AIMS for wildlife: Developing an automated interactive monitoring system to integrate real-time movement and environmental data for true adaptive management.

To effectively manage species and habitats at multiple scales, population and land managers require rapid information on wildlife use of managed areas and responses to landscape conditions and management actions. GPS tracking studies of wildlife are particularly informative to species ecology, habitat use, and conservation. Combining GPS data with administrative data and a diverse suite of remotely sensed, geo-referenced environmental (e.g., climatic) data, would more comprehensively inform how animals interact with and utilize habitats and ecosystems and our goal was to create a conceptual model for a system that would accomplish this - the 'Automated Interactive Monitoring System (AIMS) for Wildlife'. Our objective for this study was to develop a Customized Wildlife Report (CWR) - the first AIMS for Wildlife deliverable product. CWRs collate and summarize our 8-year GPS tracking dataset of ∼11 million locations from 1338 individual (16 species) avifauna and make actionable, real-time data on animal movements and trends in a specific area of interest available to managers and stakeholders for rapid application in day-to-day management. The CWR exemplar presented in this paper was developed to address needs identified by habitat managers of Sacramento National Wildlife Refuge and illustrates the highly specific, information offered and how it contributes to assessing the efficacy of conservation actions while allowing for near real-time adaptive management. The report can be easily customized for any of the thousands of wildlife refuges or regional areas of interest in the United States, emphasizing the broad application of an animal movement data stream. Utilizing diverse, extensive telemetry data streams through scientific collaboration can aid managers and conservation stakeholders with short and long-term research and conservation planning and help address a cadre of issues from local-scale habitat management to improving the understanding of landscape level impacts like drought, wildfire, and climate change on wildlife populations.

Knowledge coproduction on the impact of decisions for waterbird habitat in a changing climate.

Scientists, resource managers, and decision makers increasingly use knowledge coproduction to guide the stewardship of future landscapes under climate change. This process was applied in the California Central Valley (USA) to solve complex conservation problems, where managed wetlands and croplands are flooded between fall and spring to support some of the largest concentrations of shorebirds and waterfowl in the world. We coproduced scenario narratives, spatially explicit flooded waterbird habitat models, data products, and new knowledge about climate adaptation potential. We documented our coproduction process, and using the coproduced models, we determined when and where management actions make a difference and when climate overrides these actions. The outcomes of this process provide lessons learned on how to cocreate usable information and how to increase climate adaptive capacity in a highly managed landscape. Actions to restore wetlands and prioritize their water supply created habitat outcomes resilient to climate change impacts particularly in March, when habitat was most limited; land protection combined with management can increase the ecosystem's resilience to climate change; and uptake and use of this information was influenced by the roles of different stakeholders, rapidly changing water policies, discrepancies in decision-making time frames, and immediate crises of extreme drought. Although a broad stakeholder group contributed knowledge to scenario narratives and model development, to coproduce usable information, data products were tailored to a small set of decision contexts, leading to fewer stakeholder participants over time. A boundary organization convened stakeholders across a large landscape, and early adopters helped build legitimacy. Yet, broadscale use of climate adaptation knowledge depends on state and local policies, engagement with decision makers that have legislative and budgetary authority, and the capacity to fit data products to specific decision needs.

Coproducción de información sobre el impacto de las decisiones para el hábitat de las aves acuáticas en un clima cambiante Resumen Hay un incremento del uso que dan los científicos, gestores de recursos y los órganos decisorios a la coproducción de información para guiar la administración de los futuros paisajes bajo el cambio climático. Se aplicó este proceso para resolver problemas complejos de conservación en el Valle Central de California (EE. UU.), en donde los humedales y campos de cultivos manejados se inundan entre el otoño y la primavera para mantener una de las mayores concentraciones de aves playeras y acuáticas del mundo. Coproducimos narrativas de escenarios, modelos espacialmente explícitos de hábitats inundados de las aves acuáticas, productos de datos y conocimiento nuevo sobre el potencial de adaptación climática. Documentamos nuestro proceso de coproducción y usamos los modelos resultantes para determinar cuándo y en dónde marcan una diferencia las acciones de manejo y cuándo el clima anula estas acciones. Los resultados de este proceso proporcionan aprendizaje sobre cómo cocrear información útil y cómo incrementar la capacidad adaptativa al clima en un paisaje con mucha gestión. Las acciones de restauración de los humedales y la priorización del suministro de agua originaron un hábitat resiliente al impacto del cambio climático, particularmente en marzo, cuando el hábitat estaba más limitado; la protección del suelo combinado con el manejo puede incrementar la resiliencia del ecosistema al cambio climático; y la captación y uso de esta información estuvo influenciada por el papel de los diferentes actores, el cambio rápido de las políticas del agua, discrepancias en los marcos temporales de la toma de decisiones y las crisis inmediatas de la sequía extrema. Mientras que un grupo amplio de accionistas contribuyó conocimiento para las narrativas de escenarios y el desarrollo del modelo, para coproducir información útil, los productos de datos fueron adaptados para un conjunto pequeño de contextos decisivos, lo que con el tiempo llevó a una reducción en la participación de los actores. Una organización fronteriza convocó a los actores de todo un paisaje y los primeros adoptantes ayudaron a construir la legitimidad. A pesar de esto, el uso a gran escala de la información sobre la adaptación climática depende de las políticas locales y estatales, la participación de los órganos decisorios que tienen autoridad legislativa y presupuestaria y de la capacidad para ajustar los productos de datos a las necesidades específicas de las decisiones.

Avian influenza antibody prevalence increases with mercury contamination in wild waterfowl.

Environmental contamination is widespread and can negatively impact wildlife health. Some contaminants, including heavy metals, have immunosuppressive effects, but prior studies have rarely measured contamination and disease simultaneously, which limits our understanding of how contaminants and pathogens interact to influence wildlife health. Here, we measured mercury concentrations, influenza infection, influenza antibodies and body condition in 749 individuals from 11 species of wild ducks overwintering in California. We found that the odds of prior influenza infection increased more than fivefold across the observed range of blood mercury concentrations, while accounting for species, age, sex and date. Influenza infection prevalence was also higher in species with higher average mercury concentrations. We detected no relationship between influenza infection and body fat content. This positive relationship between influenza prevalence and mercury concentrations in migratory waterfowl suggests that immunotoxic effects of mercury contamination could promote the spread of avian influenza along migratory flyways, especially if influenza has minimal effects on bird health and mobility. More generally, these results show that the effects of environmental contamination could extend beyond the geographical area of contamination itself by altering the prevalence of infectious diseases in highly mobile hosts.

Machine learned daily life history classification using low frequency tracking data and automated modelling pipelines: application to North American waterfowl.

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.

Pathways for avian influenza virus spread: GPS reveals wild waterfowl in commercial livestock facilities and connectivity with the natural wetland landscape.

Zoonotic diseases are of considerable concern to the human population and viruses such as avian influenza (AIV) threaten food security, wildlife conservation and human health. Wild waterfowl and the natural wetlands they use are known AIV reservoirs, with birds capable of virus transmission to domestic poultry populations. While infection risk models have linked migration routes and AIV outbreaks, there is a limited understanding of wild waterfowl presence on commercial livestock facilities, and movement patterns linked to natural wetlands. We documented 11 wild waterfowl (three Anatidae species) in or near eight commercial livestock facilities in Washington and California with GPS telemetry data. Wild ducks used dairy and beef cattle feed lots and facility retention ponds during both day and night suggesting use for roosting and foraging. Two individuals (single locations) were observed inside poultry facility boundaries while using nearby wetlands. Ducks demonstrated high site fidelity, returning to the same areas of habitats (at livestock facilities and nearby wetlands), across months or years, showed strong connectivity with surrounding wetlands, and arrived from wetlands up to 1251 km away in the week prior. Telemetry data provides substantial advantages over observational data, allowing assessment of individual movement behaviour and wetland connectivity that has significant implications for outbreak management. Telemetry improves our understanding of risk factors for waterfowl-livestock virus transmission and helps identify factors associated with coincident space use at the wild waterfowl-domestic livestock interface. Our research suggests that even relatively small or isolated natural and artificial water or food sources in/near facilities increases the likelihood of attracting waterfowl, which has important consequences for managers attempting to minimize or prevent AIV outbreaks. Use and interpretation of telemetry data, especially in near-real-time, could provide key information for reducing virus transmission risk between waterfowl and livestock, improving protective barriers between wild and domestic species, and abating outbreaks.

Using the California Waterfowl Tracker to Assess Proximity of Waterfowl to Commercial Poultry in the Central Valley of California.

Migratory waterfowl are the primary reservoir of avian influenza viruses (AIV), which can be spread to commercial poultry. Surveillance efforts that track the location and abundance of wild waterfowl and link those data to inform assessments of risk and sampling for AIV currently do not exist. To assist surveillance and minimize poultry exposure to AIV, here we explored the utility of Remotely Sensed Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery in combination with land-based climate measurements (e.g., temperature and precipitation) to predict waterfowl location and abundance in near real-time in the California Central Valley (CCV), where both wild waterfowl and domestic poultry are densely located. Specifically, remotely collected MODIS and climate data were integrated into a previously developed boosted regression tree (BRT) model to predict and visualize waterfowl distributions across the CCV. Daily model-based predictions are publicly available during the winter as part of the dynamic California Waterfowl Tracker (CWT) web app hosted on the University of California's Cooperative Extension webpage. In this study, we analyzed 52 days of model predictions and produced daily spatiotemporal maps of waterfowl concentrations near the 605 commercial poultry farms in the CCV during January and February of 2019. Exposure of each poultry farm to waterfowl during each day was classified as high, medium, low, or none, depending on the density of waterfowl within 4 km of a farm. Results indicated that farms were at substantially greater risk of exposure in January, when CCV waterfowl populations peak, than in February. For example, during January, 33% (199/605) of the farms were exposed for ≥1 day to high waterfowl density vs. 19% (115/605) of the farms in February. In addition to demonstrating the overall variability of waterfowl location and density, these data demonstrate how remote sensing can be used to better triage AIV surveillance and biosecurity efforts via the utilization of a functional web app-based tool. The ability to leverage remote sensing is an integral advancement toward improving AIV surveillance in waterfowl in close proximity to commercial poultry. Expansion of these types of remote sensing methods, linked to a user-friendly web tool, could be further developed across the continental United States. The BRT model incorporated into the CWT reflects a first attempt to give an accurate representation of waterfowl distribution and density relative to commercial poultry.

Las aves acuáticas migratorias son el principal reservorio de los virus de la influenza aviar (con las siglas en inglés AIV), que pueden transmitirse a la avicultura comercial. Actualmente no existen esfuerzos de vigilancia que rastrean la ubicación y densidad de poblaciones de aves acuáticas silvestres y que vinculen esos datos para informar evaluaciones de riesgo y muestreo para influenza aviar. Para ayudar a la vigilancia y minimizar la exposición de la avicultura comercial a influenza aviar se exploró la utilidad de las imágenes satelitales por espectrorradiómetro de imágenes con resolución moderada (con las siglas en inglés MODIS) y de detección remota en combinación con mediciones climáticas terrestres (por ejemplo, temperatura y precipitación) para predecir la ubicación y densidad de aves acuáticas prácticamente en tiempo real en el Valle Central de California (CCV), donde tanto las aves acuáticas silvestres como las aves domésticas están densamente ubicadas. Específicamente, los datos MODIS y climáticos recopilados de forma remota se integraron en un modelo de árbol de regresión reforzado (BRT) desarrollado previamente para predecir y visualizar la distribución de las aves acuáticas en el Valle Central de California. Las predicciones diarias basadas en modelos están disponibles públicamente durante el invierno como parte de la aplicación dinámica en el del rastreador de aves acuáticas de California (California Waterfowl Tracker, CWT) ubicada en la página de internet de Extensión Cooperativa de la Universidad de California. En este estudio, se analizaron 52 días de predicciones del modelo y se produjeron mapas espaciotemporales diarios con densidades de aves acuáticas cerca de las 605 granjas avícolas comerciales en el Valle Central de California durante enero y febrero de 2019. La exposición de cada granja avícola a las aves acuáticas durante cada día se clasificó como alta, media, baja o nula, dependiendo de la densidad de aves acuáticas dentro de los cuatro kilómetros de una granja. Los resultados indicaron que las granjas tenían un riesgo sustancialmente mayor de exposición en enero, cuando las poblaciones de aves acuáticas en el Valle Central de California alcanzan su punto máximo, en comparación con febrero. Por ejemplo, durante enero, el 33% (199/605) de las granjas estuvieron expuestas durante más de un día a una alta densidad de aves acuáticas frente a un 19% (115/605) de las granjas en febrero. Además de demostrar la variabilidad general de la ubicación y densidad de las aves acuáticas, estos datos demuestran cómo se puede utilizar la teledetección para clasificar mejor los esfuerzos de bioseguridad y vigilancia para la influenza aviar mediante la utilización de una herramienta funcional basada en una aplicación en el internet. La capacidad de aprovechar la teledetección es un avance integral hacia la mejora de la vigilancia para influenza aviar en aves acuáticas en las proximidades de la avicultura comercial. La expansión de estos tipos de métodos de teledetección, vinculados a una herramienta en el internet que es fácil de usar, podría desarrollarse aún más en los Estados Unidos continentales. El modelo de árbol de regresión reforzado incorporado en el sistema de rastreo de aves acuáticas de California refleja un primer intento de brindar una representación precisa de la distribución y densidad de las aves acuáticas en relación con las aves comerciales.

Projected Impacts of Climate, Urbanization, Water Management, and Wetland Restoration on Waterbird Habitat in California's Central Valley.

The Central Valley of California is one of the most important regions for wintering waterbirds in North America despite extensive anthropogenic landscape modification and decline of historical wetlands there. Like many other mediterranean-climate ecosystems across the globe, the Central Valley has been subject to a burgeoning human population and expansion and intensification of agricultural and urban development that have impacted wildlife habitats. Future effects of urban development, changes in water supply management, and precipitation and air temperature related to global climate change on area of waterbird habitat in the Central Valley are uncertain, yet potentially substantial. Therefore, we modeled area of waterbird habitats for 17 climate, urbanization, water supply management, and wetland restoration scenarios for years 2006-2099 using a water resources and scenario modeling framework. Planned wetland restoration largely compensated for adverse effects of climate, urbanization, and water supply management changes on habitat areas through 2065, but fell short thereafter for all except one scenario. Projected habitat reductions due to climate models were more frequent and greater than under the recent historical climate and their magnitude increased through time. After 2065, area of waterbird habitat in all scenarios that included severe warmer, drier climate was projected to be >15% less than in the "existing" landscape most years. The greatest reduction in waterbird habitat occurred in scenarios that combined warmer, drier climate and plausible water supply management options affecting priority and delivery of water available for waterbird habitats. This scenario modeling addresses the complexity and uncertainties in the Central Valley landscape, use and management of related water supplies, and climate to inform waterbird habitat conservation and other resource management planning. Results indicate that increased wetland restoration and additional conservation and climate change adaptation strategies may be warranted to maintain habitat adequate to support waterbirds in the Central Valley.