Ecology

Tested animals
Altogether 73 individuals from seven species of subterranean rodents differing in body mass, phylogenetic relatedness, and sociality were studied (Table 1). All animals were adult non-breeders, or their breeding history was unknown in solitary species, but none of them showed signs of recent breeding, which may theoretically influence measured parameters. For the purpose of this study, we used the following taxa. African mole-rats (Bathyergidae): the social Ansell’s mole-rat Fukomys anselli (Burda, Zima, Scharff, Macholán & Kawalika 1999) occupies the miombo in a small area near Zambia’s capital Lusaka; another social species of the genus Fukomys is named here as Fukomys “Nsanje” because founders of the breeding colony were captured near town Nsanje in south Malawi. Although we used name Fukomys darlingi (Thomas 1895) for mole-rats from this population in previous studies (e.g.38,49), its taxonomic status is still not resolved; the social common mole-rat Cryptomys hottentotus hottentotus (Lesson, 1826) occurs in mesic and semi-arid regions of southern Africa; the solitary Cape dune mole-rat Bathyergus suillus (Schreber, 1782) inhabits sandy soils along the south-western coast of South Africa; and the solitary Cape mole-rat Georychus capensis (Pallas, 1778) occupies mesic areas of the South Africa50. In addition, we studied the social coruro Spalacopus cyanus (Molina, 1782) (Octodontidae) occupying various habitats in Chile51; and the solitary Upper Galilee Mountains blind mole rat Nannospalax galili (Nevo, Ivanitskaya & Beiles 2001) (Spalacidae) from Israel52. Further information about the species including number of individuals used in the study, their physiology and ecology is shown in Table 1.
All experiments were done on captive animals. Georychus capensis, C. hottentotus, and B. suillus, were captured about four months before the experiment, and kept in the animal facility at the University of Pretoria, South Africa (temperature: 23 °C; humidity: 40–60%, photoperiod: 12L:12D). The animals were housed in plastic boxes with wood shavings used as a bedding. Cryptomys hottentotus and G. capensis were fed with sweet potatoes; B. suillus with sweet potatoes, carrots, and fresh grass. Fukomys anselli, F. “Nsanje”, N. galili, and S. cyanus were kept for at least three years in captivity (or born in captivity) before the experiment in the animal facility at the University of South Bohemia in České Budějovice, Czech Republic (temperature: African mole-rats 25 °C, N. galili and S. cyanus 23 °C; humidity: 40–50%, photoperiod: 12L:12D). The animals were kept in terraria with peat as a substrate and fed with carrots, potatoes, sweet potatoes, beetroot, apple, and rodent dry food mix ad libitum.
Experimental design
We measured Tb and Ts in all species at six Tas (10, 15, 20, 25, 30 and 35 °C). Each individual of all species was measured only once in each Ta. Measurements were conducted in temperature controlled experimental rooms in České Budějovice and Pretoria. Each animal was tested on two experimental days.
The animals were placed in the experimental room individually in plastic buckets with wood shavings as bedding. On the first day, the experimental procedure started at Ta 25 °C. They spent 60 min of initial habituation in the first Ta after which Tb and Ts were measured as described in the following paragraphs. The Ta was then increased to 30 °C and 35 °C, respectively. After the experimental room reached the focal Ta, the animals were left minimally 30 min in each Ta to acclimate, and the measurements were repeated. Considering their relatively small body size, tested animals were very likely in thermal equilibrium after this period because mammals of a comparable body mass are thermally equilibrated after similar period of acclimation53,54,55,56. On the second day, the procedure was repeated with the initial Ta 20 °C and decreasing to 15 °C and 10 °C, respectively. The time span between the measurements of the same individual in different Ta was at least 150 min. Between experimental days, the animals were kept at 25 °C in the experimental room (individuals of social species were housed together with their family members).
Body temperature measurements
We used two sets of equipment to measure animal Tb and Ts. In B. suillus, G. capensis, and C. hottentotus, Tb was measured by intraperitoneally injected PIT tags ( More

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Sample collection
Algae were sampled from August 27th to September 21st (2017) from seven populations also collected for Bonthond et al. [28], including three native populations; Akkeshi (Japan), Soukanzan (Japan), Rongcheng (China); and four non-native populations; Pleudihen-sur-Rance (France), Nordstrand (Germany), Cape Charles Beach (Viriginia) and Tomales Bay (California, Fig. 1, Table S1). Individuals fixed to hard substratum (see [30]) were sampled at least a meter apart from one another and stored in separate plastic bags. As A. vermiculophyllum has a complex, haplodiplontic life-cycle only diploids were included in the experiment. Life-cycle stages were identified in the field with a dissecting microscope or post-hoc by microsatellite genotyping [31]. After transport in coolers and storage at 4 °C in the lab, bags with algae were shipped to Germany, arriving within 4–6 days after collection. In the climate room (15 °C), individuals were transferred to separate transparent aquaria with transparent lids, containing 1.75 L artificial seawater (ASW) prepared from tap water and 24 gL−1 artificial sea salt without CaCO3 (high CaCO3 concentrations increase disease risk, Weinberger data unpublished) and exposed to 12 h of light per day (86.0 µmol m−2s−1 at the water surface). Aquaria were moderately aerated with aeration stones. Per population, four diploid individuals were acclimated over 31–32 days to climate room conditions prior to starting the experiment. Water was exchanged weekly with new ASW enriched with 2 mL Provasoli-Enrichment Solution (PES; [32]). At the start of the experiment, wet weight was recorded and individuals were divided into two parts of ~10 g each and placed into two plastic tanks with 1.75 L water and 2 mL PES (Fig. 1).
Fig. 1: Schematic overview of the sampling design and experimental process.

Algae were collected from native populations Rongcheng (ron), Soukanzan (sou) and Akkeshi (akk) and non-native populations Tomales Bay (tmb), Cape Charles Beach (ccb), Pleudihen-sur-Rance (fdm) and Nordstrand (nor). In the climate room algae were acclimated for 5 weeks and divided into two thalli. One of the thalli was treated for three days with an antibiotic mixture after which both groups were monitored for six weeks, during which the treated algae received inoculum with each water change. Microbiota samples were taken in the field (tfield), directly after disturbance (t0) and after 1, 2, 4 and 6 weeks (t1, t2, t4 and t6).

Full size image

Experimental setup
To rigorously disturb the microbial community, one of each of the pairs of aquaria containing the same algal individual was treated with a combination of antibiotics, aiming to increase the effectivity (10 mgL−1 ampicillin, 10 mgL−1 streptomycin, 10 mgL−1 chloramphenicol) and the other (control) remained untreated. All experimental work was conducted with disposable gloves and sterilized equipment, to minimize contamination. After three days, the water was removed from all tanks (treated and control) and the wet weight was recorded for all algae. All individuals were rinsed with one 1.75 L volume ASW and re-incubated in 1.75 L ASW. Subsequently, both groups received new ASW with 2 mL PES weekly and individuals treated with antibiotics received also 2 mL inoculum. The inoculum was prepared from individuals of all 7 populations, following the procedure to remove epibiota as described in Bonthond et al. [28]. Briefly, apical fragments of 1 g were separated from the thallus and transferred to 50 mL tubes containing 15 ± 1 glass beads (3 mm) and 15 mL ASW and vortexed for 6 min to separate epibiota from the algal tissue. In total, 8 samples were prepared from one individual per population. The resulting suspensions were pooled and mixed with glycerol (20% final glycerol concentration), aliquoted in 50 mL tubes and stored at −20 °C. For each water exchange, a new aliquot was defrosted at room temperature and added to the water of treated algae. Wet weight was recorded weekly with water exchanges. Before weighing the individual on aluminum foil, it was dipped twice on a separate aluminum foil sheet, to reduce attached water in a systematic way. Endo- and epiphytic microbiota were sampled in the field (tfield, [28]), at the start of the experiment (t0), after one week (t1), two weeks (t2), four weeks (t4) and six weeks (t6, Fig. 1). To equalize acclimation times across populations the experiment was stacked into five groups (Table S2). At each sampling moment, 0.5 or 1 g of tissue was separated from all individuals with sterilized forceps and epibiota were extracted similarly to the preparation of the inoculum. The resulting suspension was filtered through 0.2 µm pore size PCTA filters. Both the filters and the remaining tissue were preserved at −20 °C.
DNA extraction and amplicon sequencing
Tissue samples were defrosted, rinsed with absolute ethanol and DNA free water to remove hydro- and moderately lipophilic cells and molecules from the surface and cut to fragments with sterilized scissors. DNA was then extracted from these fragments (endobiota) and from preserved filters (epibiota) using the ZYMO Fecal/soil microbe kit (D6102; ZYMO-Research, Irvine, CA, USA), following the manufacturer’s protocol. Although this method to separate endo- and epibiota was shown to resolve distinct communities [28], tightly attached epiphytic cells may not be completely removed from the surface and detectable in endophytic samples as well. Two 16S-V4 amplicon libraries, over which the samples were divided in a balanced manner, were prepared as in Bonthond et al. [28], following the two-step PCR strategy from Gohl et al. [33], using the same set of 16S-V4 target primers and indexing primers. The libraries were sequenced on the Illumina MiSeq platform (2×300 PE) at the Max-Planck-Institute for Evolutionary Biology (Plön, Germany), including four negative DNA extraction controls and four negative and positive PCR controls (mock communities; ZYMO-D6311). The fastq files were de-multiplexed (0 mismatches). Relevant field samples from Bonthond et al. [28] were combined with the new dataset and assembled, quality filtered and classified altogether with Mothur v1.43.0 [34] using the SILVA-alignment release 132 [35]. Sequences were clustered within 3% dissimilarity into OTUs using the opticlust algorithm. Mitochondrial, chloroplast, eukaryotic and unclassified sequences were removed. To prepare the community matrix we discarded singleton OTUs (in the full dataset), samples with More

Department of Geography, Texas A&M University, College Station, TX, USA
J. Loisel

Department of Geography, University of Exeter, Exeter, UK
A. V. Gallego-Sala, M. J. Amesbury, D. J. Charman & T. P. Roland

Ecosystems and Environment Research Programme, University of Helsinki, Helsinki, Finland
M. J. Amesbury, A. Korhola, M. Väliranta, S. Juutinen, K. Minkkinen & S. Piilo

Department of Geography and Geotop Research Center, University of Quebec at Montreal, Montreal, Quebec, Canada
G. Magnan & M. Garneau

Magister of Environment and Soil Science Department, Tanjungpura University, Pontianak, Indonesia
G. Anshari

Department of Geography and Environment, University of Hawaii at Manoa, Honolulu, HI, USA
D. W. Beilman

Department of Ecology and Territory, Pontificial Xavierian University, Bogota, Colombia
J. C. Benavides

Organic Geochemistry Unit, School of Chemistry, and School of Earth Sciences, University of Bristol, Bristol, UK
J. Blewett & B. D. A. Naafs

Environmental Studies Program and Earth and Oceanographic Science Department, Bowdoin College, Brunswick, ME, USA
P. Camill

Department of Geology, Chulalongkorn University, Bangkok, Thailand
S. Chawchai

Department of Geography, University of California, Los Angeles, Los Angeles, CA, USA
A. Hedgpeth

Max Planck Institute for Meteorology, Hamburg, Germany
T. Kleinen & V. Brovkin

Faculty of Engineering, Chemical and Environmental Engineering, University of Nottingham, Nottingham, UK
D. Large

Centro de Investigación GAIA Antártica, University of Magallanes, Punta Arenas, Chile
C. A. Mansilla

Climate and Environmental Physics, Physics Institute and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
J. Müller & F. Joos

Consortium Érudit, Université de Montréal, Montreal, Quebec, Canada
S. van Bellen

Department of Ecology and Conservation Biology, Texas A&M University, College Station, TX, USA
J. B. West

Department of Earth and Environmental Sciences, Lehigh University, Bethlehem, PA, USA
Z. Yu

Institute for Peat and Mire Research, School of Geographical Sciences, Northeast Normal University, Changchun, China
Z. Yu

Department of Environmental Studies, Mount Holyoke College, South Hadley, MA, USA
J. L. Bubier

Department of Geography, McGill University, Montreal, Quebec, Canada
T. Moore

Department of Physical Geography, Stockholm University, Stockholm, Sweden
A. B. K. Sannel

School of Geography, Geology and the Environment, University of Leicester, Leicester, UK
S. Page

Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium
M. Bechtold & W. Swinnen

School of Geography & Sustainable Development, University of St Andrews, St Andrews, UK
L. E. S. Cole

Department of Earth, Ocean & Atmospheric Science, Florida State University, Tallahassee, FL, USA
J. P. Chanton

Department of Bioscience, Aarhus University, Roskilde, Denmark
T. R. Christensen

Department of Earth Sciences, University of Toronto, Toronto, Ontario, Canada
M. A. Davies & S. A. Finkelstein

Instituto Franco-Argentino para el Estudio del Clima y sus Impactos, Buenos Aires, Argentina
F. De Vleeschouwer

Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA
S. Frolking & C. Treat

Department of Geobotany and Plant Ecology, University of Lodz, Lodz, Poland
M. Gałka

Laboratoire d’Ecologie Fonctionnelle et Environnement, UMR 5245, CNRS-UPS-INPT, Toulouse, France
L. Gandois

Cranfield Soil and Agrifood Institute, Cranfield University, Cranfield, UK
N. Girkin

Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada
L. I. Harris

Stockholm Environment Institute, University of York, York, UK
A. Heinemeyer

Max Planck Institute for Biogeochemistry, Jena, Germany
A. M. Hoyt

Lawrence Berkeley National Laboratory, Berkeley, CA, USA
A. M. Hoyt

Florence Bascom Geoscience Center, United States Geological Survey, Reston, VA, USA
M. C. Jones

Department of Marine and Coastal Environmental Science, Texas A&M University at Galveston, Galveston, TX, USA
K. Kaiser

Department of Biology, University of Victoria, Victoria, British Columbia, Canada
T. Lacourse

Faculty of Geographical and Geological Sciences, Climate Change Ecology Research Unit, Adam Mickiewicz University, Poznań, Poland
M. Lamentowicz

Natural Resources Institute Finland (Luke), Helsinki, Finland
T. Larmola

Agroscope, Zurich, Switzerland
J. Leifeld

Institute for Atmospheric and Earth System Research, University of Helsinki, Helsinki, Finland
A. Lohila

Finnish Meteorological Institute, Climate System Research, Helsinki, Finland
A. Lohila

Department of Geography, Royal Holloway, University of London, Egham, UK
A. M. Milner

Department of Forest Sciences, University of Helsinki, Helsinki, Finland
K. Minkkinen

School of Earth and Environmental Sciences, University of Queensland, Brisbane, Queensland, Australia
P. Moss

Lamont-Doherty Earth Observatory, Palisades, NY, USA
J. Nichols

National Park Service, Washington DC, WA, USA
J. O’Donnell

Department of Environment & Geography, University of York, York, UK
R. Payne

Department of Chemistry, and Department of Geological and Environmental Science, Hope College, Holland, MI, USA
M. Philben

Department of Geography and Environmental Science, University of Reading, Reading, UK
A. Quillet

Department of Applied Earth Sciences, Uva Wellassa University, Badulla, Sri Lanka
A. S. Ratnayake

School of Biosciences, University of Nottingham, Nottingham, UK
S. Sjögersten

Département de Géographie, Université de Montréal, Montréal, Québec, Canada
O. Sonnentag & J. Talbot

Geography, School of Natural and Built Environment, Queen’s University Belfast, Belfast, UK
G. T. Swindles

Department of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, CA, USA
A. C. Valach

Department of Environment and Sustainability, Grenfell Campus, Memorial University, Corner Brook, Newfoundland, Canada
J. Wu More

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The UN goals to provide jobs and universal education are under threat.Credit: Manish Swarup/AP/Shutterstock

In October, United Nations secretary-general António Guterres made a series of key appointments. He tasked 15 scientists from around the world with providing policymakers with evidence, as well as their thoughts, on the Sustainable Development Goals (SDGs).
This time last year, the UN’s flagship plan to end poverty and guide the world to environmental sustainability by 2030 was already off track. Since then, the pandemic has reversed most of the achievements made in the five years since countries adopted the goals.
The World Food Programme estimates that 270 million people are now at risk of starvation: double the number before the pandemic. And school closures resulting from lockdowns have set back one of the few SDGs that were within reach before the pandemic — the goal to achieve universal primary education. In December, the UN’s science and cultural organization UNESCO estimated that some 320 million children were out of school, an increase of 90 million in just one month.

In the 3 months from 1 April last year, working hours equivalent to 495 million full-time jobs were lost to lockdowns around the world, according to the International Labour Organization. And in October, the International Monetary Fund projected that the world economy would contract by more than 4% by the end of 2020, a decrease on a scale not seen in generations.
This is the situation facing the researchers whom Guterres has tasked with researching and writing the second UN Global Sustainable Development Report (GSDR) — the first was published in 2019. They have been drawn from all over the world and span a range of disciplines, including climate change, ecology, environmental economics, ethics, health policy, infectious diseases, oceanography, the governance of international organizations and the study of science and development.
For this editorial, Nature spoke to individual researchers, government and UN officials, and campaigners from high- and low-income countries. Our advice for the report’s authors and for the UN — considering the state of the pandemic and the halting progress made towards the goals so far — is twofold. First, the authors need to work fast — faster than the three-year timeline they have been allocated. Second, they must reach out beyond their usual expert networks as early as possible in the evidence-gathering process and, in particular, look for innovative ways to involve under-represented communities.

The GSDR’s three-year timetable from commissioning to publication is excessive, considering the urgent need for advice on achieving the SDGs. One way to a shorter timetable is for the UN to commit to releasing an interim or work-in-progress document before the end of this year. That could then be circulated and feedback could be gathered by governments, UN agencies and the many organizations involved in implementing the goals, and this input could be incorporated into an amended second draft.
Producing the document in such a way would generate and maintain interest and momentum, but also provide a means of ensuring greater inclusion. Making the process inclusive is as important as the final outcome. Worldwide, there are many thousands of organizations — including those focused on research and education, companies and civil-society groups — that have volunteered to create their own plans for achieving the SDGs and which are themselves trying to evaluate the pandemic’s impact on their plans. An interim report would allow them to provide feedback. This should not be difficult to organize: the pandemic has shown how easy it is to have video meetings with people from around the world.
The research team will be reporting to the UN Department of Economic and Social Affairs, based in New York City, which has responsibility for tracking the progress of the SDGs and managing the GSDR. But it is essential that the team also works closely with the individual UN agencies that have responsibility for particular SDGs.

The importance of this partnership between research and action cannot be overstated. At present, UN organizations such as the children’s charity UNICEF and the World Food Programme are operating in emergency mode. Research often suffers when budgets are stretched and personnel have to be redeployed — in this case to more pandemic-facing roles. But these organizations still need research. They still need to be able to draw on people who have the time to think and gather evidence; people with the time to reflect on that knowledge before providing advice and answering questions from their colleagues on the frontline, and from policymakers and colleagues in other roles.
Such hands-on research will not be for the GSDR authors to do, but they could help UN agencies and countries to think about how to meet their research needs during the pandemic. Researchers need to test different strategies to help children whose families lack access to smartphones, laptops and broadband. They need to study the effect the pandemic is having on health systems. And, as governments rush to revive economic growth, there is a mountain of research to be done on the pandemic’s economic impact and on how to make recovery as green as possible. The SDGs will not be met unless research can shine a light on these and other issues.
The UN and its science advisers — on the SDGs especially — need to work at speed, and involve under-represented communities, all of which will require extra resources, including more people and more funding. Without this, it’s not realistic to expect them to work differently. But business as usual is not an option. Continued research will be needed to support action to end the current crisis and get onto a pathway to greater well-being and, eventually, prosperity and environmental sustainability. The UN’s science advisers have been given a bigger responsibility than many are ever likely to face. Everyone must be ready to work with them and help them succeed. More

Classification of dietary preferences and habitats for bird species in Japan via literature
We reviewed the food habits of 633 native avian species listed in the Check-list of Japanese Birds, 7th Revised Edition39 in attempting to represent the whole avian fauna of Japan. Nine ecological traits related to distribution, habitat and diet are listed in our database along with references as shown below (Table 1): (1) the distribution and breeding status in each region of Japan (Fig. 1), (2) the endemicity in Japan (Endemic, or −: not endemic to Japan)39, (3) the species status in the Red List of Threatened Species of Japan, (4) main habitat (Terrestrial, Freshwater, and/or Marine, or Unknown), (5) dietary categories (I: carnivore, II: herbivore, IV: omnivore, or Unknown; Fig. 2), (6) main diet(s) (I: some animals, II: some plants, I-i: fishes, I-ii: vertebrates, I-iii: arthropods, I-iv: molluscs, I-v: unknown or other animals, II-fr: plants [fruits and/or seeds], and/or III: scavenger, or Unknown; Fig. 2), (7) all recorded food habits (I-i, I-ii, I-iii, I-iv, I-v, II-fr, II-le: plants [leaves and/or others], or III; Fig. 2), (8) molluscs as avian food resources (iv-t: terrestrial molluscs, iv-f: freshwater molluscs, iv-mg: marine gastropods, iv-mb: marine bivalves, iv-mc: marine cephalopods, or iv-o: others or unknown molluscs; Fig. 2), (9) descriptions of molluscan prey in literature, and (10) referenced bibliographies.
Table 1 Description of each variable, and factor levels.
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Fig. 1

Seven categories of distribution area in this study.

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Fig. 2

The categories of preferred foods in this study. Food preferences were first categorized into four big groups (I. carnivore, II. herbivore, III. scavenger and IV. omnivore), and two of them (I and II) were further separated. In particular, molluscs were classified in detail.

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To keep our findings relevant, we reviewed the validity of species binomial names listed in our database and provide updates reflective of current taxonomic knowledge in 2020. A review was conducted using the Birds of the World online research database36 and apparent updates to binomials were cross-referenced using the International Union for Conservation of Nature’s Red List of Threatened Species (https://www.iucnredlist.org). The updated binomial information is included in Online-only Table 1.
We roughly categorised seven regions for avian distribution in the Japanese archipelago (Hok: Hokkaido Island and/or surrounding islands, Hon: Honshu Island and/or surrounding islands, Shi: Shikoku Island and/or surrounding islands, Kyu: Kyushu Island and/or surrounding islands, Ryu: Ryukyu archipelago, Izu: Izu islands, Oga: Ogasawara islands; Fig. 1), and classified six categories for residency in each region of Japan (RB: resident breeder, MB: migrant breeder, WV: winter visitor, PV: passage visitor, FB: former breeder, or −: not distributed, rare, or unknown) based on the Check-list of Japanese Birds, 7th Revised Edition39, and added seven categories for the species status in Japan based on the 2020, 4th Version of the Japanese Red Lists (EX: extinct, CR: critically endangered, EN: endangered, VU: vulnerable, NT: near threatened, DD: data deficient, or −: common species or not listed)5. To determine each species’ main diet, we primarily focused on literature describing “preferred” or “main” food habits, although we also utilized information about the frequency of target foods in crop and gizzard contents. The taxonomies of molluscan prey written in the database were mainly based on MolluscaBase (http://www.molluscabase.org), the online database of world mollusc classifications. While our database does not contain perfect information on distribution, residency, and conservation status in terms of current knowledge, we believe it represents a high degree of accuracy and usefulness in pulling together comprehensive information from different sources.
The diet data in this study was collected from 165 scientific articles and books including dietary information on Japanese birds. We searched for the following two series of keywords in Google Scholar for each bird species: {“scientific name” AND [“food habits” OR “diet” OR “food habits (in Japanese)” OR “crop and gizzard contents (in Japanese)”]} and {“standard Japanese name (in Japanese)” AND [“food habits” OR “diet” OR “food habits (in Japanese)” OR “crop and gizzard contents (in Japanese)”]}. Keyword searching and browsing was conducted between 2nd May and 27th December in 2017, and the top one-hundred and all results for each series of keywords was checked, respectively. Moreover, we manually reviewed additional several literatures and books as possible. These included publications in English and Japanese and were published between 1913 and 2018. All 165 references citing the food habits for each bird species are recorded in the database, and listed on the reference list in Zenodo40.
Land snails detected from the crop and gizzard of two bird species in Hokkaido, Japan
Crop and gizzard samples were obtained from two juvenile Oriental Turtle-Doves (Streptopelia orientalis; Columbidae, Columbiformes; Fig. 3A) and one juvenile Hazel Grouse (Tetrastes bonasia; Phasianidae, Galliformes; Fig. 3B). An individual T. bonasia was hunted at Ubaranai site no. 1 (Abashiri City, Hokkaido, Japan; N 43.9678°, E 144.0414°) on 7 November 2013, and two S. orientalis were shot at Ubaranai site no. 2 (Abashiri City, Hokkaido, Japan; N 43.9261°, E 144.0406°) on 28 October 2016. These birds were shot by a professional hunter for food and stored in a freezer; we then received them from the hunter and carefully extracted the crop and gizzard contents. Crop and gizzard contents of T. bonasia were identified from a photograph, while those of S. orientalis were identified directly from samples. In addition, the combined weight of crop and gizzard contents were measured for both S. orientalis individuals using an electronic scale (wet and dry weights for one, and dry weight only for the other; Online-only Table 2). The data collected from these samples is also included in our database.
Fig. 3

(A,B) Two bird species investigated in this study, Streptopelia orientalis (A), and Tetrastes bonasia (B). (C–H) The prey items detected from avian crops and gizzards of S. orientalis, (C) Cochlicopa lubrica (Cochlicopidae, Stylommatophora), (D) Discus pauper (Discidae, Stylommatophora), (E) Karaftohelix (Ezohelix) gainesi (Camaenidae, Stylommatophora), (F) Parakaliella affinis (Helicarionidae, Stylommatophora), (G) Persicaria thunbergii (Polygonaceae, Caryophyllales), and (H) Schizopepon bryoniifolius (Cucurbitaceae, Cucurbitales). I. The photograph of crop and gizzard contents of T. bonasia.

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