The extent to which environmental context mediates the bioaccumulation of biotransported contaminants by stream‐resident organisms is poorly understood. For example, it is unclear the extent to which contaminant type, instream characteristics or resident fish identity interact to influence the uptake of contaminants deposited by Pacific salmon (Oncorhynchus spp.) during their spawning runs. To address this uncertainty, we sampled four stream‐resident fish species from 13 watersheds of the Laurentian Great Lakes in locations with and without salmon runs across a gradient of instream and watershed characteristics. We determined the polychlorinated biphenyl (PCB) and mercury (Hg) concentration along with the stable isotope ratios for salmon and stream‐resident fish. We found that stream‐resident fish PCB concentrations were higher in reaches with salmon and were positively related to δ15N. In contrast, resident fish Hg concentrations were similar or lower in reaches with salmon compared with reaches lacking salmon and either exhibited a negative or no relationship with δ15N. Based on AICc, resident fish exhibited species‐specific PCB concentrations that were positively related to salmon PCB flux. Hg burdens exhibited an interaction between fish length and salmon Hg flux—as salmon Hg inputs increased, Hg levels decreased with increasing resident fish length. Because salmon eggs are enriched in PCBs but depleted in Hg, contaminant loads of resident fish appear to be driven by consumption of salmon eggs. We found no support for models that included the mediating influence of instream or watershed factors. Synthesis and applications. Our results highlight that contaminants bioaccumulate differently depending on contaminant type, species identity and the trophic pathway to contamination. Consequently, consideration of the recipient food web and route of exposure is critical to understanding the fate of biotransported contaminants in ecosystems. The transfer of contaminants by migratory organisms represents an understudied stressor in ecology. Effective management of biotransported contaminants will require the delineation of hotspots of biotransport and implementation of best management practices to reduce inputs of salmon‐derived pollutants.

tThe availability of Very High Resolution (VHR) optical sensors and a growing image archive that is fre-quently updated, allows the use of change detection in post-disaster recovery and monitoring for robustand rapid results. The proposed semi-automated GIS object-based method uses readily available pre-disaster GIS data and adds existing knowledge into the processing to enhance change detection. It also allows targeting specific types of changes pertaining to similar man-made objects. This change detection method is based on pre/post normalized index, gradient of intensity, texture and edge similarity filters within the object and a set of training data. Once the change is quantified, based on training data, the method can be used automatically to detect change in order to observe recovery over time in large areas.Analysis over time can also contribute to obtaining a full picture of the recovery and development after disaster, thereby giving managers a better understanding of productive management practices

No abstract

The availability of Very High Resolution (VHR) optical sensors and a growing image archive that is frequently updated, allows the use of change detection in post-disaster recovery and monitoring for robust and rapid results. The proposed semi-automated GIS object-based method uses readily available pre-disaster GIS data and adds existing knowledge into the processing to enhance change detection. It also allows targeting specific types of changes pertaining to similar man-made objects. This change detection method is based on pre/post normalized index, gradient of intensity, texture and edge similarity filters within the object and a set of training data. Once the change is quantified, based on training data, the method can be used automatically to detect change in order to observe recovery over time in large areas. Analysis over time can also contribute to obtaining a full picture of the recovery and development after disaster, thereby giving managers a better understanding of productive management practices.

Abstract-Climate change is an issue of growing economic, social, and political concern. Continued rise in the average temperatures of the Earth could lead to drastic climate change or an increased frequency of extreme events, which would negatively affect agriculture, population, and global health. One way of studying the dynamics of the Earth's changing climate is by attempting to identify regions that exhibit similar climatic behavior in terms of long-term variability. Climate networks have emerged as a strong analytics framework for both descriptive analysis and predictive modeling of the emergent phenomena. Previously, the networks were constructed using only one measure of similarity, namely the (linear) Pearson cross correlation, and were then clustered using a community detection algorithm. However, nonlinear dependencies are known to exist in climate, which begs the question whether more complex correlation measures are able to capture any such relationships. In this paper, we present a systematic study of different univariate measures of similarity and compare how each affects both the network structure as well as the predictive power of the clusters.