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Single phenological measures, like the average rate of phenological advancement, may be insufficient to explain how climate change is driving trends in animal populations. Here, we develop a multifactorial concept of spring phenology—including the onset of spring, spring duration, interannual variability, and their temporal changes—as a driver for population dynamics of migratory terrestrial species in seasonal environments. Using this conceptual model, we found that effects of advancing spring phenology on animal populations may be buffered or amplified depending on the duration and interannual variability of spring green‐up, and those effects are modified by evolutionary and plastic adaptations of species. Furthermore, we compared our modelling results with empirical data on normalized difference vegetation index‐based spring green‐up phenology and population trends of 106 European landbird finding similar associations. We conclude how phenological changes are expected to affect migratory bird populations across Europe and identify regions that are particularly prone to suffer population declines.

 

Many migratory species are declining due to global environmental change. Yet, their complex annual cycles make unravelling the impacts of potential drivers such as climate and land‐use change on migrations a major challenge. Identifying where, when and how threatening processes impact species' migratory journeys and population dynamics is crucial for identifying effective conservation actions. Here, we describe how a new migration modelling framework – Spatially explicit Adaptive Migration Models (SAMMs) – can simulate the optimal behavioural decisions required to migrate across open land‐ or seascapes varying in character over space and time, without requiring predefined behavioural rules. Models of adaptive behaviour have been used widely in theoretical ecology but have great untapped potential in real‐world contexts. Applying adaptive behaviour models across open environments will allow users to explore how migratory species may adapt their routes and usage of intermediate sites in response to environmental change. We outline how SAMMs can be used to model migratory journeys through aerial, terrestrial and aquatic environments, demonstrating their potential using a case study on the common cuckoo (Cuculus canorus) and comparing modelled to observed behaviours. SAMMs offer a tool to identify the key threats faced by migratory species, how they could adapt to future migratory journeys in response to changing environmental conditions and the consequences of not being able to adapt to change.

 

Wind has a significant yet complex effect on bird migration speed. With prevailing south wind, overall migration is generally faster in spring than in autumn. However, studies on the difference in airspeed between seasons have shown contrasting results so far, in part due to their limited geographical or temporal coverage. Using the first full‐year weather radar data set of nocturnal bird migration across western Europe together with wind speed from reanalysis data, we investigate variation of airspeed across season. We additionally expand our analysis of ground speed, airspeed, wind speed, and wind profit variation across time (seasonal and daily) and space (geographical and altitudinal). Our result confirms that wind plays a major role in explaining both temporal and spatial variabilities in ground speed. The resulting airspeed remains relatively constant at all scales (daily, seasonal, geographically and altitudinally). We found that spring airspeed is overall 5% faster in Spring than autumn, but we argue that this number is not significant compared to the biases and limitation of weather radar data. The results of the analysis can be used to further investigate birds' migratory strategies across space and time, as well as their energy use.

 

Abstract Weather radar networks have great potential for continuous and long-term monitoring of aerial biodiversity of birds, bats, and insects. Biological data from weather radars can support ecological research, inform conservation policy development and implementation, and increase the public’s interest in natural phenomena such as migration. Weather radars are already used to study animal migration, quantify changes in populations, and reduce aerial conflicts between birds and aircraft. Yet efforts to establish a framework for the broad utilization of operational weather radar for biodiversity monitoring are at risk without suitable data policies and infrastructure in place. In Europe, communities of meteorologists and ecologists have made joint efforts toward sharing and standardizing continent-wide weather radar data. These efforts are now at risk as new meteorological data exchange policies render data useless for biodiversity monitoring. In several other parts of the world, weather radar data are not even available for ecological research. We urge policy makers, funding agencies, and meteorological organizations across the world to recognize the full potential of weather radar data. We propose several actions that would ensure the continued capability of weather radar networks worldwide to act as powerful tools for biodiversity monitoring and research. 

Climate change is drastically changing the timing of biological events across the globe. Changes in the phenology of seasonal migrations between the breeding and wintering grounds have been observed across biological taxa, including birds, mammals, and insects. For birds, strong links have been shown between changes in migration phenology and changes in weather conditions at the wintering, stopover, and breeding areas. For other animal taxa, the current understanding of, and evidence for, climate (change) influences on migration still remains rather limited, mainly due to the lack of long‐term phenology datasets. Bracken Cave in Texas (USA) holds one of the largest bat colonies of the world. Using weather radar data, a unique 23‐year (1995–2017) long time series was recently produced of the spring and autumn migration phenology of Brazilian free‐tailed bats (Tadarida brasiliensis) at Bracken Cave. Here, we analyse these migration phenology time series in combination with gridded temperature, precipitation, and wind data across Mexico and southern USA, to identify the climatic drivers of (changes in) bat migration phenology. Perhaps surprisingly, our extensive spatiotemporal search did not find temperature to influence either spring or autumn migration. Instead, spring migration phenology seems to be predominantly driven by wind conditions at likely wintering or spring stopover areas during the migration period. Autumn migration phenology, on the other hand, seems to be dominated by precipitation to the east and north‐east of Bracken Cave. Long‐term changes towards more frequent migration and favourable wind conditions have, furthermore, allowed spring migration to occur 16 days earlier. Our results illustrate how some of the remaining knowledge gaps on the influence of climate (change) on bat migration and abundance can be addressed using weather radar analyses.

 

Light‐level geolocator tags use ambient light recordings to estimate the whereabouts of an individual over the time it carried the device. Over the past decade, these tags have emerged as an important tool and have been used extensively for tracking animal migrations, most commonly small birds.

Analysing geolocator data can be daunting to new and experienced scientists alike. Over the past decades, several methods with fundamental differences in the analytical approach have been developed to cope with the various caveats and the often complicated data.

Here, we explain the concepts behind the analyses of geolocator data and provide a practical guide for the common steps encompassing most analyses – annotation of twilights, calibration, estimating and refining locations, and extraction of movement patterns – describing good practices and common pitfalls for each step.

We discuss criteria for deciding whether or not geolocators can answer proposed research questions, provide guidance in choosing an appropriate analysis method and introduce key features of the newest open‐source analysis tools.

We provide advice for how to interpret and report results, highlighting parameters that should be reported in publications and included in data archiving.

Finally, we introduce a comprehensive supplementary online manual that applies the concepts to several datasets, demonstrates the use of open‐source analysis tools with step‐by‐step instructions and code and details our recommendations for interpreting, reporting and archiving.