A comparison of drone imagery and groundbased methods for estimating the extent of habitat destruction by lesser snow geese (\u3ci\u3eAnser caerulescens caerulescens\u3c/i\u3e) in La Pérouse Bay

Lesser snow goose (Anser caerulescens caerulescens) populations have dramatically altered vegetation communities through increased foraging pressure. In remote regions, regular habitat assessments are logistically challenging and time consuming. Drones are increasingly being used by ecologists to conduct habitat assessments, but reliance on georeferenced data as ground truth may not always be feasible. We estimated goose habitat degradation using photointerpretation of drone imagery and compared estimates to those made with ground-based linear transects. In July 2016, we surveyed five study plots in La Pérouse Bay, Manitoba, to evaluate the effectiveness of a fixed-wing drone with simple Red Green Blue (RGB) imagery for evaluating habitat degradation by snow geese. Ground-based land cover data was collected and grouped into barren, shrub, or non-shrub categories. We compared estimates between ground-based transects and those made from unsupervised classification of drone imagery collected at altitudes of 75, 100, and 120 m above ground level (ground sampling distances of 2.4, 3.2, and 3.8 cm respectively). We found large time savings during the data collection step of drone surveys, but these savings were ultimately lost during imagery processing. Based on photointerpretation, overall accuracy of drone imagery was generally high (88.8% to 92.0%) and Kappa coefficients were similar to previously published habitat assessments from drone imagery. Mixed model estimates indicated 75m drone imagery overestimated barren (F2,182 = 100.03, P \u3c 0.0001) and shrub classes (F2,182 = 160.16, P \u3c 0.0001) compared to ground estimates. Inconspicuous graminoid and forb species (non-shrubs) were difficult to detect from drone imagery and were underestimated compared to ground-based transects (F2,182 = 843.77, P \u3c 0.0001). Our findings corroborate previous findings, and that simple RGB imagery is useful for evaluating broad scale goose damage, and may play an important role in measuring habitat destruction by geese and other agents of environmental change

Fight or Flight: Parental Decisions about Predators at Nests of Northern Bobwhites (Colinus virginianus)

Patterns of nest defense against predators by ground-nesting bird species in the wild are poorly understood, largely because of a historical inability to directly monitor nests. Most nest-defense studies have observed responses elicited from artificial predators or human observers presented to nesting birds, and few have attempted to present these events in the context of predator—prey relationships found in the wild. We hypothesized that predator threat level (e.g., the threat posed to the clutch or to the clutch and the attending adult), parental characteristics, clutch investment, and future reproductive opportunities would influence avian nest-defense decisions. During 1999–2006, we examined predation events (n = 242) from 790 video-monitored Northern Bobwhite (Colinus virginianus) nests. We evaluated parental, predator, daily, and seasonal correlates that potentially contributed to patterns of nest defense by Northern Bobwhites using a model selection approach. The top model showed that nest defense was strongest at nests with larger predators that posed a threat to both adults and the clutch. This model also contained clutch size, but parameter estimates suggest that predator type was the only significant factor determining rates of nest defense. Our results suggest that Northern Bobwhites use the threat posed to the nest and the attending adult by the approaching predator as the primary cue in decisions to engage in nest defense

SNAPSHOT USA 2020: A second coordinated national camera trap survey of the United States during the COVID-19 pandemic

Managing wildlife populations in the face of global change requires regular data on the abundance and distribution of wild animals, but acquiring these over appropriate spatial scales in a sustainable way has proven challenging. Here we present the data from Snapshot USA 2020, a second annual national mammal survey of the USA. This project involved 152 scientists setting camera traps in a standardized protocol at 1485 locations across 103 arrays in 43 states for a total of 52,710 trap-nights of survey effort. Most (58) of these arrays were also sampled during the same months (September and October) in 2019, providing a direct comparison of animal populations in 2 years that includes data from both during and before the COVID-19 pandemic. All data were managed by the eMammal system, with all species identifications checked by at least two reviewers. In total, we recorded 117,415 detections of 78 species of wild mammals, 9236 detections of at least 43 species of birds, 15,851 detections of six domestic animals and 23,825 detections of humans or their vehicles. Spatial differences across arrays explained more variation in the relative abundance than temporal variation across years for all 38 species modeled, although there are examples of significant site-level differences among years for many species. Temporal results show how species allocate their time and can be used to study species interactions, including between humans and wildlife. These data provide a snapshot of the mammal community of the USA for 2020 and will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, and the impacts of species interactions on daily activity patterns. There are no copyright restrictions, and please cite this paper when using these data, or a subset of these data, for publication

FIGHT OR FLIGHT: PARENTAL DECISIONS ABOUT PREDATORS AT NESTS OF NORTHERN BOBWHITES (COLINUS VIRGINIANUS)

Patterns of nest defense against predators by ground-nesting bird species in the wild are poorly understood, largely because of a historical inability to directly monitor nests. Most nest-defense studies have observed responses elicited from artificial predators or human observers presented to nesting birds, and few have attempted to present these events in the context of predator–prey relationships found in the wild. We hypothesized that predator threat level (e.g., the threat posed to the clutch or to the clutch and the attending adult), parental characteristics, clutch investment, and future reproductive opportunities would influence avian nest-defense decisions. During 1999–2006, we examined predation events (n = 242) from 790 video-monitored Northern Bobwhite (Colinus virginianus) nests. We evaluated parental, predator, daily, and seasonal correlates that potentially contributed to patterns of nest defense by Northern Bobwhites using a model selection approach. The top model showed that nest defense was strongest at nests with larger predators that posed a threat to both adults and the clutch. This model also contained clutch size, but parameter estimates suggest that predator type was the only significant factor determining rates of nest defense. Our results suggest that Northern Bobwhites use the threat posed to the nest and the attending adult by the approaching predator as the primary cue in decisions to engage in nest defense. Los patrones de defensa del nido contra depredadores por parte de especies de aves silvestres que anidan en el suelo son pobremente entendidos, en gran parte debido a una inhabilidad histórica para monitorear directamente los nidos. La mayoría de estudios de defensa del nido han observado respuestas desencadenadas por depredadores artificiales o por observadores humanos que se presentan a las aves anidantes, y pocos han intentado presentar dichos eventos en el contexto de las relaciones depredador-presa que se encuentran en condiciones silvestres. Planteamos la hipótesis de que el nivel de amenaza del depredador (e.g. la amenaza impuesta a la nidada o a la nidada y al adulto que cuida de ella), las características de los padres, la inversión en la nidada, y las oportunidades futuras de reproducirse podrían influenciar las decisiones de defensa del nido de las aves. Entre 1999 y 2006 examinamos los eventos de depredación (n = 242) de 790 nidos de Colinus virginianus monitoreados en vídeo. Evaluamos las variables parentales, del depredador, diarias y estacionales que potencialmente contribuyen a los patrones de defensa del nido por C. virginianus usando una aproximación de selección de modelos. El mejor modelo mostró que la defensa del nido fue más fuerte en nidos con depredadores más grandes que amenazaban a la nidada y al adulto que cuidaba de ella. Este modelo también incluyó el tamaño de la nidada, pero los parámetros estimados sugieren que el tipo de depredador fue el único factor que afecta significativamente las tasas de defensa del nido. Nuestros resultados sugieren que C. virginianus usa la amenaza impuesta por el depredador que se acerca al nido y al adulto que lo cuida como la pista primaria para la toma de decisiones sobre la defensa del nido

A comparison of drone imagery and ground-based methods for estimating the extent of habitat destruction by lesser snow geese (Anser caerulescens caerulescens) in La Pérouse Bay.

Lesser snow goose (Anser caerulescens caerulescens) populations have dramatically altered vegetation communities through increased foraging pressure. In remote regions, regular habitat assessments are logistically challenging and time consuming. Drones are increasingly being used by ecologists to conduct habitat assessments, but reliance on georeferenced data as ground truth may not always be feasible. We estimated goose habitat degradation using photointerpretation of drone imagery and compared estimates to those made with ground-based linear transects. In July 2016, we surveyed five study plots in La Pérouse Bay, Manitoba, to evaluate the effectiveness of a fixed-wing drone with simple Red Green Blue (RGB) imagery for evaluating habitat degradation by snow geese. Ground-based land cover data was collected and grouped into barren, shrub, or non-shrub categories. We compared estimates between ground-based transects and those made from unsupervised classification of drone imagery collected at altitudes of 75, 100, and 120 m above ground level (ground sampling distances of 2.4, 3.2, and 3.8 cm respectively). We found large time savings during the data collection step of drone surveys, but these savings were ultimately lost during imagery processing. Based on photointerpretation, overall accuracy of drone imagery was generally high (88.8% to 92.0%) and Kappa coefficients were similar to previously published habitat assessments from drone imagery. Mixed model estimates indicated 75m drone imagery overestimated barren (F2,182 = 100.03, P < 0.0001) and shrub classes (F2,182 = 160.16, P < 0.0001) compared to ground estimates. Inconspicuous graminoid and forb species (non-shrubs) were difficult to detect from drone imagery and were underestimated compared to ground-based transects (F2,182 = 843.77, P < 0.0001). Our findings corroborate previous findings, and that simple RGB imagery is useful for evaluating broad scale goose damage, and may play an important role in measuring habitat destruction by geese and other agents of environmental change