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he most basic behavioural states of animals can be described as active or passive. While high-resolution observations of activity patterns can provide insights into the ecology of animal species, few methods are able to measure the activity of individuals of small taxa in their natural environment. We present a novel approach in which a combination of automatic radiotracking and machine learning is used to distinguish between active and passive behaviour in small vertebrates fitted with lightweight transmitters (<0.4 g).
We used a dataset containing >3 million signals from very-high-frequency (VHF) telemetry from two forest-dwelling bat species (Myotis bechsteinii [n = 52] and Nyctalus leisleri [n = 20]) to train and test a random forest model in assigning either active or passive behaviour to VHF-tagged individuals. The generalisability of the model was demonstrated by recording and classifying the behaviour of tagged birds and by simulating the effect of different activity levels with the help of humans carrying transmitters. The model successfully classified the activity states of bats as well as those of birds and humans, although the latter were not included in model training (F1 0.96–0.98).
We provide an ecological case-study demonstrating the potential of this automated monitoring tool. We used the trained models to compare differences in the daily activity patterns of two bat species. The analysis showed a pronounced bimodal activity distribution of N. leisleri over the course of the night while the night-time activity of M. bechsteinii was relatively constant. These results show that subtle differences in the timing of species' activity can be distinguished using our method.
Our approach can classify VHF-signal patterns into fundamental behavioural states with high precision and is applicable to different terrestrial and flying vertebrates. To encourage the broader use of our radiotracking method, we provide the trained random forest models together with an R package that includes all necessary data processing functionalities. In combination with state-of-the-art open-source automated radiotracking, this toolset can be used by the scientific community to investigate the activity patterns of small vertebrates with high temporal resolution, even in dense vegetation.
Aim
How species respond to climate change is influenced by their sensitivity to climatic conditions (i.e. their climatic niche) and aspects of their adaptive capacity (e.g. their dispersal ability and ecological niche). To date, it is largely unknown whether and how species’ sensitivity to climate change and their adaptive capacity covary. However, understanding this relationship is important to predict the potential consequences of a changing climate for species assemblages. Here, we test how species’ sensitivity to climate change and trait-based measures of their ecological adaptive capacity (i) vary along a broad elevational gradient and (ii) covary across a large number of bird species.
Location
A Neotropical elevational gradient (300–3600 m.a.s.l.) in the Manú Biosphere Reserve, south-east Peru.
Methods
We focus on 215 frugivorous bird species along a Neotropical elevational gradient. We approximate species’ sensitivity to climate change by their climatic niche breadth, based on species occurrences across South America and bioclimatic variables. In addition, we use a trait-based approach to estimate the dispersal ability of species (approximated by their wing pointedness), their dietary niche breadth (approximated by bill width) and their habitat niche breadth (the number of used habitat classes).
Results
We found that (i) species’ climatic niche breadth increased with elevation, while their trait-based dispersal ability and dietary niche breadth decreased with elevation, and (ii) sensitivity to climate change and trait-based adaptive capacity were not related across species.
Main conclusions
These results suggest different mechanisms of how species in lowland and highland assemblages might respond to climate change. The independent variation of species’ sensitivity to climate change and their trait-based adaptive capacity suggests that accounting for both dimensions will improve assessments of species’ susceptibility to climate change and potential impacts of climate change on diverse species assemblages.
Aim: Recent studies in southern Africa identified past biome stability as an important predictor of biodiversity. We aimed to assess the extent to which past biome stability predicts present global biodiversity patterns, and the extent to which projected climatic changes may lead to eventual biome changes in areas with constant past biome.
Location: Global.
Taxon: Spermatophyta; terrestrial vertebrates.
Methods: Biome constancy was assessed and mapped using results from 89 dynamic global vegetation model simulations, driven by outputs of palaeoclimate experiments spanning the past 140 ka. We tested the hypothesis that terrestrial vertebrate diversity is predicted by biome constancy. We also simulated potential future vegetation, and hence potential future biome patterns, and quantified and mapped the extent of projected eventual future biome change in areas of past constant biome.
Results: Approximately 11% of global ice-free land had a constant biome since 140 ka. Apart from areas of constant Desert, many areas with constant biome support high species diversity. All terrestrial vertebrate groups show a strong positive relationship between biome constancy and vertebrate diversity in areas of greater diversity, but no relationship in less diverse areas. Climatic change projected by 2100 commits 46%–66% of global ice-free land, and 34%–52% of areas of past constant biome (excluding areas of constant Desert) to eventual biome change.
Main conclusions: Past biome stability strongly predicts vertebrate diversity in areas of higher diversity. Future climatic changes will lead to biome changes in many areas of past constant biome, with profound implications for biodiversity conservation. Some projected biome changes will result in substantial reductions in biospheric carbon sequestration and other ecosystem services.