerous method specific tools available to properly account for sampling design, too often in the analysis of ecological data, sample design is ignored and consequences are not properly considered. We demonstrate here that violation of this assumption can lead to biased parameter estimates in ecological research. In addition, to the set of tools available for researchers to properly account for sampling design in model based analysis, we introduce inverse probability bootstrapping (IPB). Inverse probability bootstrapping is an easily implemented method for obtaining equal probability re-samples from a probability sample, from which unbiased model based estimates can be made. We demonstrate the potential for bias in model-based analyses that ignore sample inclusion probabilities, and the effectiveness of IPB sampling in eliminating this bias, using both simulated and actual ecological data. For illustration, we considered three model based analysis tools--linear regression, quantile regression, and boosted regression tree analysis. In all models, using both simulated and actual ecological data, we found inferences to be biased, sometimes severely, when sample inclusion probabilities were ignored, while IPB sampling effectively produced unbiased parameter estimates.