Biome-scale signatures of land-use change on raptor abundance: insights from single-visit detection-based models

Spatial and temporal non-stationarity in long-term population dynamics of over-wintering birds of North America

Understanding population changes across long time scales and at fine spatiotemporal resolutions is important for confronting a broad suite of conservation challenges. However, this task is hampered by a lack of quality long-term census data for multiple species collected across large geographic regions. Here, we used century-long (1919-2018) data from the Audubon Christmas Bird Count (CBC) survey to assess population changes in over 300 avian species in North America and evaluate their temporal non-stationarity. To estimate population sizes across the entire century, we employed a Bayesian hierarchical model that accounts for species detection probabilities, variable sampling effort, and missing data. We evaluated population trends using generalized additive models (GAMs) and assessed temporal non-stationarity in the rate of population change by extracting the first derivatives from the fitted GAM functions. We then summarized the population dynamics across species, space, and time using a non-parametric clustering algorithm that categorized individual population trends into four distinct trend clusters. We found that species varied widely in their population trajectories, with over 90% of species showing a considerable degree of spatial and/or temporal non-stationarity, and many showing strong shifts in the direction and magnitude of population trends throughout the past century. Species were roughly equally distributed across the four clusters of population trajectories, although grassland, forest, and desert specialists more commonly showed declining trends. Interestingly, for many species, region-wide population trends often differed from those observed at individual sites, suggesting that conservation decisions need to be tailored to fine spatial scales. Together, our results highlight the importance of considering spatial and temporal non-stationarity when assessing long-term population changes. More generally, we demonstrate the promise of novel statistical techniques for improving the utility and extending the temporal scope of existing citizen science datasets.

Using single visits into integrated occupancy models to make the most of existing monitoring programs

A major challenge in statistical ecology consists of integrating knowledge from different data sets to produce robust ecological indicators. To estimate species distribution, occupancy models are a flexible framework that can accommodate several data sets obtained from different sampling methods. However, repeating visits at sampling sites is a prerequisite for using standard occupancy models. Occupancy models were recently developed to analyze detection/non-detection data collected during a single visit. To date, single-visit occupancy models have never been used to integrate several different data sets. Here, we showcase an approach that combines two data sets into an integrated single-visit occupancy model. As a case study, we estimated the distribution of common bottlenose dolphin (Tursiops truncatus) over the northwestern Mediterranean Sea by combining 24,624 km of aerial surveys and 21,464 km of at-sea monitoring. We compared the outputs of single- vs. repeated-visit occupancy models into integrated occupancy models. Integrated models allowed a better sampling coverage of the targeted population, which provided a better precision for occupancy estimates than occupancy models using data sets in isolation. Overall, single- and repeated-visit integrated occupancy models produced similar inference about the distribution of bottlenose dolphins. We suggest that single-visit occupancy models open promising perspectives for the use of existing ecological data sets.

Roadside Car Surveys: Methodological Constraints and Solutions for Estimating Parrot Abundances across the World

Parrots stand out among birds because of their poor conservation status and the lack of available information on their population sizes and trends. Estimating parrot abundance is complicated by the high mobility, gregariousness, patchy distributions, and rarity of many species. Roadside car surveys can be useful to cover large areas and increase the probability of detecting spatially aggregated species or those occurring at very low densities. However, such surveys may be biased due to their inability to handle differences in detectability among species and habitats. We conducted 98 roadside surveys, covering > 57,000 km across 20 countries and the main world biomes, recording ca. 120,000 parrots from 137 species. We found that larger and more gregarious species are more easily visually detected and at greater distances, with variations among biomes. However, raw estimates of relative parrot abundances (individuals/km) were strongly correlated (r = 0.86–0.93) with parrot densities (individuals/km2) estimated through distance sampling (DS) models, showing that variability in abundances among species (>40 orders of magnitude) overcomes any potential detectability bias. While both methods provide similar results, DS cannot be used to study parrot communities or monitor the population trends of all parrot species as it requires a minimum of encounters that are not reached for most species (64% in our case), mainly the rarest and more threatened. However, DS may be the most suitable choice for some species-specific studies of common species. We summarize the strengths and weaknesses of both methods to guide researchers in choosing the best–fitting option for their particular research hypotheses, characteristics of the species studied, and logistical constraints.