No abstract

In response to the threat of introductions of non-native forest insects, the Early Detection and Rapid Response (EDRR) program in Alaska monitors for arrivals of non-native insects, an effort that is limited by the time required to process samples using morphological methods. We compared conventional methods of processing EDRR traps with metabarcoding methods for processing the same samples. We deployed Lindgren funnel traps at three points of entry in Alaska using standard EDRR methods and trap samples were later processed using routine sorting and identification based on morphology. Samples were then processed using High Throughput Sequencing (HTS) metabarcoding methods. In three samples bycatch was included and in three samples non-native species were added. Morophological and HTS methods yielded generally similar results for scolytine and cerambycid beetle assemblages, but HTS provided more species-resolution identifications (46 species) than morphological methods (4 species plus the 3 non-native species known a priori). None of the non-native species were detected by HTS. Including bycatch did not appear to hinder identifications of scolytine and cerambycid beetles by HTS. From among the bycatch, two Palearctic species adventive to North America, Placusa incompleta Sjöberg, 1934 and Hydrophoria lancifer (Harris, 1780), are newly reported from Alaska. We do not recommend replacing our current morphological monitoring methods with HTS methods because we believe that we would be more likely to detect known non-native pest species using morphology. However, we would use HTS to increase our sample size without greatly increasing time required to process samples. We would also recommend HTS methods for surveillance monitoring where the set of target taxa is not limited to known pest species.

Human social interactions require understanding and predict- ing other people’s behavior. A growing body of work has found that these inferences are structured around an assump- tion that agents act rationally and efficiently in space. While powerful, this view treats action understanding in a vacuum, ignoring that much social inference happens in the context of familiar, hierarchically structured events (e.g.: buying gro- ceries, ordering in a restaurant). We propose that social and world knowledge is critical for efficiently interpreting behavior and test this idea through a simple block-building paradigm, where participants infer an agent’s sub-task (study 1a), next action (study 1b), and higher-level goal (study 1c), from very sparse observations. We compare these inferences against a Bayesian model of goal inference that exploits task structure to interpret agents’ actions. This model fit participant judg- ments with high quantitative accuracy, highlighting how world knowledge may help support social inferences in a rich and powerful way.

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Clouds are parameterized in climate models using quantities on the model grid‐scale to approximate the cloud cover and impact on radiation. Because of the complexity of processes involved with clouds, these parameterizations are one of the key challenges in climate modeling. Differences in parameterizations of clouds are among the main contributors to the spread in climate sensitivity across models. In this work, the clouds in three generations of an atmosphere model lineage are evaluated against satellite observations. Satellite simulators are used within the model to provide an appropriate comparison with individual satellite products. In some respects, especially the top‐of‐atmosphere cloud radiative effect, the models show generational improvements. The most recent generation, represented by two distinct branches of development, exhibits some regional regressions in the cloud representation; in particular the southern ocean shows a positive bias in cloud cover. The two branches of model development show how choices during model development, both structural and parametric, lead to different cloud climatologies. Several evaluation strategies are used to quantify the spatial errors in terms of the large‐scale circulation and the cloud structure. The Earth mover's distance is proposed as a useful error metric for the passive satellite data products that provide cloud‐top pressure‐optical depth histograms. The cloud errors identified here may contribute to the high climate sensitivity in the Community Earth System Model, version 2 and in the Energy Exascale Earth System Model, version 1.