Ducks Unlimited Canada

A gestão estratégica do conhecimento no ecossistema de inovação enfrenta gargalos críticos devido à fragmentação de fontes e à complexidade da linguagem técnico-científica. Tais barreiras comprometem a agilidade organizacional e a integração entre a academia e o setor produtivo, dificultando o acesso a editais de fomento e parcerias estratégicas. Este artigo apresenta o desenvolvimento da Plataforma ORBITA, uma ferramenta de gestão inteligente concebida sob princípios de Ciência Aberta e Engenharia Ágil. Fruto de uma parceria acadêmico-empresarial, a plataforma utiliza tecnologias de web scraping e a API Gemini para realizar o tratamento semântico e a simplificação de termos técnicos, visando reduzir a carga cognitiva de gestores e empreendedores. Os resultados, validados com diversos perfis de usuários, indicam alta eficácia na recuperação centralizada de informações dinâmicas e na democratização do acesso ao conhecimento estratégico. Conclui-se que a ORBITA atua como um mediador estratégico capaz de reduzir assimetrias informacionais e otimizar processos de PD&I, fortalecendo a competitividade no ecossistema de inovação.

This study evaluates the performance of artificial intelligence (AI) technologies for wetland classification in the province of Alberta, Canada, using integrated remote sensing inputs, including airborne light detection and ranging (LiDAR), orthophotography, and multi-sensor satellite imagery (Sentinel-1, Sentinel-2, PlanetScope). Our primary objective was to assess whether AI-driven modelling approaches, specifically machine learning (ML) and deep learning (DL), can meet Alberta’s provincial wetland mapping standards. We hypothesized that integrating high-resolution LiDAR with multi-seasonal optical and radar data composites into advanced AI algorithms would achieve the required classification accuracy, detail, and minimum mapping unit targets. We tested several methodologies in four ecologically distinct pilot areas representing Alberta’s Boreal, Grassland, and Parkland Natural Regions. AI models included ensemble ML using Extreme Gradient Boosting (XGBoost) and Random Forest, and a DL U-Net convolutional neural network (CNN). AI models were trained on expert-labelled photoplots and validated using in situ field surveys. Our findings demonstrate that both ML and DL models met and, in several cases, exceeded the provincial mapping standards with validation overall accuracies surpassing >70% (form), >80% (class), and >90% (wetland–upland). U-Net CNN models generally produced the highest overall accuracies and most precise wetland extent delineation, but XGBoost offered finer detail and granularity for detailed mapping of rare wetland forms. Integrating LiDAR data and derivatives further enhanced model performance, improving accuracy by as much as 13%. Based on these outcomes, we provide a set of recommendations for scaling up these approaches, focusing on model selection, LiDAR imagery integration, and the continued value of field surveys to support the operational scaling of AI-driven classification approaches for wetland inventory updates across Alberta’s diverse landscapes. However, key challenges remain in scaling up this approach due to the cost of acquiring high-resolution LiDAR and satellite imagery.

Abstract Ammospiza nelsoni subvirgata (Acadian subspecies of Nelson’s Sparrow) breeds in saltmarshes from northern Massachusetts to New Brunswick and eastern Quebec. In the Canadian Maritimes, this subspecies also successfully breeds in diked agricultural lands (i.e., “dikeland”) that were originally created by Acadian settlers in the 1600s. Little is known about the reasons for or consequences of using dikeland for breeding. To fill this knowledge gap, we tracked male and female sparrows, and monitored nest fates in natural saltmarsh and human-made dikeland habitats. We collected fecal samples from adults and nestlings to examine which habitat type they were foraging in, and we also quantified vegetative cover. We hypothesized that flood risk in saltmarshes played an important role in the decision of A. n. subvirgata to nest in dikeland given that the saltmarsh is regularly inundated with tidal water. Based on nest monitoring, we estimated higher overall nest success in dikeland than saltmarshes. Fecal sample analysis showed distinct differences in diet between individuals using dikeland compared to saltmarshes. We also observed differences in vegetation. These results suggest that A. n. subvirgata are able to take advantage of a readily available human-made habitat for breeding. With rising sea levels and increased storm events threatening coastal habitats, it is important to understand if coastal-breeding birds can adapt to changes and what trade-offs exist for individuals who shift to alternative habitats.

The Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, Nunavut (74°55′N, 109°34′W) was established in 2003 to examine Arctic ecosystem processes that would be impacted by climate warming and permafrost degradation. This paper provides a synthesis of how remote sensing has contributed to biogeophysical modelling and monitoring at the CBAWO from 2003 to 2023. Given the location and isolated nature of the CBAWO in the Canadian High Arctic, remote sensing data and derivatives have been instrumental for studies examining ecosystem structure and function at local and landscape scales. In combination with field measurements, remote sensing data facilitated mapping and modelling of vegetation types, % vegetation cover and aboveground phytomass, soil moisture, carbon exchange rates, and permafrost degradation and disturbance. It has been demonstrated that even in an environment with limited vegetation cover and phytomass, spectral vegetation indices (e.g., the normalized difference vegetation index) are able to model various biogeophysical variables. These applications are feasible for research sites such as the CBAWO using high spatial resolution remote sensing data across the visible, infrared, and microwave regions of the electromagnetic spectrum. Furthermore, as the satellite record continues to expand, we will gain a greater understanding of the impacts arising from the expected continued warming at northern latitudes. Although the logistics for research in the Arctic remain challenging, today's technologies (e.g., high spatial resolution satellite remote sensing, automated in situ sensors and data loggers, and wireless communication systems) can support a host of scientific endeavours in the Arctic (and other remote sites) through modelling and monitoring of biogeophysical variables and Earth surface processes with limited but critical field campaigns. The research synthesized here for the CBAWO highlights the essential role of remote sensing of terrestrial ecosystems in the Canadian Arctic.

Knowledge of interspecific and spatiotemporal variation in demography-environment relationships is key for understanding the population dynamics of sympatric species and developing multispecies conservation strategies. We used hierarchical random-effects models to examine interspecific and spatial variation in annual productivity in six migratory ducks (i.e., American wigeon [Mareca americana], blue-winged teal [Spatula discors], gadwall [Mareca strepera], green-winged teal [Anas crecca], mallard [Anas platyrhynchos] and northern pintail [Anas acuta]) across six distinct ecostrata in the Prairie Pothole Region of North America. We tested whether breeding habitat conditions (seasonal pond counts, agricultural intensification, and grassland acreage) or cross-seasonal effects (indexed by flooded rice acreage in primary wintering areas) better explained variation in the proportion of juveniles captured during late summer banding. The proportion of juveniles (i.e., productivity) was highly variable within species and ecostrata throughout 1961-2019 and generally declined through time in blue-winged teal, gadwall, mallard, pintail, and wigeon, but there was no support for a trend in green-winged teal. Productivity in Canadian ecostrata declined with increasing agricultural intensification and increased with increasing pond counts. We also found a strong cross-seasonal effect, whereby more flooded rice hectares during winter resulted in higher subsequent productivity. Our results suggest highly consistent environmental and anthropogenic effects on waterfowl productivity across species and space. Our study advances our understanding of current year and cross-seasonal effects on duck productivity across a suite of species and at finer spatial scales, which could help managers better target working-lands conservation programs on both breeding and wintering areas. We encourage other researchers to evaluate environmental drivers of population dynamics among species in a single modeling framework for a deeper understanding of whether conservation plans should be generalized or customized given limited financial resources.

Climate and land use change are two of the primary threats to global biodiversity; however, each species within a community may respond differently to these facets of global change. Although it is typically assumed that species use the habitat that is advantageous for survival and reproduction, anthropogenic changes to the environment can create ecological traps, making it critical to assess both habitat selection (e.g. where species congregate on the landscape) and the influence of selected habitats on the demographic processes that govern population dynamics. We used a long-term (1958-2011), large-scale, multi-species dataset for waterfowl that spans the United States and Canada to estimate species-specific responses to climate and land use variables in a landscape that has undergone significant environmental change across space and time. We first estimated the effects of change in climate and land use variables on habitat selection and population dynamics for nine species. We then hypothesized that species-specific responses to environmental change would scale with life-history traits, specifically: longevity, nesting phenology and female breeding site fidelity. We observed species-level heterogeneity in the demographic and habitat selection responses to climate and land use change, which would complicate community-level habitat management. Our work highlights the importance of multi-species monitoring and community-level analysis, even among closely related species. We detected several relationships between life-history traits, particularly nesting phenology, and species' responses to environmental change. One species, the early-nesting northern pintail (Anas acuta), was consistently at the extreme end of responses to land use and climate predictors and has been a species of conservation concern since their population began to decline in the 1980s. They, and the blue-winged teal, also demonstrated a positive habitat selection response to the proportion of cropland on the landscape that simultaneously reduced abundance the following year, indicative of susceptibility to ecological traps. By distilling the diversity of species' responses to environmental change within a community, our methodological approach and findings will help improve predictions of community responses to global change and can inform multi-species management and conservation plans in dynamic landscapes that are based on simple tenets of life-history theory.

Abstract Lakes are classified by thermal mixing regimes, with shallow waterbodies historically categorized as continuously mixing systems. Yet, recent studies demonstrate extended summertime stratification in ponds, underscoring the need to reassess thermal classifications for shallow waterbodies. In this study, we examined the summertime thermal dynamics of 34 ponds and shallow lakes across temperate North America and Europe to categorize and identify the drivers of different mixing regimes. We identified three mixing regimes: rarely (n = 18), intermittently (n = 10), and often (n = 6) mixed, where waterbodies mixed an average of 2%, 26%, and 75% of the study period, respectively. Waterbodies in the often mixed category were larger (≥4.17 ha) and stratification weakened with increased wind shear stress, characteristic of “shallow lakes.” In contrast, smaller waterbodies, or “ponds,” mixed less frequently, and stratification strengthened with increased shortwave radiation. Shallow ponds (<0.74 m) mixed intermittently, with daytime stratification often breaking down overnight due to convective cooling. Ponds ≥0.74 m deep were rarely or never mixed, likely due to limited wind energy relative to the larger density gradients associated with slightly deeper water columns. Precipitation events weakened stratification, even causing short‐term mixing (hours to days) in some sites. By examining a broad set of shallow waterbodies, we show that mixing regimes are highly sensitive to very small differences in size and depth, with potential implications for ecological and biogeochemical processes. Ultimately, we propose a new framework to characterize the variable mixing regimes of ponds and shallow lakes.

The eastern population of harlequin duck Histrionicus histrionicus in Canada has been designated a species of special concern since 2001 and as endangered from 1991 to 2001, largely due to low and declining wintering numbers detected in the 1980s. Our objectives were to summarize the current state of knowledge of harlequin duck abundance and distribution in Eastern Canada, and assess trends in wintering abundance across regions over the past 30+ years. Abundance estimates were generated from targeted surveys with thorough spatial coverage of major wintering areas on insular Newfoundland (NL), Nova Scotia (NS), and New Brunswick (NB) between 2015 and 2018, complemented by regional trends estimated from Christmas Bird Count (CBC) data collected at 12 sites across the same wintering range between 1988 and 2021. Overall, targeted surveys indicated that numbers have increased and distributions have expanded beyond historically surveyed regions, particularly in NS. This was supported by trends from CBC data, with steep average increases (mean λ > 1.05) on most CBC circles in Eastern Canada since 1988 or later. Using localized annual rates of mean population change from CBC data to project counts from dedicated surveys, we suggest wintering areas in the provinces of NL, NS, and NB may currently be supporting a combined total of 5682 birds (95% CI 5065-6354) in 2022, well exceeding the recovery target of 3000 individuals listed in the 2007 management plan. Our findings indicate that the eastern population is in recovery, but also emphasize that a consistent, dedicated monitoring program would enable managers to confidently evaluate management actions, and to respond most effectively should regional trends slow or reverse.