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Understanding timing and distribution of virus spread is critical to global commercial and wildlife biosecurity management. A highly pathogenic avian influenza virus (HPAIv) global panzootic, affecting ~600 bird and mammal species globally and over 83 million birds across North America (December 2023), poses a serious global threat to animals and public health. We combined a large, long‐term waterfowl GPS tracking dataset (16 species) with on‐ground disease surveillance data (county‐level HPAIv detections) to create a novel empirical model that evaluated spatiotemporal exposure and predicted future spread and potential arrival of HPAIv via GPS tracked migratory waterfowl through 2022. Our model was effective for wild waterfowl, but predictions lagged HPAIv detections in poultry facilities and among some highly impacted nonmigratory species. Our results offer critical advance warning for applied biosecurity management and planning and demonstrate the importance and utility of extensive multispecies tracking to highlight potential high‐risk disease spread locations and more effectively manage outbreaks.

Identifying migration routes and fall stopover sites of Cinnamon Teal (Spatula cyanoptera septentrionalium) can provide a spatial guide to management and conservation efforts, and address vulnerabilities in wetland networks that support migratory waterbirds. Using high spatiotemporal resolution GPS‐GSM transmitters, we analyzed 61 fall migration tracks across western North America during our three‐year study (2017–2019). We marked Cinnamon Teal primarily during spring/summer in important breeding and molting regions across seven states (California, Oregon, Washington, Idaho, Utah, Colorado, and Nevada). We assessed fall migration routes and timing, detected 186 fall stopover sites, and identified specific North American ecoregions where sites were located. We classified underlying land cover for each stopover site and measured habitat selection for 12 land cover types within each ecoregion. Cinnamon Teal selected a variety of flooded habitats including natural, riparian, tidal, and managed wetlands; wet agriculture (including irrigation ditches, flooded fields, and stock ponds); wastewater sites; and golf and urban ponds. Wet agriculture was the most used habitat type (29.8% of stopover locations), and over 72% of stopover locations were on private land. Relatively scarce habitats such as wastewater ponds, tidal marsh, and golf and urban ponds were highly selected in specific ecoregions. In contrast, dry non‐habitat across all ecoregions, and dry agriculture in the Cold Deserts and Mediterranean California ecoregions, was consistently avoided. Resources used by Cinnamon Teal often reflected wetland availability across the west and emphasize their adaptability to dynamic resource conditions in arid landscapes. Our results provide much needed information on spatial and temporal resource use by Cinnamon Teal during migration and indicate important wetland habitats for migrating waterfowl in the western United States. We analyzed 61 fall migration tracks of fall migrating Cinnamon Teal in western North America. Furthermore, we investigated habitat selection patterns at fall stopover sites. Our results can be used to further inform waterfowl conservation and management efforts in the United States and Mexico.

Identifying the migration routes and stopover sites of fall migrating cinnamon teal (Spatula cyanoptera septentrionalium) can provide a spatial guide to conservation efforts for this understudied species. Most information known about the distribution of cinnamon teal in North America has been derived from spatially and temporally limited band returns and visual surveys. With recent advancements in GPS tracking technology (smaller, lighter transmitters with longer battery life), we now have the capability to study the movements and habitat use of this species at high spatial resolution and in near real-time. Using GPS/GSM transmitters, we tracked the fall migration of 61 cinnamon teal across western North America over the course of three years (2017-2019). Birds were marked primarily during the spring and summer in seven different states (California, Oregon, Washington, Idaho, Utah, Colorado, and Nevada). Marking locations were selected to coincide with existing banding efforts at sites that represented important breeding and molting regions. We identified 261 fall stopover sites and classified the underlying habitat within a 5km radius for each site. Stopover sites were grouped by level 1 ecoregions and habitat selection was measured within each group. We highlighted differences in wetland habitat composition across ecoregions in the fall and found varying selection preferences between regions. Our results can direct conservation planning looking to align management efforts with migrating waterfowl needs by providing a spatial and temporal map of resource requirements during this vulnerable life stage.

Background Identifying animal behaviors, life history states, and movement patterns is a prerequisite for many animal behavior analyses and effective management of wildlife and habitats. Most approaches classify short-term movement patterns with high frequency location or accelerometry data. However, patterns reflecting life history across longer time scales can have greater relevance to species biology or management needs, especially when available in near real-time. Given limitations in collecting and using such data to accurately classify complex behaviors in the long-term, we used hourly GPS data from 5 waterfowl species to produce daily activity classifications with machine-learned models using “automated modelling pipelines”. Methods Automated pipelines are computer-generated code that complete many tasks including feature engineering, multi-framework model development, training, validation, and hyperparameter tuning to produce daily classifications from eight activity patterns reflecting waterfowl life history or movement states. We developed several input features for modeling grouped into three broad categories, hereafter “feature sets”: GPS locations, habitat information, and movement history. Each feature set used different data sources or data collected across different time intervals to develop the “features” (independent variables) used in models. Results Automated modelling pipelines rapidly developed easily reproducible data preprocessing and analysis steps, identification and optimization of the best performing model and provided outputs for interpreting feature importance. Unequal expression of life history states caused unbalanced classes, so we evaluated feature set importance using a weighted F1-score to balance model recall and precision among individual classes. Although the best model using the least restrictive feature set (only 24 hourly relocations in a day) produced effective classifications (weighted F1 = 0.887), models using all feature sets performed substantially better (weighted F1 = 0.95), particularly for rarer but demographically more impactful life history states (i.e., nesting). Conclusions Automated pipelines generated models producing highly accurate classifications of complex daily activity patterns using relatively low frequency GPS and incorporating more classes than previous GPS studies. Near real-time classification is possible which is ideal for time-sensitive needs such as identifying reproduction. Including habitat and longer sequences of spatial information produced more accurate classifications but incurred slight delays in processing.