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      Echolocating bats prefer a high risk-high gain foraging strategy to increase prey profitability

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          Abstract

          Predators that target multiple prey types are predicted to switch foraging modes according to prey profitability to increase energy returns in dynamic environments. Here, we use bat-borne tags and DNA metabarcoding of feces to test the hypothesis that greater mouse-eared bats make immediate foraging decisions based on prey profitability and changes in the environment. We show that these bats use two foraging strategies with similar average nightly captures of 25 small, aerial insects and 29 large, ground-dwelling insects per bat, but with much higher capture success in the air (76%) vs ground (30%). However, owing to the 3–20 times larger ground prey, 85% of the nightly food acquisition comes from ground prey despite the 2.5 times higher failure rates. We find that most bats use the same foraging strategy on a given night suggesting that bats adapt their hunting behavior to weather and ground conditions. We conclude that these bats use high risk-high gain gleaning of ground prey as a primary foraging tactic, but switch to aerial hunting when environmental changes reduce the profitability of ground prey, showing that prey switching matched to environmental dynamics plays a key role in covering the energy intake even in specialized predators.

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          Bats are the only mammals capable of powered flight and therefore need a high calorie intake to survive. They hunt at night using the echoes made by their own calls to navigate and locate prey.

          Bats can use different tactics to hunt for food: hawking involves catching prey on the wing and requires fast aerial manoeuvring and more intense echolocation calls, while gleaning involves listening for movements of ground and water dwelling prey as the bat hovers. Some bat species specialise as hawkers or gleaners but maintain the ability to hunt with both methods. With the ever-growing impact of human activities on their habitats, it is important to understand how adaptable bats feeding habits are to changes in their environment.

          To find out more, Stidsholt et al. studied greater mouse-eared bats, which primarily feed by gleaning. To understand how this species chooses feeding strategies they fitted bats with tiny backpacks that could record the animal’s location and foraging behaviour. They could also monitor prey sizes by recording the sounds of the bats chewing.

          Stidsholt et al. found that, although these bats tried to catch prey on the ground more often than in the air, they were actually more successful as airborne hunters. Despite this, gleaning was still a more profitable strategy for them, because the body mass of ground prey is higher than for airborne prey. Gleaning gave the bats a higher calorie intake, even though their capture rate was lower.

          Although feeding habits differed slightly between individual bats on a given night of monitoring, there were much larger changes in behaviour between different feeding nights. This shows that, although this species of bat prefers gleaning, they will switch strategies to hawking as their environment changes, for example if there is more airborne prey, or if rainfall makes it hard to hear movements on the ground. Bats tended to get enough calories for their needs but did not catch more prey than they needed to survive.

          Stidsholt et al. concluded that greater mouse-eared bats change their feeding strategy based on prey availability and size, as well as the bat’s environment. Their study provides an important insight into how bats fit into the ecosystem and how adaptable bats might be to changes in their habitat.

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          Most cited references49

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          Fitting Linear Mixed-Effects Models Usinglme4

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            Cutadapt removes adapter sequences from high-throughput sequencing reads

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              VSEARCH: a versatile open source tool for metagenomics

              Background VSEARCH is an open source and free of charge multithreaded 64-bit tool for processing and preparing metagenomics, genomics and population genomics nucleotide sequence data. It is designed as an alternative to the widely used USEARCH tool (Edgar, 2010) for which the source code is not publicly available, algorithm details are only rudimentarily described, and only a memory-confined 32-bit version is freely available for academic use. Methods When searching nucleotide sequences, VSEARCH uses a fast heuristic based on words shared by the query and target sequences in order to quickly identify similar sequences, a similar strategy is probably used in USEARCH. VSEARCH then performs optimal global sequence alignment of the query against potential target sequences, using full dynamic programming instead of the seed-and-extend heuristic used by USEARCH. Pairwise alignments are computed in parallel using vectorisation and multiple threads. Results VSEARCH includes most commands for analysing nucleotide sequences available in USEARCH version 7 and several of those available in USEARCH version 8, including searching (exact or based on global alignment), clustering by similarity (using length pre-sorting, abundance pre-sorting or a user-defined order), chimera detection (reference-based or de novo), dereplication (full length or prefix), pairwise alignment, reverse complementation, sorting, and subsampling. VSEARCH also includes commands for FASTQ file processing, i.e., format detection, filtering, read quality statistics, and merging of paired reads. Furthermore, VSEARCH extends functionality with several new commands and improvements, including shuffling, rereplication, masking of low-complexity sequences with the well-known DUST algorithm, a choice among different similarity definitions, and FASTQ file format conversion. VSEARCH is here shown to be more accurate than USEARCH when performing searching, clustering, chimera detection and subsampling, while on a par with USEARCH for paired-ends read merging. VSEARCH is slower than USEARCH when performing clustering and chimera detection, but significantly faster when performing paired-end reads merging and dereplication. VSEARCH is available at https://github.com/torognes/vsearch under either the BSD 2-clause license or the GNU General Public License version 3.0. Discussion VSEARCH has been shown to be a fast, accurate and full-fledged alternative to USEARCH. A free and open-source versatile tool for sequence analysis is now available to the metagenomics community.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                18 April 2023
                2023
                : 12
                : e84190
                Affiliations
                [1 ] Zoophysiology, Department of Bioscience, Aarhus University ( https://ror.org/01aj84f44) Aarhus Denmark
                [2 ] Acoustic and Functional Ecology, Max Planck Institute for Biological Intelligence Seewiesen Germany
                [3 ] Department of Animal Diversity and Resources, Institute of Biodiversity and Ecosystem Research, Bulgarian Academy of Sciences ( https://ror.org/01x8hew03) Sofia Bulgaria
                [4 ] Department of Zoology, Tel Aviv University ( https://ror.org/04mhzgx49) Tel Aviv Israel
                National Institute of Polar Research ( https://ror.org/05k6m5t95) Japan
                Max Planck Institute for Biology Tübingen ( https://ror.org/0243gzr89) Germany
                National Institute of Polar Research ( https://ror.org/05k6m5t95) Japan
                National Institute of Polar Research ( https://ror.org/05k6m5t95) Japan
                National Institute of Polar Research ( https://ror.org/05k6m5t95) Japan
                University of Western Ontario ( https://ror.org/02grkyz14) Canada
                Author information
                https://orcid.org/0000-0002-2187-7835
                https://orcid.org/0000-0001-8362-1301
                https://orcid.org/0000-0002-5208-5259
                Article
                84190
                10.7554/eLife.84190
                10112884
                37070239
                502be2f0-b6e0-4b23-a044-a9cb34fb01a0
                © 2023, Stidsholt et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 13 October 2022
                : 08 March 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100008398, Villum Fonden;
                Award ID: 41386
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100005995, Bulgarian Academy of Sciences;
                Award ID: DFNP-17-71/28.07.2017
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100002808, Carlsbergfondet;
                Award ID: Semper Ardens
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;
                Award ID: 241711556
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Ecology
                Custom metadata
                Greater mouse-eared bats prefer to hunt large ground insects despite high failure rates, but switch to smaller, easily caught flying insects in response to environmental changes.

                Life sciences
                biologging,bioacoustics,foraging behavior,prey profitability,prey switching,high risk-high gain strategy,other

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