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Aim: Artificial light at night (ALAN) and roads are known threats to nocturnally migrating birds. How associations with ALAN and roads are defined in combination for these species at the population level across the full annual cycle has not been explored. Location: Western Hemisphere. Methods: We estimated range‐wide exposure, predictor importance and the prevalence of positive associations with ALAN and roads at a weekly temporal resolution for 166 nocturnally migrating bird species in three orders: Passeriformes (n = 104), Anseriformes (n = 27) and Charadriiformes (n = 35). We clustered Passeriformes based on the prevalence of positive associations. Results: Positive associations with ALAN and roads were more prevalent for Passeriformes during migration when exposure and importance were highest. Positive associations with ALAN and roads were more prevalent for Anseriformes and Charadriiformes during the breeding season when exposure was lowest. Importance was uniform for Anseriformes and highest during migration for Charadriiformes. Our cluster analysis identified three groups of Passeriformes, each having similar associations with ALAN and roads. The first occurred in eastern North America during migration where exposure, prevalence, and importance were highest. The second wintered in Mexico and Central America where exposure, prevalence and importance were highest. The third occurred throughout North America where prevalence was low, and exposure and importance were uniform. The first and second were comprised of dense habitat specialists and long‐distance migrants. The third was comprised of open habitat specialists and short distance migrants. Main conclusions: Our findings suggest ALAN and roads pose the greatest risk during migration for Passeriformes and during the breeding season for Anseriformes and Charadriiformes. Our results emphasise the close relationship between ALAN and roads, the diversity of associations dictated by taxonomy, exposure, migration strategy and habitat and the need for more informed and comprehensive mitigation strategies where ALAN and roads are treated as interconnected threats. [ABSTRACT FROM AUTHOR]

Aim: Species distribution maps are essential for assessing extinction risk and guiding conservation efforts. However, most come sourced as expert‐drawn range maps with known issues of accuracy or are developed with overly complex modelling procedures. Thus, data‐driven alternatives that are accessible and reliable are a welcome addition to the spatial conservation toolkit. Here, we developed a geospatial workflow to refine the distribution of a species from its extent of occurrence (EOO) to area of habitat (AOH) within the species range map. The range maps are produced with an inverse distance weighted (IDW) interpolation procedure using presence and absence points derived from primary biodiversity data. Location: The Americas (North, South, Central America and the Caribbean). Methods: As a case study, we mapped the distribution of 723 resident forest birds in the Americas and assessed their performance in comparison with expert‐drawn range maps. We evaluated differences in accuracy, spatial overlap, range map size and derived AOH. Results: The geospatial workflow generated IDW range maps with a higher overall accuracy (87% versus 62%) and fewer errors of omission (<1%) and commission (14%) than the expert range maps (28% both errors). The spatial overlap between both datasets was low (35%), but the agreement increased in areas of high probability of occurrence (68%). We did not find significant differences in range size, but the AOH derived from the expert‐drawn range maps was consistently smaller than the estimates from the IDW range maps. Main Conclusions: Our geospatial workflow provides a straightforward approach to accurately map species ranges and the estimation of area of habitat (AOH) for conservation planning and decision‐making. Conversely, procedures that refine expert‐drawn range maps to obtain AOH risk producing biased estimates for local‐scale applications. [ABSTRACT FROM AUTHOR]

Aim: Evaluating conservation effectiveness is essential to protect at‐risk species and to maximize the limited resources available to land managers. Over 60% of North American grassland and aridlands have been lost since the 1800s. Birds in these habitats are among the most imperilled in North America, yet most remaining habitats are unprotected. Despite the need to measure impact, conservation efforts on private and working lands are rarely evaluated, due in part to limited availability of suitable methods. Location: Northern Great Plains of North America. Methods: We developed a novel metric to evaluate grassland and aridland bird community response to habitat management, the Bird‐Friendliness Index (BFI), consisting of density estimates of grassland and aridland birds weighted by conservation need and a functional diversity metric to incorporate resiliency. We used the BFI to identify ecologically significant areas for grassland and aridland birds and compared them with other prioritizations. Then, we used the BFI to evaluate the effects of simulated habitat management on grassland and aridland bird communities. Results: The most resilient bird communities were found in the Prairie Potholes region of Alberta, Saskatchewan and North Dakota and the lowest BFI values in the southern and western regions of the Northern Great Plains. BFI values were significantly greater in areas included in one or more prioritization, and increased with the number of prioritizations an area was included within. BFI values increased in response to simulated bird‐friendly habitat management. Main conclusions: Our findings suggest that practices recommended for use in bird‐friendly grassland habitat management plans will increase the abundance and resilience of the grassland and aridland bird community and that bird community responses to management will be detected using the BFI. The BFI is a tool by which conservationists and managers can carry out adaptive management and accountable conservation now and into the future. [ABSTRACT FROM AUTHOR]

Aim: Our aim was to develop predictive statistical models for mapping the abundance of 18 waterfowl species at a pan‐Canadian level. We refined the previous generation of national waterfowl models by (a) developing new, more interpretable statistical models that (b) explicitly account for spatiotemporal variations in waterfowl abundance, while (c) testing for associations with an updated suite of habitat covariates. Location: All of Canada, excluding the Northern Arctic ecozone. Methods: Our response variables were annual species counts on 2,227 aerial‐survey segments over a period of 25 years (1990–2015). Combining machine‐learning and hierarchical regression modelling, we devised an innovative covariate selection strategy to select for each species the best subset of a panel of 232 candidate habitat covariates. With the selected covariates, we implemented hierarchical generalized linear models in a Bayesian framework, using the integrated nested Laplace approximation and stochastic partial differential equation approaches. Results: On average, our models explained 47% of the observed variance for spatiotemporal predictions and 74% for temporally averaged spatial predictions. The 18 species models included 94 significant waterfowl‐habitat associations involving 42 distinct habitat covariates, with an average of 5.3 covariates per model. Covariates for forest attributes were the most represented in our models. The proportional biomass of Populus tremuloides was the most frequently selected covariate (10/94 associations in 10/18 species). Model predictions generated spatial and spatiotemporal maps of species abundances over almost all of Canada. Main conclusions: We showed that it is possible to efficiently combine machine‐learning, variable selection and hierarchical Bayesian methods that exploit high‐dimensional covariate spaces. Our approach yielded powerful and easily interpretable species distribution models with very few covariates, while accounting for residual autocorrelation. Possible applications of the resulting models and maps include the development of biodiversity indicators, the evaluation and execution of conservation planning strategies, and ecosystem services monitoring. [ABSTRACT FROM AUTHOR]

The urgent need to mitigate and adapt to climate change necessitates a comprehensive understanding of carbon cycling dynamics. Traditionally, global carbon cycle models have focused on vegetation, but recent research suggests that animals can play a significant role in carbon dynamics under some circumstances, potentially enhancing the effectiveness of nature‐based solutions to mitigate climate change. However, links between animals, plants, and carbon remain unclear. We explored the complex interactions between defaunation and ecosystem carbon in Earth's most biodiverse and carbon‐rich biome, tropical rainforests. Defaunation can change patterns of seed dispersal, granivory, and herbivory in ways that alter tree species composition and, therefore, forest carbon above‐ and belowground. Most studies we reviewed show that defaunation reduces carbon storage 0−26% in the Neo‐ and Afrotropics, primarily via population declines in large‐seeded, animal‐dispersed trees. However, Asian forests are not predicted to experience changes because their high‐carbon trees are wind dispersed. Extrapolating these local effects to entire ecosystems implies losses of ∼1.6 Pg CO2 equivalent across the Brazilian Atlantic Forest and 4−9.2 Pg across the Amazon over 100 years and of ∼14.7−26.3 Pg across the Congo basin over 250 years. In addition to being hard to quantify with precision, the effects of defaunation on ecosystem carbon are highly context dependent; outcomes varied based on the balance between antagonist and mutualist species interactions, abiotic conditions, human pressure, and numerous other factors. A combination of experiments, large‐scale comparative studies, and mechanistic models could help disentangle the effects of defaunation from other anthropogenic forces in the face of the incredible complexity of tropical forest systems. Overall, our synthesis emphasizes the importance of—and inconsistent results when—integrating animal dynamics into carbon cycle models, which is crucial for developing climate change mitigation strategies and effective policies. [ABSTRACT FROM AUTHOR]