When and where to move: Dynamic occupancy models explain the range dynamics of a food nomadic bird under climate and land cover change

Contributions of Distribution Modelling to the Ecological Study of Psittaciformes

We provide an overview of the use of species distribution modeling to address research questions related to parrot ecology and conservation at a global scale. We conducted a literature search and applied filters to select the 82 most relevant studies to discuss. The study of parrot species distribution has increased steadily in the past 30 years, with methods and computing development maturing and facilitating their application for a wide range of research and applied questions. Conservation topics was the most popular topic (37%), followed by ecology (34%) and invasion ecology (20%). The role of abiotic factors explaining parrot distribution is the most frequent ecological application. The high prevalence of studies supporting on-ground conservation problems is a remarkable example of reduction in the research–action gap. Prediction of invasion risk and assessment of invasion effect were more prevalent than examples evaluating the environmental or economic impact of these invasions. The integration of species distribution models with other tools in the decision-making process and other data (e.g., landscape metrics, genetic, behavior) could even further expand the range of applications and provide a more nuanced understanding of how parrot species are responding to their even more changing landscape and threats.

Species-landscape interactions drive divergent population trajectories in four forest-dependent Afromontane forest songbird species within a biodiversity hotspot in South Africa

Species confined to naturally fragmented habitats may exhibit intrinsic population complexity which may challenge interpretations of species response to anthropogenic landscape transformation. In South Africa, where native forests are naturally fragmented, forest-dependent birds have undergone range declines since 1992, most notably among insectivores. These insectivores appear sensitive to the quality of natural matrix habitats, and it is unknown whether transformation of the landscape matrix has disrupted gene flow in these species. We undertook a landscape genetics study of four forest-dependent insectivorous songbirds across southeast South Africa. Microsatellite data were used to conduct a priori optimization of landscape resistance surfaces (land cover, rivers and dams, and elevation) using cost-distances along least-cost pathway (LCP), and resistance distances (IBR). We detected pronounced declines in effective population sizes over the past two centuries for the endemic forest specialist Cossypha dichroa and Batis capensis, alongside recent gene flow disruption in B. capensis, C. dichroa and Pogonocichla stellata. Landscape resistance modelling showed both native forest and dense thicket configuration facilitates gene flow in P. stellata, B. capensis and C. dichroa. Facultative dispersal of P. stellata through dense thicket likely aided resilience against historic landscape transformation, whereas combined forest-thicket degradation adversely affected the forest generalist B. capensis. By contrast, Phylloscopus ruficapilla appears least reliant upon landscape features to maintain gene flow and was least impacted by anthropogenic landscape transformation. Collectively, gene flow in all four species is improved at lower elevations, along river valleys, and riparian corridors- where native forest and dense thicket better persist. Consistent outperformance of LCP over IBR land-cover models for P. stellata, B. capensis and C. dichroa demonstrates the benefits of wildlife corridors for South African forest-dependent bird conservation, to ameliorate the extinction debts from past and present anthropogenic forest exploitation.

Can dynamic occupancy models improve predictions of species' range dynamics? A test using Swiss birds

Predictions of species' current and future ranges are needed to effectively manage species under environmental change. Species ranges are typically estimated using correlative species distribution models (SDMs), which have been criticized for their static nature. In contrast, dynamic occupancy models (DOMs) explicitily describe temporal changes in species' occupancy via colonization and local extinction probabilities, estimated from time series of occurrence data. Yet, tests of whether these models improve predictive accuracy under current or future conditions are rare. Using a long-term data set on 69 Swiss birds, we tested whether DOMs improve the predictions of distribution changes over time compared to SDMs. We evaluated the accuracy of spatial predictions and their ability to detect population trends. We also explored how predictions differed when we accounted for imperfect detection and parameterized models using calibration data sets of different time series lengths. All model types had high spatial predictive performance when assessed across all sites (mean AUC > 0.8), with flexible machine learning SDM algorithms outperforming parametric static and DOMs. However, none of the models performed well at identifying sites where range changes are likely to occur. In terms of estimating population trends, DOMs performed best, particularly for species with strong population changes and when fit with sufficient data, while static SDMs performed very poorly. Overall, our study highlights the importance of considering what aspects of performance matter most when selecting a modelling method for a particular application and the need for further research to improve model utility. While DOMs show promise for capturing range dynamics and inferring population trends when fitted with sufficient data, computational constraints on variable selection and model fitting can lead to reduced spatial accuracy of predictions, an area warranting more attention.

Extrinsic stressors modulate resource evaluations: insights from territoriality under artificial noise

Background

Competition is considered to rely on the value attributed to resources by animals, but the influence of extrinsic stressors on this value remains unexplored. Although natural or anthropogenic environmental stress often drives decreased competition, assumptions that this relies on resource devaluation are without formal evidence. According to theory, physiological or perceptual effects may influence contest behaviour directly, but motivational changes due to resource value are expected to manifest as behavioural adjustments only in interaction with attainment costs and resource benefits. Thus, we hypothesise that stressor-induced resource devaluations will impose greater effects when attainment costs are high, but not when resource benefits are higher. Noise may elicit such effects because it impacts the acoustic environment and imposes physiological and behavioural costs to animals. Therefore, we manipulated the acoustic environment using playbacks of artificial noise to test our hypotheses in the territorial male Siamese fighting fish, Betta splendens.

Results

Compared to a no-playback control, noise reduced defense motivation only when territory owners faced comparatively bigger opponents that impose greater injury costs, but not when territories also contained bubble nests that offer reproductive benefits. In turn, nest-size decreases were noted only after contests under noise treatment, but temporal nest-size changes relied on cross-contest variation in noise and comparative opponent size. Thus, the combined effects of noise are conditional on added attainment costs and offset by exceeding resource benefits.

Conclusion

Our findings provide support for the hypothesised modulation of resource value under extrinsic stress and suggest implications for competition under increasing anthropogenic activity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12983-021-00397-x.

An Empirical and Mechanistic Explanation of Abundance-Occupancy Relationships for a Critically Endangered Nomadic Migrant

The positive abundance-occupancy relationship (AOR) is a pervasive pattern in macroecology. Similarly, the association between occupancy (or probability of occurrence) and abundance is also usually assumed to be positive and in most cases constant. Examples of AORs for nomadic species with variable distributions are extremely rare. Here we examined temporal and spatial trends in the AOR over 7 years for a critically endangered nomadic migrant that relies on dynamic pulses in food availability to breed. We predicted a negative temporal relationship, where local mean abundances increase when the number of occupied sites decreases, and a positive relationship between local abundances and the probability of occurrence. We also predicted that these patterns are largely attributable to spatiotemporal variation in food abundance. The temporal AOR was significantly negative, and annual food availability was significantly positively correlated with the number of occupied sites but negatively correlated with abundance. Thus, as food availability decreased, local densities of birds increased, and vice versa. The abundance-probability of occurrence relationship was positive and nonlinear but varied between years due to differing degrees of spatial aggregation caused by changing food availability. Importantly, high abundance (or occupancy) did not necessarily equate to high-quality habitat and may be indicative of resource bottlenecks or exposure to other processes affecting vital rates. Our results provide a rare empirical example that highlights the complexity of AORs for species that target aggregated food resources in dynamic environments.

Opportunistic citizen science data transform understanding of species distributions, phenology, and diversity gradients for global change research

Opportunistic citizen science (CS) programs allow volunteers to report species observations from anywhere, at any time, and can assemble large volumes of historic and current data at faster rates than more coordinated programs with standardized data collection. This can quickly provide large amounts of species distributional data, but whether this focus on participation comes at a cost in data quality is not clear. Although automated and expert vetting can increase data reliability, there is no guarantee that opportunistic data will do anything more than confirm information from professional surveys. Here, we use eButterfly, an opportunistic CS program, and a comparable dataset of professionally collected observations, to measure the amount of new distributional species information that opportunistic CS generates. We also test how well opportunistic CS can estimate regional species richness for a large group of taxa (>300 butterfly species) across a broad area. We find that eButterfly contributes new distributional information for >80% of species, and that opportunistically submitting observations allowed volunteers to spot species ~35 days earlier than professionals. Although eButterfly did a relatively poor job at predicting regional species richness by itself (detecting only about 35-57% of species per region), it significantly contributed to regional species richness when used with the professional dataset (adding ~3 species that had gone undetected in professional surveys per region). Overall, we find that the opportunistic CS model can provide substantial complementary species information when used alongside professional survey data. Our results suggest that data from opportunistic CS programs in conjunction with professional datasets can strongly increase the capacity of researchers to estimate species richness, and provide unique information on species distributions and phenologies that are relevant to the detection of the biological consequences of global change.