Ecology

1.Pocock, M. J., Tweddle, J. C., Savage, J., Robinson, L. D. & Roy, H. E. The diversity and evolution of ecological and environmental citizen science. PLoS ONE 12, e0172579 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
2.Chandler, M. et al. Contribution of citizen science towards international biodiversity monitoring. Biol. Cons. 213, 280–294 (2017).Article 

Google Scholar 
3.Chandler, M. et al. Involving citizen scientists in biodiversity observation. In The GEO Handbook on Biodiversity Observation Networks 211–237 (Springer, 2017).4.McKinley, D. C. et al. Citizen science can improve conservation science, natural resource management, and environmental protection. Biol. Cons. 208, 15–28 (2017).Article 

Google Scholar 
5.Pereira, H. M. et al. Monitoring essential biodiversity variables at the species level. In The GEO Handbook on Biodiversity Observation Networks 79–105 (Springer, 2017).6.Wiggins, A. & Crowston, K. From conservation to crowdsourcing: A typology of citizen science. in 2011 44th Hawaii International Conference on System Sciences 1–10 (IEEE, 2011).7.Haklay, M. Citizen science and volunteered geographic information: Overview and typology of participation. In Crowdsourcing Geographic Knowledge 105–122 (Springer, 2013).8.Kelling, S. et al. Using semistructured surveys to improve citizen science data for monitoring biodiversity. Bioscience 69, 170–179 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
9.Welvaert, M. & Caley, P. Citizen surveillance for environmental monitoring: Combining the efforts of citizen science and crowdsourcing in a quantitative data framework. Springerplus 5, 1890 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
10.Callaghan, C. T., Rowley, J. J., Cornwell, W. K., Poore, A. G. & Major, R. E. Improving big citizen science data: Moving beyond haphazard sampling. PLoS Biol. 17, e3000357 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Bonter, D. N. & Cooper, C. B. Data validation in citizen science: A case study from project FeederWatch. Front. Ecol. Environ. 10, 305–307 (2012).Article 

Google Scholar 
12.Kosmala, M., Wiggins, A., Swanson, A. & Simmons, B. Assessing data quality in citizen science. Front. Ecol. Environ. 14, 551–560 (2016).Article 

Google Scholar 
13.Burgess, H. K. et al. The science of citizen science: Exploring barriers to use as a primary research tool. Biol. Cons. 208, 113–120 (2017).Article 

Google Scholar 
14.Courter, J. R., Johnson, R. J., Stuyck, C. M., Lang, B. A. & Kaiser, E. W. Weekend bias in citizen science data reporting: Implications for phenology studies. Int. J. Biometeorol. 57, 715–720 (2013).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Sullivan, B. L. et al. The eBird enterprise: An integrated approach to development and application of citizen science. Biol. Cons. 169, 31–40 (2014).Article 

Google Scholar 
16.Kelling, S. et al. Can observation skills of citizen scientists be estimated using species accumulation curves?. PLoS ONE 10, e0139600 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
17.Tiago, P., Ceia-Hasse, A., Marques, T. A., Capinha, C. & Pereira, H. M. Spatial distribution of citizen science casuistic observations for different taxonomic groups. Sci. Rep. 7, 1–9 (2017).CAS 
Article 

Google Scholar 
18.Geldmann, J. et al. What determines spatial bias in citizen science? Exploring four recording schemes with different proficiency requirements. Divers. Distrib. 22, 1139–1149 (2016).Article 

Google Scholar 
19.Callaghan, C. T. et al. Three frontiers for the future of biodiversity research using citizen science data. Bioscience 71, 55–63 (2021).
Google Scholar 
20.Ward, D. F. Understanding sampling and taxonomic biases recorded by citizen scientists. J. Insect Conserv. 18, 753–756 (2014).Article 

Google Scholar 
21.Troudet, J., Grandcolas, P., Blin, A., Vignes-Lebbe, R. & Legendre, F. Taxonomic bias in biodiversity data and societal preferences. Sci. Rep. 7, 1–14 (2017).CAS 
Article 

Google Scholar 
22.Martı́n-López, B., Montes, C., Ramı́rez, L. & Benayas, J. What drives policy decision-making related to species conservation? Biol. Conserv. 142, 1370–1380 (2009).23.Boakes, E. H. et al. Distorted views of biodiversity: Spatial and temporal bias in species occurrence data. PLoS Biol 8, e1000385 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
24.Aceves-Bueno, E. et al. The accuracy of citizen science data: A quantitative review. Bull. Ecol. Soc. Am. 98, 278–290 (2017).Article 

Google Scholar 
25.Davies, T. K., Stevens, G., Meekan, M. G., Struve, J. & Rowcliffe, J. M. Can citizen science monitor whale-shark aggregations? Investigating bias in mark–recapture modelling using identification photographs sourced from the public. Wildl. Res. 39, 696–704 (2013).Article 

Google Scholar 
26.Crall, A. W. et al. Assessing citizen science data quality: An invasive species case study. Conserv. Lett. 4, 433–442 (2011).Article 

Google Scholar 
27.van Strien, A. J., van Swaay, C. A. & Termaat, T. Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analysed with occupancy models. J. Appl. Ecol. 50, 1450–1458 (2013).Article 

Google Scholar 
28.Johnston, A., Moran, N., Musgrove, A., Fink, D. & Baillie, S. R. Estimating species distributions from spatially biased citizen science data. Ecol. Model. 422, 108927 (2020).Article 

Google Scholar 
29.Tiago, P., Pereira, H. M. & Capinha, C. Using citizen science data to estimate climatic niches and species distributions. Basic Appl. Ecol. 20, 75–85 (2017).Article 

Google Scholar 
30.Sullivan, B. L. et al. Using open access observational data for conservation action: A case study for birds. Biol. Cons. 208, 5–14 (2017).Article 

Google Scholar 
31.Callaghan, C. T. et al. Citizen science data accurately predicts expert-derived species richness at a continental scale when sampling thresholds are met. Biodivers. Conserv. 29, 1323–1337 (2020).Article 

Google Scholar 
32.Birkin, L. & Goulson, D. Using citizen science to monitor pollination services. Ecol. Entomol. 40, 3–11 (2015).Article 

Google Scholar 
33.Delaney, D. G., Sperling, C. D., Adams, C. S. & Leung, B. Marine invasive species: Validation of citizen science and implications for national monitoring networks. Biol. Invasions 10, 117–128 (2008).Article 

Google Scholar 
34.Schultz, C. B., Brown, L. M., Pelton, E. & Crone, E. E. Citizen science monitoring demonstrates dramatic declines of monarch butterflies in western north america. Biol. Cons. 214, 343–346 (2017).Article 

Google Scholar 
35.Bird, T. J. et al. Statistical solutions for error and bias in global citizen science datasets. Biol. Cons. 173, 144–154 (2014).Article 

Google Scholar 
36.Isaac, N. J., van Strien, A. J., August, T. A., de Zeeuw, M. P. & Roy, D. B. Statistics for citizen science: Extracting signals of change from noisy ecological data. Methods Ecol. Evol. 5, 1052–1060 (2014).Article 

Google Scholar 
37.Dickinson, J. L. et al. The current state of citizen science as a tool for ecological research and public engagement. Front. Ecol. Environ. 10, 291–297 (2012).Article 

Google Scholar 
38.Bonney, R. et al. Next steps for citizen science. Science 343, 1436–1437 (2014).ADS 
PubMed 
Article 

Google Scholar 
39.Jordan, R. C., Gray, S. A., Howe, D. V., Brooks, W. R. & Ehrenfeld, J. G. Knowledge gain and behavioral change in citizen-science programs. Conserv. Biol. 25, 1148–1154 (2011).PubMed 
Article 

Google Scholar 
40.Crall, A. W. et al. The impacts of an invasive species citizen science training program on participant attitudes, behavior, and science literacy. Public Underst. Sci. 22, 745–764 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
41.Jordan, R. C., Ballard, H. L. & Phillips, T. B. Key issues and new approaches for evaluating citizen-science learning outcomes. Front. Ecol. Environ. 10, 307–309 (2012).Article 

Google Scholar 
42.Evans, C. et al. The neighborhood nestwatch program: Participant outcomes of a citizen-science ecological research project. Conserv. Biol. 19, 589–594 (2005).Article 

Google Scholar 
43.Haywood, B. K., Parrish, J. K. & Dolliver, J. Place-based and data-rich citizen science as a precursor for conservation action. Conserv. Biol. 30, 476–486 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Pocock, M. J. et al. A vision for global biodiversity monitoring with citizen science. In Advances in Ecological Research vol. 59, 169–223 (Elsevier, 2018).45.Tiago, P., Gouveia, M. J., Capinha, C., Santos-Reis, M. & Pereira, H. M. The influence of motivational factors on the frequency of participation in citizen science activities. Nat. Conserv. 18, 61 (2017).Article 

Google Scholar 
46.Isaac, N. J. & Pocock, M. J. Bias and information in biological records. Biol. J. Lin. Soc. 115, 522–531 (2015).Article 

Google Scholar 
47.Angulo, E. & Courchamp, F. Rare species are valued big time. PLoS ONE 4, e5215 (2009).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
48.Booth, J. E., Gaston, K. J., Evans, K. L. & Armsworth, P. R. The value of species rarity in biodiversity recreation: A birdwatching example. Biol. Cons. 144, 2728–2732 (2011).Article 

Google Scholar 
49.Rowley, J. J. et al. FrogID: Citizen scientists provide validated biodiversity data on frogs of australia. Herpetol. Conserv. Biol. 14, 155–170 (2019).
Google Scholar 
50.Boakes, E. H. et al. Patterns of contribution to citizen science biodiversity projects increase understanding of volunteers recording behaviour. Sci. Rep. 6, 33051 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Garrard, G. E., McCarthy, M. A., Williams, N. S., Bekessy, S. A. & Wintle, B. A. A general model of detectability using species traits. Methods Ecol. Evol. 4, 45–52 (2013).Article 

Google Scholar 
52.Denis, T. et al. Biological traits, rather than environment, shape detection curves of large vertebrates in neotropical rainforests. Ecol. Appl. 27, 1564–1577 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
53.Sólymos, P., Matsuoka, S. M., Stralberg, D., Barker, N. K. & Bayne, E. M. Phylogeny and species traits predict bird detectability. Ecography 41, 1595–1603 (2018).Article 

Google Scholar 
54.Wood, C., Sullivan, B., Iliff, M., Fink, D. & Kelling, S. eBird: Engaging birders in science and conservation. PLoS Biol 9, 1001220 (2011).Article 
CAS 

Google Scholar 
55.GBIF.org (3rd December 2019). GBIF occurrence download. https://doi.org/10.15468/dl.lpwczr56.Gilfedder, M. et al. Brokering trust in citizen science. Soc. Nat. Resour. 32, 292–302 (2019).Article 

Google Scholar 
57.Callaghan, C., Lyons, M., Martin, J., Major, R. & Kingsford, R. Assessing the reliability of avian biodiversity measures of urban greenspaces using eBird citizen science data. Avian Conserv. Ecol. 12, 66 (2017).
Google Scholar 
58.Johnston, A. et al. Best practices for making reliable inferences from citizen science data: Case study using eBird to estimate species distributions. BioRxiv 574392 (2019).59.Myhrvold, N. P. et al. An amniote life-history database to perform comparative analyses with birds, mammals, and reptiles: Ecological archives E096–269. Ecology 96, 3109–3109 (2015).Article 

Google Scholar 
60.Dale, J., Dey, C. J., Delhey, K., Kempenaers, B. & Valcu, M. The effects of life history and sexual selection on male and female plumage colouration. Nature 527, 367–370 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
61.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).62.Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2019).ADS 
Article 

Google Scholar 
63.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
64.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

Google Scholar 
65.Johnston, A. et al. Species traits explain variation in detectability of UK birds. Bird Study 61, 340–350 (2014).Article 

Google Scholar 
66.Steen, V. A., Elphick, C. S. & Tingley, M. W. An evaluation of stringent filtering to improve species distribution models from citizen science data. Divers. Distrib. 25, 1857–1869 (2019).Article 

Google Scholar 
67.Henckel, L., Bradter, U., Jönsson, M., Isaac, N. J. & Snäll, T. Assessing the usefulness of citizen science data for habitat suitability modelling: Opportunistic reporting versus sampling based on a systematic protocol. Divers. Distrib. 26, 1276–1290 (2020).Article 

Google Scholar 
68.Caley, P., Welvaert, M. & Barry, S. C. Crowd surveillance: Estimating citizen science reporting probabilities for insects of biosecurity concern. J. Pest. Sci. 93, 543–550 (2020).Article 

Google Scholar 
69.Périquet, S., Roxburgh, L., le Roux, A. & Collinson, W. J. Testing the value of citizen science for roadkill studies: A case study from South Africa. Front. Ecol. Evol. 6, 15 (2018).Article 

Google Scholar 
70.Nakagawa, S. & Freckleton, R. P. Model averaging, missing data and multiple imputation: A case study for behavioural ecology. Behav. Ecol. Sociobiol. 65, 103–116 (2011).Article 

Google Scholar 
71.Schlossberg, S., Chase, M. & Griffin, C. Using species traits to predict detectability of animals on aerial surveys. Ecol. Appl. 28, 106–118 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.Aristeidou, M., Scanlon, E. & Sharples, M. Profiles of engagement in online communities of citizen science participation. Comput. Hum. Behav. 74, 246–256 (2017).Article 

Google Scholar 
73.Troscianko, J., Skelhorn, J. & Stevens, M. Quantifying camouflage: How to predict detectability from appearance. BMC Evol. Biol. 17, 1–13 (2017).Article 

Google Scholar 
74.Schuetz, J. G. & Johnston, A. Characterizing the cultural niches of North American birds. Proc. Natl. Acad. Sci. 22, 10868–10873 (2019).Article 
CAS 

Google Scholar 
75.Lišková, S. & Frynta, D. What determines bird beauty in human eyes?. Anthrozoös 26, 27–41 (2013).Article 

Google Scholar 
76.Tulloch, A. I., Possingham, H. P., Joseph, L. N., Szabo, J. & Martin, T. G. Realising the full potential of citizen science monitoring programs. Biol. Cons. 165, 128–138 (2013).Article 

Google Scholar 
77.Kobori, H. et al. Citizen science: A new approach to advance ecology, education, and conservation. Ecol. Res. 31, 1–19 (2016).CAS 
Article 

Google Scholar 
78.Callaghan, C. T., Poore, A. G., Major, R. E., Rowley, J. J. & Cornwell, W. K. Optimizing future biodiversity sampling by citizen scientists. Proc. R. Soc. B 286, 20191487 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
79.Pacifici, K. et al. Integrating multiple data sources in species distribution modeling: A framework for data fusion. Ecology 98, 840–850 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
80.Robinson, O. J. et al. Integrating citizen science data with expert surveys increases accuracy and spatial extent of species distribution models. Divers. Distrib. 26, 976–986 (2020).Article 

Google Scholar 
81.van Strien, A. J., Termaat, T., Groenendijk, D., Mensing, V. & Kery, M. Site-occupancy models may offer new opportunities for dragonfly monitoring based on daily species lists. Basic Appl. Ecol. 11, 495–503 (2010).Article 

Google Scholar 
82.Van der Wal, R. et al. Mapping species distributions: A comparison of skilled naturalist and lay citizen science recording. Ambio 44, 584–600 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
83.Dennis, E. B., Morgan, B. J., Brereton, T. M., Roy, D. B. & Fox, R. Using citizen science butterfly counts to predict species population trends. Conserv. Biol. 31, 1350–1361 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
84.Stoudt, S., Goldstein, B. R. & De Valpine, P. Identifying charismatic bird species and traits with community science data. bioRxiv. https://doi.org/10.1101/2021.06.05.446577 More

NATURE INDEX
24 September 2021

Coral conservation strikes a balance

Australia–Fiji collaboration matches community needs with reef protection.

Clare Watson

0

Clare Watson

Clare Watson is a freelance writer in Wollongong, Australia.

View author publications

You can also search for this author in PubMed
 Google Scholar

Share on Twitter
Share on Twitter

Share on Facebook
Share on Facebook

Share via E-Mail
Share via E-Mail

Download PDF

A spear fisherman catches reef fish, a cultural mainstay on Mali Island in Fiji.Credit: Juergen Freund/naturepl.com

Coral reefs are under threat, and so too are the livelihoods of more 500 million people who depend on them. Global climate change is causing longer and more frequent marine heatwaves, leading to widespread and repeated coral bleaching. Overfishing and pollution exacerbate the problem, adding pressure to these marine biodiversity hotspots that sustain coastal communities.Reef-management programmes that limit or prohibit fishing and other commercial activities are bound to be ineffective if local communities are not involved in their design and management, says Sangeeta Mangubhai, a coral-reef ecologist in Fiji. “If people haven’t been engaged in the management [of conservation strategies], they’re not as likely to understand what the rules are, or they might not comply with it,” she says. Initiatives that are designed to protect coral reefs without incorporating insights from local communities may also affect them in unintended ways, she adds.
Nature Index 2021 Science cities
In collaboration with environmental social scientist, Georgina Gurney, Mangubhai is identifying the conditions that support both conservation outcomes and the wellbeing of coastal communities who often have cultural practices and spiritual ties to the sea. Their work explores the social factors that influence coral-reef-management programmes, such as the perceived fairness of payment schemes that direct tourism revenue back to the communities who manage local reefs (G. G. Gurney et al. Environ. Sci. Policy 124, 23–32; 2021).“First and foremost, it’s an ethical and moral issue,” says Gurney. “Conservation should not impinge on the wellbeing of people; it should promote the wellbeing of people.”Based at James Cook University (JCU) in Townsville, a city on the northeastern coast of Queensland, Australia, Gurney has close access to the Great Barrier Reef, which contains the world’s largest coral reef ecosystem. The university has long-standing ties with researchers in nearby Pacific island nations, such as Papua New Guinea, Fiji and New Caledonia.Townsville was the second most-prolific city in the 82 high-quality natural-sciences journals tracked by the Nature Index for research related to the United Nations’ Sustainable Development Goal (SDG) Life below water (SDG14) in 2015–20, with a Share of 15.59, 52% of which is attributed to JCU. Beijing, placed first by output related to SDG14, had a Share of 17.88 for the same period. (For more information on the analyses used in this article, see ‘A guide to Nature Index’.)

Georgina Gurney and Sangeeta Mangubhai at a fish market in Suva, Fiji.Credit: Isabelle Gurney

According to Gurney, successful conservation programmes should evaluate social factors alongside ecological outcomes, such as fish stocks and coral health, although this is rarely the case. With Mangubhai and other collaborators, Gurney has developed a framework that combines 90 social and ecological indicators, from coral cover and fish biomass to household incomes derived from the reef, equitable benefit-sharing and conflicts occurring over marine resources (G. G. Gurney et al. Biol. Conserv. 240, 108298; 2019).In principle, the framework standardizes how outcomes of coral-reef programmes are evaluated to improve data collection and enable cross-country comparisons. It has been adopted by the New York-based non-governmental organization, the Wildlife Conservation Society (WCF), and its partners in 7 countries and more than 130 communities across Africa, Asia and the Pacific.Besides improving conservation efforts, Mangubhai, who leads the WCF’s Fiji programme, says the partnership gives equal footing to local conservation scientists and policymakers, empowering them to direct independent research. “If you have these meaningful collaborations, the outcome is going to have so much more of an impact on the ground,” she says.Incorporating an understanding of the social factors that influence coral-reef conservation into marine-management strategies translates to respect for local traditional cultural practices of Indigenous Fijians, says Mangubhai. Temporary closures called tabu, which are used to maintain the productivity of their customary fishing grounds, are a good example. “It’s a real merging of traditional knowledge and other best practices, such as size limits on fish catch, to help communities achieve the outcomes they want for themselves,” she says.

doi: https://doi.org/10.1038/d41586-021-02409-6This article is part of Nature Index 2021 Science cities, an editorially independent supplement produced with the financial support of third parties. About this content.

Related Articles

Sustainable Development Goals research speaks to city strengths and priorities

Tracking 20 leading cities’ Sustainable Development Goals research

How cities are collaborating to safeguard oceans

Rising tide of floating plastics spurs surge in research

Uneven spread of research leaves poorer cities short of solutions

Pursuit of better batteries underpins China’s lead in energy research

How Belo Horizonte’s bid to tackle hunger inspired other cities

New York bids to level the playing field in a metropolis of inequality

Subjects

Conservation biology

Ocean sciences

Sustainability

Latest on:

Ocean sciences

How cities are collaborating to help safeguard oceans
Nature Index 24 SEP 21

Rising tide of floating plastics spurs surge in research
Nature Index 24 SEP 21

Widespread phytoplankton blooms triggered by 2019–2020 Australian wildfires
Article 15 SEP 21

Sustainability

Sustainable Development Goals research speaks to city strengths and priorities
Nature Index 24 SEP 21

Tracking 20 leading cities’ Sustainable Development Goals research
Nature Index 24 SEP 21

Pursuit of better batteries underpins China’s lead in energy research
Nature Index 24 SEP 21

Jobs

Postdoctoral Research Fellow in Bioinformatics and Genomics

Max Planck Institute for Molecular Biomedicine
Münster, Germany

Associate Professor (Tenure) or Professor (Tenure), Biomaterials

The University of British Columbia (UBC)
Vancouver, Canada

Postdoctoral Fellow in Functional Genomics/Glycomics

The University of British Columbia (UBC)
Vancouver, Canada

60048: Physicist, Statistician, theoretical Computer Scientist or similar (f/m/x) – Development of causal inference methods in the field causal Inference and machine learning as part of the EU project XAIDA

German Aerospace Center (DLR)
Jena, Germany More

NATURE INDEX
24 September 2021

Rising tide of floating plastics spurs surge in research

Strong government policies and research insights are essential to deliver on a pledge to clean up the sea.

Michael Eisenstein

0

Michael Eisenstein

Michael Eisenstein is a freelance writer in Philadelphia, Pennsylvania.

View author publications

You can also search for this author in PubMed
 Google Scholar

Share on Twitter
Share on Twitter

Share on Facebook
Share on Facebook

Share via E-Mail
Share via E-Mail

Download PDF

A jellyfish swims beneath a slick of floating plastic debris in the Indian Ocean near Sri Lanka.Credit: Alex Mustard/naturepl.com

Many stories have been written about the ‘Great Pacific garbage patch’, a name evoking a vast Sargasso Sea of plastic bottles and bags. But the reality is that much of this debris has been broken down into a murky suspension of ‘microplastics’ spanning an area three times the size of France.
Nature Index 2021 Science cities
These plastic flecks introduce long-lasting chemical pollution into marine and coastal ecosystems, says Daoji Li, an oceanographer at East China Normal University in Shanghai. In 2020, Li and his colleagues found that microplastic debris is highly concentrated in even the deepest underwater trenches (G. Peng et al. Water Res. 168, 115121; 2020). Staving off this influx of pollutants is a target of the United Nations’ Sustainable Development Goal (SDG) Life below water (SDG14), with its aim to “prevent and significantly reduce marine pollution of all kinds” by 2025.Between 4.8 million and 12.7 million tonnes of plastic waste entered the oceans in 2010, according to a study in Science, and those numbers are expected to increase dramatically by 2050 without improvements to waste-management infrastructure (J. Jambeck et al. Science 347, 768–771; 2015). Scientists in China, which is a major producer and importer of plastic waste, are taking the lead in amelioration. According to the 2021 UNESCO Science Report, floating plastic debris was the fastest-growing area of SDG-related research in 2012–19 (see ‘A buoyant field’). Publications from the Chinese mainland on the topic jumped from 7 in the period 2012–15 to 286 in 2016–19, placing it third by volume after the United States and United Kingdom. Much of this work has come from investigators in Beijing, the top-ranked city in the Nature Index for SDG14-related research. (For more information on the analyses used in this article, see ‘A guide to Nature Index’.)

Source: UNESCO

Li is sceptical that much can be done to eliminate existing plastic pollution. “But what we can do is stop them entering to the ocean,” he says. His team has developed a monitoring framework that outlines ‘gold-standard’ technologies and assays for detecting and quantifying microplastic contamination.Government action is essential to stem the flow of plastic debris. UNESCO reports that 127 countries have adopted legislation to regulate plastic bags. In 2020, China launched an ambitious effort to ban plastic bags nationwide by 2022 and cut single-use plastic in restaurants by one-third by 2025 — although the COVID-19 pandemic created a surge in demand for delivery that derailed this effort.Despite the many hurdles to overcome, Li feels positive about the future. “I am pretty confident that we could meet the target set for SDG14,” he says, “but when we realize those challenges, we should keep going.”

Source: UNESCO

doi: https://doi.org/10.1038/d41586-021-02408-7This article is part of Nature Index 2021 Science cities, an editorially independent supplement produced with the financial support of third parties. About this content.

Related Articles

Sustainable Development Goals research speaks to city strengths and priorities

Tracking 20 leading cities’ Sustainable Development Goals research

How cities are collaborating to safeguard oceans

Coral conservation strikes a balance

Uneven spread of research leaves poorer cities short of solutions

Pursuit of better batteries underpins China’s lead in energy research

How Belo Horizonte’s bid to tackle hunger inspired other cities

New York bids to level the playing field in a metropolis of inequality

Subjects

Conservation biology

Ocean sciences

Sustainability

Latest on:

Ocean sciences

Coral conservation strikes a balance
Nature Index 24 SEP 21

How cities are collaborating to help safeguard oceans
Nature Index 24 SEP 21

Widespread phytoplankton blooms triggered by 2019–2020 Australian wildfires
Article 15 SEP 21

Sustainability

Sustainable Development Goals research speaks to city strengths and priorities
Nature Index 24 SEP 21

Tracking 20 leading cities’ Sustainable Development Goals research
Nature Index 24 SEP 21

Coral conservation strikes a balance
Nature Index 24 SEP 21

Jobs

Postdoctoral Research Fellow in Bioinformatics and Genomics

Max Planck Institute for Molecular Biomedicine
Münster, Germany

Associate Professor (Tenure) or Professor (Tenure), Biomaterials

The University of British Columbia (UBC)
Vancouver, Canada

Postdoctoral Fellow in Functional Genomics/Glycomics

The University of British Columbia (UBC)
Vancouver, Canada

60048: Physicist, Statistician, theoretical Computer Scientist or similar (f/m/x) – Development of causal inference methods in the field causal Inference and machine learning as part of the EU project XAIDA

German Aerospace Center (DLR)
Jena, Germany More

NATURE INDEX
24 September 2021

How cities are collaborating to help safeguard oceans

Despite missed deadlines in 2020 for key targets in marine conservation, momentum for these Sustainable Development Goals is growing.

Michael Eisenstein

0

Michael Eisenstein

Michael Eisenstein is a freelance writer in Philadelphia, Pennsylvania.

View author publications

You can also search for this author in PubMed
 Google Scholar

Share on Twitter
Share on Twitter

Share on Facebook
Share on Facebook

Share via E-Mail
Share via E-Mail

Download PDF

Bart Shepherd, co-leader of the Hope for Reefs initiative, guides fish into a decompression chamber while on expedition in Vanuatu.Credit: Luiz Rocha/California Academy of Sciences

For about 30 minutes each year, vast colonies of corals in the waters of Palau, an island nation in the western Pacific, erupt in an almost perfectly synchronized mass-spawning event. Releasing buoyant packages of sperm and egg cells into the water to be fertilized by neighbouring colonies, these hermaphroditic species must make the most of rare opportunities to seed new life.In one of the world’s few indoor coral-culturing labs, Rebecca Albright and her team at the California Academy of Sciences in San Francisco are recreating the seasonal and lunar shifts that trigger such an event. The aim is to create multiple spawning systems that can be studied under controlled conditions. “Corals are notorious for being fickle animals to keep in captivity,” says Albright, a coral biologist and co-leader of Hope for Reefs, a global initiative to research and restore crucial coral-reef systems. “Most only sexually reproduce once a year, so you have to simulate all these environmental cues to elicit that.”
Nature Index 2021 Science cities
Strategies for cultivating and transplanting healthy corals into depleted areas are a crucial part of strengthening populations against what Albright describes as the “one-two punch effect” of climate change. Rising temperatures cause coral bleaching and death, while ocean acidification caused by increased levels of carbon dioxide makes corals less resilient and prevents regrowth. “If we are able to cap warming at 1.5  °C, we’re still going to lose 90% of reefs by 2050,” she says. “And if we edge towards 2 °C, we risk losing 97% to 99%.”Of the United Nations’ 17 Sustainable Development Goals (SDGs), Life below water (SDG14) and other SDGs related to environmental sustainability — Responsible consumption and production (SDG12), Climate action (SDG13) and Life on land (SDG15) — were the weakest in both donor funding and outcomes, attracting less than US$25 billion between them in 2000–13, according to the 2021 UNESCO Science Report (see go.nature.com/3zlojva). SDGs that are more directly related to economic growth — Industry, innovation and infrastructure (SDG9) and Sustainable cities and communities (SDG11) — by comparison, received $130 billion and $147 billion, respectively, over the same period.James Leape, co-director of Stanford University’s Center for Ocean Solutions in California, notes that four of the ten targets for SDG14, which aims to “conserve and sustainably use the oceans, seas and marine resources”, were due in 2020. All were missed. These include controlling the global damage wrought by illegal and unregulated fishing, which remains largely unchecked, and implementing scientifically grounded strategies for restoring affected fish stocks.But there are signs of momentum. The amount of ocean being conserved and managed within marine protected areas (MPAs), for example, has increased from 0.9% to 7.7% since 2000, says Leape. MPAs are regions in which fishing, mining and other activities are restricted. Efforts are under way to further expand the number of MPAs globally.Coastal collaborationsAs the world’s leading fishing nation, responsible for 15% of the reported global wild fish catch, China has ramped up efforts to designate new MPAs. Since 1980, China has designated more than 270 MPAs, comprising about 5% of its national waters. But it’s a long way off efforts by countries such as the United States, which has more than 1,000 MPAs that cover about 26% of its waters, and the United Kingdom, with 371 MPAs comprising 38% of its seas. In a 2019 Nature correspondence, fisheries researchers Yunzhou Li and Yiping Ren, from the Ocean University of China in Qingdao and Yong Chen from the University of Maine, Orono, say that effective monitoring and strict enforcement will also be essential to the success of China’s efforts (see Nature 573, 346; 2019).In a city-based analysis by the Nature Index, Beijing had the greatest output related to SDG14 in the 82 natural-sciences journals tracked by the index in 2015–20, with a Share of 17.88, followed by the coastal city of Townsville in northeastern Queensland, Australia (Share 15.59) and the Boston metropolitan area (Share 13.66). The San Francisco Bay Area, second only to Beijing in output related to all 17 SDGs, had the sixth-highest Share for SDG14 (13.24). (For more information on the analyses used in this article, see ‘A guide to Nature Index’.)

Residents in the coastal town of Maroantsetra, in northeastern Madagascar, display their catch.Credit: Rebecca Gaal

Many small island states face serious threats from the rapid decline of their coral reefs, which represent one of the world’s most diverse ecosystems. Gildas Todinanahary, a marine biologist at the Fisheries and Marine Science Institute at the University of Toliara in Madagascar, says the percentage of live coral cover surrounding the island nation has dropped from more than 80% in the 1980s to less than 10%, on average, today. “Decades ago, they used to say there will always be fish in the sea,” says Todinanahary. “Now they say there are no more fish.” This has jeopardized the livelihood of the fishing communities on the island’s western shore, he says.Christopher Golden, an ecologist and epidemiologist at the Harvard School of Public Health in Boston, is working with Todinanahary and his colleagues to deploy a series of small tiered platforms, designed to mimic the cracks and crevices of the reef, into healthy coral communities along the Madagascar coast. Once colonized, these structures are transported into degraded reefs in an effort to repopulate them. “If we can create a healthier reef, we can then rehabilitate some of the fish populations, and that will lead to improved fish-catch and greater access to seafood as a nutritional resource,” says Golden.Todinanahary is enthusiastic about the potential for seeding new reefs in barren coastal stretches, but says education and outreach to fishing communities will be key to ensuring that those restoration efforts endure. “It’s important to help communities change their habits and activities,” he says — for example, by providing training for alternative livelihoods such as aquaculture.Buy-in from community leaders is also crucial to the success of partnerships between researchers in leading science cities and colleagues in low- and middle-income maritime nations in SDG-related projects. In 2016, the government of Palau invited Leape and his team at Stanford to develop a strategy for turning 80% of its exclusive economic zone, a 370-km radius surrounding the island, into a protected area where fishing is prohibited. The initiative went into effect in January 2020. “We’re using satellite tracking to understand the patterns of use of the sanctuary by large pelagic species, and using DNA analysis to monitor biodiversity in the sanctuary,” says Leape. Palau’s programme has helped to motivate other island nations in the region to extend marine protection and conservation efforts as part of the Micronesia Challenge, an initiative to conserve 50% of marine resources and 30% of terrestrial resources by 2030.Golden’s research emphasizes both the sustainability and food-security sides of the fisheries-management coin, with routine health assessments of communities in places such as Madagascar and the Republic of Kiribati, an island nation in the central Pacific Ocean, coupled with close monitoring of the ecological health of their surrounding waters. To help this effort, Golden and his colleagues developed the Aquatic Food Composition Database, which compiles detailed nutritional information on more than 3,700 local plant and animal species to provide ecologically grounded guidance to local fishers. “We can look at what type of resilience there might be if we lose access to one species and have to focus on another,” says Golden. “We can understand the type of nourishment that people are actually getting from their catch.”Stanford’s Center for Ocean Solutions is also leveraging new technologies to guide sustainable fishing practices that benefit small-scale fishers, whose livelihood SDG14 aims to safeguard. “Their catches account for about two-thirds of the seafood we eat, and 90% of the fishery jobs,” says Leape. The centre is partnering with ABALOBI, an organization in South Africa founded by fisheries researcher Serge Raemaekers, from the University of Cape Town. ABALOBI has designed a mobile app toolbox to help fishers track specific fish populations, coordinate boats and crews, and bring catches to market. Leape is hopeful that early pilot testing in Africa and the Indian Ocean will pave the way for broader deployment in the near future.In parallel, Leape’s team is working on strategies to crack down on illegal fishing — currently estimated to account for roughly 20% of the global catch. This is being achieved partly through tools such as the satellite-based fishery monitoring efforts of Global Fishing Watch, a website run by Google in partnership with conservation non-profit organizations Oceana and SkyTruth. But technology is only part of the solution. Leape sees a crucial role for aggressive government enforcement and getting major corporations to engage in closer oversight of fishing practices. “We’ve been using Global Fishing Watch and other data sources to understand the patterns and areas for illegal fishing,” he says. “We’re working with these partners to try to translate that data into a more concerted effort to crack the problem.”

doi: https://doi.org/10.1038/d41586-021-02407-8This article is part of Nature Index 2021 Science cities, an editorially independent supplement produced with the financial support of third parties. About this content.

Related Articles

Sustainable Development Goals research speaks to city strengths and priorities

Tracking 20 leading cities’ Sustainable Development Goals research

Rising tide of floating plastics spurs surge in research

Coral conservation strikes a balance

Uneven spread of research leaves poorer cities short of solutions

Pursuit of better batteries underpins China’s lead in energy research

How Belo Horizonte’s bid to tackle hunger inspired other cities

New York bids to level the playing field in a metropolis of inequality

Subjects

Conservation biology

Ocean sciences

Sustainability

Climate change

Latest on:

Ocean sciences

Coral conservation strikes a balance
Nature Index 24 SEP 21

Rising tide of floating plastics spurs surge in research
Nature Index 24 SEP 21

Widespread phytoplankton blooms triggered by 2019–2020 Australian wildfires
Article 15 SEP 21

Sustainability

Sustainable Development Goals research speaks to city strengths and priorities
Nature Index 24 SEP 21

Tracking 20 leading cities’ Sustainable Development Goals research
Nature Index 24 SEP 21

Coral conservation strikes a balance
Nature Index 24 SEP 21

Jobs

Postdoctoral Research Fellow in Bioinformatics and Genomics

Max Planck Institute for Molecular Biomedicine
Münster, Germany

Associate Professor (Tenure) or Professor (Tenure), Biomaterials

The University of British Columbia (UBC)
Vancouver, Canada

Postdoctoral Fellow in Functional Genomics/Glycomics

The University of British Columbia (UBC)
Vancouver, Canada

60048: Physicist, Statistician, theoretical Computer Scientist or similar (f/m/x) – Development of causal inference methods in the field causal Inference and machine learning as part of the EU project XAIDA

German Aerospace Center (DLR)
Jena, Germany More

We developed environmental DNA (eDNA) detection protocols to assist in habitat identification for conservation for the US federally endangered Hine’s emerald dragonfly (Somatochlora hineana). Larval S. hineana have been observed in groundwater-fed calcareous fen habitats in Illinois, Wisconsin, Michigan, and Missouri in the USA, and Ontario, Canada. Habitat destruction and fragmentation have been the primary cause of S. hineana population decline1. Therefore, a key part of conservation efforts to benefit S. hineana is the identification and protection of any remaining habitat areas. Conventional sampling for the presence of S. hineana often includes both adult and larval sampling.Larval S. hineana surveys include benthic-sampling and the pumping of crayfish burrows. Larval S. hineana are most often found in the burrows of Cambarus (= Lacunicambarus) diogenes throughout the year and are almost exclusively found in C. diogenes burrows during their overwintering period2. Comprehensive larval surveys can take months to complete, require intensive training of field personnel, are reliant on favorable weather conditions, and are only effective if late instar larvae can be collected for identification. Adult S. hineana surveys are difficult due to short flight season, habitat segregation by sex, large potential flight range (adults can range for many kilometers from larval habitat), risk of harm when netting adult dragonflies, and difficulty observing genitalia characteristics necessary for accurate species identification when in flight1.Given the restrictions of conventional sampling techniques, there has been a great need to develop a method to expedite field site identification. Environmental DNA can be used to guide and prioritize locations for conventional surveying methods, increasing the speed at which habitats can be identified for protection and restoration.Environmental DNA (eDNA) is a relatively new surveillance method used to detect the presence of a species within a habitat by collecting environmental samples (e.g., soil and water) that contain cell fragments and exogenous DNA3. Mitochondrial genes, which are more plentiful and have a higher resistance to degradation than nuclear genes, are targeted and amplified to determine species presence or absence4,5,6,7.Currently, there is a taxonomic skew toward fish, amphibian, and mollusk eDNA studies7,8 suggesting the need to determine if eDNA methods can be useful for detecting aquatic insects. Environmental DNA analysis from 27 taxa of freshwater arthropods had been published as of 2019; some of these taxa include Procambarus clarkii, Pacifastacus leniusculus, and Gammarus pulex8. Additionally, the critically endangered plecopteran Isogenus nubecula was detected using eDNA methods9.The potential advantages of using eDNA rather than traditional surveying methods include the reduction of field labor hours10, reduced impact to sensitive habitats7, and a lower threshold of detection11,12. Additionally, eDNA has proven to be an effective tool when traditional methods require timely/costly surveying efforts6 and for detecting cryptic invasive species10.Although there is always some risk of damaging the habitat when studying a system, environmental DNA sampling (i.e., water, soil, ice) is much less invasive and has far less potential for harming native and endangered species than many traditional surveying methods7. For example, electrofishing can cause damage in the form of removing/killing fish from the sample site13. Traditional sampling methods for larval populations of S. hineana include benthic sampling (monitoring populations in stream beds) and burrow-pumping (a novel technique used to locate larvae within crayfish burrows)2. These techniques can disrupt flow patterns within shallow streams, collapse burrows, and harm/kill sampled individuals.While there has been some speculation that eDNA sampling may have high false-positive rates due to ancient DNA contamination from extirpated populations, studies show that eDNA typically becomes undetectable in water within 1–44 days after source removal10,14,15,16,17,18,19,20,21 and approximately 144 days in soil22. This suggests that eDNA surveys are contemporaneous and can be used to inform conservation efforts.Environmental DNA degradation is likely more complex in a field setting, and the persistence (defined here as the length of time eDNA remains detectable within a habitat or mesocosm) and net-accumulation (defined here as the difference between the amount of eDNA produced and the amount of eDNA degraded over time) are likely to vary depending on numerous factors that alter source/sink dynamics3. Spatiotemporal dynamics are especially important in affecting the persistence and accumulation of eDNA in the field and need to be accounted for when developing eDNA methodologies23. Concentrations of eDNA may fluctuate spatially and/or temporally as a result of fluctuations in biomass18,24,25, transport through a flowing system17,26,27,28, age structuring of target populations7,16, feeding activity29, life-history events5, seasonal habitat preference13,30, water temperature24,31,32,33, hydrology13,27, inhibition13,27, and microbial activity34. Some studies show that water pH affects eDNA degradation rates19, while others do not35. Similarly, some studies show that UV light exposure affects eDNA degradation rates17, while others show no such effect36.In this study, we focused on the effects that seasonal shifts in temperature have on the persistence and net-accumulation of larval S. hineana eDNA. Since temperature drives the production of eDNA through metabolic processes31 and directly alters the rate of microbial degradation of eDNA32, it may be the most important variable driving seasonal shifts in eDNA detection.Somatochlora hineana larval molting activity varies with seasonal changes, the net-accumulation of S. hineana eDNA within a habitat. Adult S. hineana females lay eggs within streams and streamlets during their flight period (July–early August). Eggs typically mature over winter. In the following year, hatching of pro-larva from eggs occurs between April and June. All S. hineana larvae go through approximately 12 larval instars (F-11 to F-0). The first 6 larval instars (F-11 through F-6) occur rapidly within the first year, and the final 6 (F-5 through F-0) occur more slowly over a period of 2–4 years1. Since S. hineana larvae take several years to fully mature, they survive overwintering in shallow, partially frozen streams within Cambarus (= Lacunicambarus) diogenes crayfish burrows. While S. hineana larvae overwinter within burrows, they rarely consume food or molt, thus reducing the amount of eDNA shed2.The net-accumulation of larval S. hineana eDNA was likely to increase with increasing temperatures2,31,37, while the persistence of larval S. hineana eDNA was likely to decrease with increasing temperatures32. Therefore, we assessed the seasonal shift in persistence and net-accumulation of larval S. hineana eDNA in temperature-controlled mesocosms that reflect the larval overwintering period (5.0 °C) and the larval active period (16.0 °C). This study provided preliminary information regarding the seasonal shift in eDNA production for larval S. hineana. Understanding the seasonal dynamics of larval S. hineana eDNA is vital for efficient detection of this rare aquatic species using eDNA protocols. Our mesocosm results have informed subsequent field sampling of S. hineana eDNA. More

1.Cramp, S. & Perrins, C. M. in The Birds of the Western Palearctic, Vol. 7 (eds. Cramp, S. & Perrins, C. M.) (Oxford University Press, 1993).2.Lack, D. Clutch and brood size in the Robin. Br. Birds 39(98–109), 130–135 (1946).
Google Scholar 
3.Lack, D. Further notes on clutch and brood size in the Robin. Br. Birds 41(98–104), 130–137 (1948).
Google Scholar 
4.Lack, D. The Life of Robin (Witherby, 1965).
Google Scholar 
5.Harper, D. G. C. Pairing strategies and mate choice in female Robins (Erithacus rubecula). Anim. Behav. 33, 862–875 (1985).Article 

Google Scholar 
6.Lebedeva, N. V. & Lomadze, N. H. in The Robin Erithacus Rubecula in the North-Western Caucasus (eds. Matishov, G. G. & Lebedeva, N. V.) 252–277 (SSC RAS Publishing, 2007).7.Knysh, N. P. Materials on the biology of Robin in forest-steppe deciduous forests of Sumy region. Berkut 17, 41–60 (2008).
Google Scholar 
8.Zimin V. B. in The Robin in the North of the Area, Vol. 1. Distribution. Number. Reproduction (ed. Zimin, V. B.) 401–422 (Karel’skiy nauchnyy centr RAN, 2009).9.Baranovskiy, A. V. & Ivanov, E. S. Features of reproductive biology of robins (Erithacus rubecula) in anthropogenic habitats (for example, the city of Ryazan). Principy èkologii 6, 17–25 (2017).
Google Scholar 
10.Wesołowski, T. Primeval conditions—What can we learn from them?. Ibis 149, 64–77 (2007).Article 

Google Scholar 
11.Tobias, J. & Seddon, N. Territoriality as a paternity guard in the European robin Erithacus rubecula. Anim. Behav. 60, 165–173 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Tobias, J. & Seddon, N. Female begging in European robins: Do neighbors eavesdrop for extrapair copulations?. Behav. Ecol. 13, 637–642 (2002).Article 

Google Scholar 
13.Lubjuhn, T., Strohbach, S., Brün, J., Gerken, T. & Epplen, J. T. Extra-pair paternity in great tits (Parus major)—A long term study. Behaviour 136, 1157–1172 (1999).Article 

Google Scholar 
14.Griffith, S. C., Owens, I. P. F. & Thuman, K. A. Extra pair paternity in birds: A review of interspecific variation and adaptive function. Mol. Ecol. 11, 2195–2212 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Cockburn, A. Prevalence of different modes of parental care in birds. Proc. Biol. Sci. 273, 1375–1383 (2006).PubMed 
PubMed Central 

Google Scholar 
16.Zagalska-Neubauer, M. & Dubiec, A. Techniki i markery molekularne w badaniach zmienności genetycznej ptaków. Not. Ornit. 48, 193–206 (2007).
Google Scholar 
17.Brouwer, L. & Griffith, S. C. Extra-pair paternity in birds. Mol. Ecol. 28, 4864–4882 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
18.Petter, S. C., Miles, D. B. & White, M. M. Genetic evidence of mixed reproductive strategy in a monogamous bird. Condor 92, 702–708 (1990).Article 

Google Scholar 
19.Jennions, M. D. & Petrie, M. Why do females mate multiply? A review of the genetic benefits. Biol. Rev. 75, 21–64 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Akçay, E. & Roughgarden, J. Extra-pair paternity in birds: Review of the genetic benefits. Evol. Ecol. Res. 9, 855–868 (2007).
Google Scholar 
21.Dietzen, C., Witt, H.-H. & Wink, M. The phylogeographic differentiation of the European robin Erithacus rubecula on the Canary Islands revealed by mitochondrial DNA sequence data and morphometrics: Evidence for a new robin on Gran Canaria?. Avian Sci. 3, 115–131 (2003).
Google Scholar 
22.Rodrigues, P. et al. Phylogeography and genetic diversity of the Robin (Erithacus rubecula) in the Azores Islands: Evidence of a recent colonisation. J. Ornithol. 154, 889–900 (2013).Article 

Google Scholar 
23.Fulgione, D., Rippa, D., Manganiello, E., Caliendo, M. F. & Rastogi, R. K. Seasonal genetic structure analysis of a resident population of European Robin. Open Zool. J. 1, 11–17 (2008).CAS 
Article 

Google Scholar 
24.Morin, P. A., Messier, J. & Woodruff, D. S. DNA extraction, amplification, and direct sequencing from hornbill feathers. J. Sci. Soc. Thail. 20, 31–41 (1994).CAS 
Article 

Google Scholar 
25.Wright, T. F. et al. Microsatellite variation among divergent populations of stalk-eyed flies, genus Cyrtodiopsis. Genet. Res. 84, 27–40 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Yue, G.-H., Kovacs, B. & Orban, L. A new problem with cross-species amplification of microsatellites: Generation of non-homologous products. Dongwuxue Yanjiu 2, 131–140 (2010).
Google Scholar 
27.Chapuis, M.-P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24, 621–631 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Dąbrowski, M. J., Bornelöv, S., Kruczyk, M., Baltzer, N. & Komorowski, J. ‘True’ null allele detection in microsatellite loci: A comparison of methods, assessment of difficulties and survey of possible improvements. Mol. Ecol. Resour. 15, 477–488 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
29.Primmer, C. R., Møller, A. P. & Ellegren, H. A wide-range survey of cross-species microsatellite amplification in birds. Mol. Ecol. 5, 365–378 (1996).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Jaroszewicz, B. et al. Białowieża forest—A relic of the high naturalness of European forests. Forests 10, 849 (2019).Article 

Google Scholar 
31.Campos, A. R. et al. How do Robins Erithacus rubecula resident in Iberia respond to seasonal flooding by conspecific migrants?. Bird Study 58, 435–442 (2011).Article 

Google Scholar 
32.Owen, J. C. Collecting, processing, and storing avian blood: A review. J. Field Ornithol. 82, 339–354 (2011).Article 

Google Scholar 
33.Horváth, M. B., Martínez-Cruz, B., Negro, J. J., Kalmár, L. & Godoy, J. A. An overlooked DNA source for non-invasive genetic analysis in birds. J. Avian Biol. 36, 84–88 (2005).Article 

Google Scholar 
34.Faircloth, B. C. MSATCOMMANDER: Detection of microsatellite repeat arrays and automated, locus-specific primer design. Mol. Ecol. Resour. 8, 92–94 (2008).CAS 
PubMed 
Article 

Google Scholar 
35.Schuelke, M. An economic method for the fluorescent labeling of PCR fragments. Nat. Biotechnol. 18, 233–234 (2000).CAS 
PubMed 
Article 

Google Scholar 
36.Austin, J. D. et al. Permanent genetic resources added to Molecular Ecology Resources Database 1 February 2011–31 March 2011. Mol. Ecol. Resour. 11, 757–758 (2011).CAS 
PubMed 
Article 

Google Scholar 
37.Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research—An update. Bioinformatics 28, 2537–2539 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Raymond, M. & Rousset, F. GENEPOP (version 1.2): Population genetics software for exact tests and ecumenicism. Heredity 86, 248–249 (1995).Article 

Google Scholar 
39.Rousset, F. GENEPOP’007: A complete reimplementation of the GENEPOP software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).PubMed 
Article 

Google Scholar 
40.Goudet, J. FSTAT, a program to estimate and test gene diversities and fixation indices, version 2.9.3. http://www.unil.ch/izea/softwares/fstat.htlm (2001).41.Kalinowski, S. T., Taper, M. L. & Marshall, T. C. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol. Ecol. 16, 1099–1106 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
42.Grohme, M. A., Soler, R. F., Wink, M. & Frohme, M. Microsatellite marker discovery using single molecule real-time circular consensus sequencing on the Pacific Biosciences RS. Biotechniques 55, 253–256 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Liljegren, M. M., de Muinck, E. J. & Trosvik, P. Microsatellite length scoring by single molecule real time sequencing-effects of sequence structure and PCR regime. PLoS ONE 11, e0159232 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
44.Dutta, N. et al. Microsatellite marker set for genetic diversity assessment of primitive Chitala chitala (Hamilton, 1822) derived through SMRT sequencing technology. Mol. Biol. Rep. 46, 41–49 (2018).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
45.Selkoe, K. A. & Toonen, R. J. Microsatellites for ecologists: A practical guide to using and evaluating microsatellite markers. Ecol. Lett. 9, 615–629 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
46.Corner, S., Yuzbasiyan-Gurkan, V., Agnew, D. & Venta, P. J. Development of a 12-plex of new microsatellite markers using a novel universal primer method to evaluate the genetic diversity of jaguars (Panthera onca) from North American zoological institutions. Conserv. Genet. Resour. 11, 487–497 (2019).Article 

Google Scholar 
47.Graham, B. A., Carpenter, A. M., Friesen, V. L. & Burg, T. M. A comparison of neutral genetic differentiation and genetic diversity among migratory and resident populations of Golden-crowned-Kinglets (Regulus satrapa). J. Ornithol. 161, 509–519 (2020).Article 

Google Scholar 
48.Bensch, S., Grahn, M., Müller, N., Gay, L. & Akesson, S. A. Genetic, morphological, and feather isotope variation of migratory willow warblers show gradual divergence in a ring. Mol. Ecol. 18, 3087–3096 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
49.Kralj, J., Procházka, P., Fainová, D., Patzenhauerová, H. & Tutiš, V. Intraspecific variation in the wing shape and genetic differentiation of reed warblers Acrocephalus scirpaceus in Croatia. Acta Ornithol. 45, 51–58 (2010).Article 

Google Scholar 
50.Mettler, R. et al. Contrasting patterns of genetic differentiation among blackcaps (Sylvia atricapilla) with divergent migratory orientations in Europe. PLoS ONE 8, e81365 (2013).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Gyllensten, U., Jakonsson, S. & Temrin, H. No evidence for illegitimate young in monogamous and polygynous warblers. Nature 343, 168–170 (1990).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Gil, D., Slater, P. J. B. & Graves, J. A. Extrapair paternity and song characteristics in the willow warbler Phylloscopus trochilus. J. Avian Biol. 38, 291–297 (2007).Article 

Google Scholar 
53.Moskalenko, V. N., Belokon, M. M., Belokon, Y. S. & Goretskaia, M. I. Extrapair young in nests of the Wood Warbler (Phylloscopus sibilatrix) in the Middle Russia (poster). In 26th International Ornithological Congress (2014).54.Grendelmeier, A., Arlettaz, R., Olano-Marin, J. & Pasinelli, G. Experimentally provided conspecific cues boost bird territory density but not breeding performance. Behav. Ecol. 28, 174–185 (2017).Article 

Google Scholar 
55.Petrie, M. & Kempenaers, B. Extrapair paternity in birds: Explaining variation between species and populations. Trends Ecol. Evol. 13, 52–58 (1998).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Wagner, R. H. Hidden leks: sexual selection and the clustering of avian territories. Ornithol. Monogr. 49, 123–145 (1998).Article 

Google Scholar 
57.Fletcher, R. J. & Miller, C. W. On the evolution of hidden leks and the implications for reproductive and habitat selection behaviours. Anim. Behav. 71, 1247–1251 (2006).Article 

Google Scholar 
58.Broughton, R. K., Bubnicki, J. W. & Maziarz, M. Multi-scale settlement patterns of a migratory songbird in a European primeval forest. Behav. Ecol. Sociobiol. 74, 1–12 (2020).Article 

Google Scholar  More

1.Brommer, J. E., Pietiäinen, H. & Kolunen, H. Reproduction and survival in a variable environment: Ural owls (Strix uralensis) and the three-year vole cycle. Auk 119, 544–550. https://doi.org/10.1642/0004-8038(2002)119[0544:rasiav]2.0.co;2 (2002).Article 

Google Scholar 
2.Begon, M., Townsend, C. R. & Harper, J. L. Ecology, Individuals, Populations and Communities 4th edn. (Blackwell, 2006).
Google Scholar 
3.Chang, A. M. & Wiebe, K. L. Body condition in snowy owls wintering on the prairies is greater in females and older individuals and may contribute to sex-biased mortality. Auk 133, 738–746. https://doi.org/10.1642/auk-16-60.1 (2016).Article 

Google Scholar 
4.McLean, N., van der Jeugd, H. P. & van de Pol, M. High intra-specific variation in avian body condition responses to climate limits generalisation across species. PLoS ONE 13, e0192401. https://doi.org/10.1371/journal.pone.0192401 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
5.McLean, N. M., van der Jeugd, H. P., van Turnhout, C. A. M., Lefcheck, J. S. & van de Pol, M. Reduced avian body condition due to global warming has little reproductive or population consequences. Oikos 129, 714–730. https://doi.org/10.1111/oik.06802 (2020).Article 

Google Scholar 
6.Aubry, L. M. et al. Climate change, phenology, and habitat degradation: Drivers of gosling body condition and juvenile survival in lesser snow geese. Glob. Change Biol. 19, 149–160. https://doi.org/10.1111/gcb.12013 (2013).ADS 
Article 

Google Scholar 
7.Gardner, J. L., Amano, T., Sutherland, W. J., Clayton, M. & Peters, A. Individual and demographic consequences of reduced body condition following repeated exposure to high temperatures. Ecology 97, 786–795. https://doi.org/10.1890/15-0642.1 (2016).Article 
PubMed 

Google Scholar 
8.Newton, I. Population Limitation in Birds (Academic Press, 1998).
Google Scholar 
9.Dunn, P. O. & Møller, A. P. Effects of Climate Change on Birds 2nd edn. (Oxford University Press, 2019).Book 

Google Scholar 
10.Crossin, G. T. et al. A carryover effect of migration underlies individual variation in reproductive readiness and extreme egg size dimorphism in Macaroni penguins. Am. Nat. 176, 357–366. https://doi.org/10.1086/655223 (2010).Article 
PubMed 

Google Scholar 
11.Clausen, K. K., Madsen, J. & Tombre, I. M. Carry-over or compensation? The impact of winter harshness and post-winter body condition on spring-fattening in a migratory goose species. PLoS ONE 10(7), e0132312. https://doi.org/10.1371/journal.pone.0132312 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
12.Selonen, V., Wistbacka, R. & Korpimäki, E. Food abundance and weather modify reproduction of two arboreal squirrel species. J. Mammal. 97, 1376–1384. https://doi.org/10.1093/jmammal/gyw096 (2016).Article 

Google Scholar 
13.Harrison, X. A., Blount, J. D., Inger, R., Norris, D. R. & Bearhop, S. Carry-over effects as drivers of fitness differences in animals. J. Anim. Ecol. 80, 4–18. https://doi.org/10.1111/j.1365-2656.2010.01740.x (2011).Article 
PubMed 

Google Scholar 
14.O’Connor, C. M., Norris, D. R., Crossin, G. T. & Cooke, S. J. Biological carryover effects: Linking common concepts and mechanisms in ecology and evolution. Ecosphere 5, 1–11. https://doi.org/10.1890/es13-00388.1 (2014).Article 

Google Scholar 
15.Montreuil-Spencer, C., Schoenemann, K., Lendvai, A. Z. & Bonier, F. Winter corticosterone and body condition predict breeding investment in a nonmigratory bird. Behav. Ecol. 30, 1642–1652. https://doi.org/10.1093/beheco/arz129 (2019).Article 

Google Scholar 
16.Korpimäki, E. Body mass of breeding Tengmalm’s owls Aegolius funereus: Seasonal, between-year, site and age-related variation. Ornis Scand. 21, 169–178. https://doi.org/10.2307/3676776 (1990).Article 

Google Scholar 
17.Dijkstra, C., Daan, S., Meijer, T., Cave, A. J. & Foppen, R. P. B. Daily and seasonal-variations in body-mass of the kestrel in relation to food availability and reproduction. Ardea 76, 127–140 (1988).
Google Scholar 
18.Pietiäinen, H. & Kolunen, H. Female body condition and breeding of the Ural owl Strix uralensis. Funct. Ecol. 7, 726–735. https://doi.org/10.2307/2390195 (1993).Article 

Google Scholar 
19.Wijnandts, H. Ecological energetics of the long-eared owl (Asio otus). Ardea 72, 1–92 (1984).
Google Scholar 
20.Korpimäki, E. & Hakkarainen, H. Fluctuating food supply affects the cluch size of Tengmalm’s owl independent of laying date. Oecologia 85, 543–552 (1991).ADS 
Article 

Google Scholar 
21.Korpimäki, E. & Wiehn, J. Clutch size of kestrels: Seasonal decline and experimental evidence for food limitation under fluctuating food conditions. Oikos 83, 259–272. https://doi.org/10.2307/3546837 (1998).Article 

Google Scholar 
22.Pietiäinen, H. Seasonal and individual variation in the production of offspring in the Ural owl Strix uralensis. J. Anim. Ecol. 58, 905–920. https://doi.org/10.2307/5132 (1989).Article 

Google Scholar 
23.Wellicome, T. I. Effects of food on reproduction in burrowing owls (Athene cunicularia) during three stages of the breeding season (Ph.D. dissertation). (University of Alberta, 2000).24.Ilmonen, P. et al. Parental effort and blood parasitism in Tengmalm’s owl: Effects of natural and experimental variation in food abundance. Oikos 86, 79–86. https://doi.org/10.2307/3546571 (1999).Article 

Google Scholar 
25.Santangeli, A., Hakkarainen, H., Laaksonen, T. & Korpimäki, E. Home range size is determined by habitat composition but feeding rate by food availability in male Tengmalm’s owls. Anim. Behav. 83, 1115–1123. https://doi.org/10.1016/j.anbehav.2012.02.002 (2012).Article 

Google Scholar 
26.Griebel, R. L. & Savidge, J. A. Factors related to body condition of nestling burrowing owls in Buffalo Gap National Grassland, South Dakota. Wilson Bull. 115, 477–480. https://doi.org/10.1676/02-094 (2003).Article 

Google Scholar 
27.Valkama, J., Korpimäki, E., Holm, A. & Hakkarainen, H. Hatching asynchrony and brood reduction in Tengmalm’s owl Aegolius funereus: The role of temporal and spatial variation in food abundance. Oecologia 133, 334–341. https://doi.org/10.1007/s00442-002-1033-2 (2002).ADS 
Article 
PubMed 

Google Scholar 
28.König, C. & Weick, F. Owls of the World 2nd edn. (Yale University Press, 2008).
Google Scholar 
29.Mikkola, H. Owls of Europe (Poyser, 1983).
Google Scholar 
30.Korpimäki, E. On the Ecology and Biology of Tengmalm’s Owl (Aegolius funereus) in Southern Ostrobothnia and Soumenselkä, Western Finland Vol. 13, 1–84 (University of Oulu, 1981).
Google Scholar 
31.Korpimäki, E. Diet of breeding Tengmalm’s owls Aegolius funereus: Long-term changes and year-to-year variation under cyclic food conditions. Ornis Fenn. 65, 21–30 (1988).
Google Scholar 
32.Korpimäki, E. & Hakkarainen, H. The Boreal Owl: Ecology, Behaviour and Conservation of a Forest-Dwelling Predator (Cambridge University Press, 2012).Book 

Google Scholar 
33.Kouba, M., Bartoš, L., Šindelář, J. & Šťastný, K. Alloparental care and adoption in Tengmalm’s owl (Aegolius funereus). J. Ornithol. 158, 185–191. https://doi.org/10.1007/s10336-016-1381-z (2017).Article 

Google Scholar 
34.Eldegard, K. & Sonerud, G. A. Experimental increase in food supply influences the outcome of within-family conflicts in Tengmalm’s owl. Behav. Ecol. Sociobiol. 64, 815–826 (2010).Article 

Google Scholar 
35.Eldegard, K. & Sonerud, G. A. Sex roles during post-fledging care in birds: Female Tengmalm’s owls contribute little to food provisioning. J. Ornithol. 153, 385–398. https://doi.org/10.1007/s10336-011-0753-7 (2012).Article 

Google Scholar 
36.Kouba, M., Bartoš, L. & Šťastný, K. Differential movement patterns of juvenile Tengmalm’s owls (Aegolius funereus) during the post-fledging dependence period in two years with contrasting prey abundance. PLoS ONE 8(7), e67034. https://doi.org/10.1371/journal.pone.0067034 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
37.Korpimäki, E. Fluctuating food abundance determines the lifetime reproductive success of male Tengmalm’s owls. J. Anim. Ecol. 61, 103–111 (1992).Article 

Google Scholar 
38.Kouba, M., Bartoš, L., Korpimäki, E. & Zárybnická, M. Factors affecting the duration of nestling period and fledging order in Tengmalm’s owl (Aegolius funereus): Effect of wing length and hatching sequence. PLoS ONE 10(3), e0121641. https://doi.org/10.1371/journal.pone.0121641 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
39.Björklund, H., Saurola, P. & Valkama, J. Petolintuvuosi 2019 oli kohtalainen (Summary: Breeding and population trends of common raptors and owls in Finland in 2019). Yearb. Linnut Mag. 2019, 44–59 (2020).
Google Scholar 
40.Kouba, M., Bartoš, L., Bartošová, J., Hongisto, K. & Korpimäki, E. Interactive influences of fluctuations of main food resources and climate change on long-term population decline of Tengmalm’s owls in the boreal forest. Sci. Rep. 10, 20429. https://doi.org/10.1038/s41598-41020-77531-y (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
41.Ferrero, J. J., Grande, J. M. & Negro, J. J. Copulation behavior of a potentially double-brooded bird of prey, the black-winged kite (Elanus caeruleus). J. Raptor Res. 37, 1–7 (2003).
Google Scholar 
42.Sergio, F. From individual behaviour to population pattern: Weather-dependent foraging and breeding performance in black kites. Anim. Behav. 66, 1109–1117. https://doi.org/10.1006/anbe.2003.2303 (2003).Article 

Google Scholar 
43.Korpimäki, E. Effects of age on breeding performance of Tengmalm’s owl Aegolius funereus in western Finland. Ornis Scand. 19, 21–26 (1988).Article 

Google Scholar 
44.Laaksonen, T., Korpimäki, E. & Hakkarainen, H. Interactive effects of parental age and environmental variation on the breeding performance of Tengmalm’s owls. J. Anim. Ecol. 71, 23–31. https://doi.org/10.1046/j.0021-8790.2001.00570.x (2002).Article 

Google Scholar 
45.Korpimäki, E. Highlights from a long-term study of Tengmalm’s owls: Cyclic fluctuations in vole abundance govern mating systems, population dynamics and demography. Brit. Birds 113, 316–333 (2020).
Google Scholar 
46.Peig, J. & Green, A. J. New perspectives for estimating body condition from mass/length data: The scaled mass index as an alternative method. Oikos 118, 1883–1891. https://doi.org/10.1111/j.1600-0706.2009.17643.x (2009).Article 

Google Scholar 
47.Korpimäki, E., Norrdahl, K., Huitu, O. & Klemola, T. Predator-induced synchrony in population oscillations of coexisting small mammal species. Proc. R. Soc. B-Biol. Sci. 272, 193–202 (2005).Article 

Google Scholar 
48.Huitu, O., Norrdahl, K. & Korpimäki, E. Landscape effects on temporal and spatial properties of vole population fluctuations. Oecologia 135, 209–220. https://doi.org/10.1007/s00442-002-1171-6 (2003).ADS 
Article 
PubMed 

Google Scholar 
49.Schreiber-Gregory, D. N. & Jackson, H. M. Multicollinearity: What is it, why should we care, and how can it be controlled. In Proc. SAS R Global Forum 2017, Conference Paper 1404 (2017).50.Zuur, A., Ieno, E. N. & Smith, G. M. Analyzing Ecological Data (Springer, 2007).Book 

Google Scholar 
51.Tao, J., Littel, R., Patetta, M., Truxillo, C. & Wolfinger, R. Mixed Model Analyses Using the SAS System Course Notes (SAS Institute Inc., 2002).
Google Scholar 
52.Burnham, K. P. & Anderson, D. R. Model Selection and Inference: A Practical Information-Theoretical Approach (Springer, 1998).Book 

Google Scholar 
53.Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Control 19, 716–723 (1974).ADS 
MathSciNet 
Article 

Google Scholar 
54.Vaida, F. & Blanchard, S. Conditional Akaike information for mixed-effects models. Biometrika 92, 351–370. https://doi.org/10.1093/biomet/92.2.351 (2005).MathSciNet 
Article 
MATH 

Google Scholar 
55.Ward, E. J. A review and comparison of four commonly used Bayesian and maximum likelihood model selection tools. Ecol. Model. 211, 1–10. https://doi.org/10.1016/j.ecolmodel.2007.10.030 (2008).CAS 
Article 

Google Scholar 
56.Schwarz, G. Estimating the dimension of a model. Ann. Stat. 6, 461–464 (1978).MathSciNet 
Article 

Google Scholar 
57.Christensen, W. Agreeing to disagree: Using SAS to make reasoned decisions when information criteria select different models. In SAS Conference Proceedings: Western Users of SAS Software 2018. September 5–7, 2018, Sacramento, California, Paper 099–2018 (2018).58.Posada, D. & Buckley, T. R. Model selection and model averaging in phylogenetics: Advantages of Akaike information criterion and Bayesian approaches over likelihood ratio tests. Syst. Biol. 53, 793–808. https://doi.org/10.1080/10635150490522304 (2004).Article 
PubMed 

Google Scholar 
59.Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach 2nd edn. (Springer, 2002).MATH 

Google Scholar 
60.Buckland, S. T., Burnham, K. P. & Augustin, N. H. Model selection: An integral part of inference. Biometrics 53, 603–618. https://doi.org/10.2307/2533961 (1997).Article 
MATH 

Google Scholar 
61.Wagenmakers, E. J. & Farrell, S. AIC model selection using Akaike weights. Psychon. Bull. Rev. 11, 192–196. https://doi.org/10.3758/bf03206482 (2004).Article 
PubMed 

Google Scholar 
62.Lack, D. The Natural Regulation of Animal Numbers (Oxford University Press, 1954).
Google Scholar 
63.Korpela, K. et al. Nonlinear effects of climate on boreal rodent dynamics: Mild winters do not negate high-amplitude cycles. Glob. Change Biol. 19, 697–710. https://doi.org/10.1111/gcb.12099 (2013).ADS 
Article 

Google Scholar 
64.Wiehn, J. & Korpimäki, E. Food limitation on brood size: Experimental evidence in the Eurasian kestrel. Ecology 78, 2043–2050. https://doi.org/10.2307/2265943 (1997).Article 

Google Scholar 
65.Korpimäki, E. & Lagerström, M. Survival and natal dispersal of fledglings of Tengmalm’s owl in relation to fluctuating food conditions and hatching date. J. Anim. Ecol. 57, 433–441 (1988).Article 

Google Scholar 
66.Norris, K. J. Female choice and the quality of parental care in the great tit Parus major. Behav. Ecol. Sociobiol. 27, 275–281 (1990).Article 

Google Scholar 
67.Naef-Daenzer, B., Widmer, F. & Nuber, M. Differential post-fledging survival of great and coal tits in relation to their condition and fledging date. J. Anim. Ecol. 70, 730–738. https://doi.org/10.1046/j.0021-8790.2001.00533.x (2001).Article 

Google Scholar 
68.Grüebler, M. U. & Naef-Daenzer, B. Postfledging parental effort in barn swallows: Evidence for a trade-off in the allocation of time between broods. Anim. Behav. 75, 1877–1884. https://doi.org/10.1016/j.anbehav.2007.12.002 (2008).Article 

Google Scholar 
69.Jones, T. M., Ward, M. P., Benson, T. J. & Brawn, J. D. Variation in nestling body condition and wing development predict cause-specific mortality in fledgling dickcissels. J. Avian Biol. 48, 439–447. https://doi.org/10.1111/jav.01143 (2017).Article 

Google Scholar 
70.Magrath, R. D. Nestling weight and juvenile survival in the blackbird, Turdus merula. J. Anim. Ecol. 60, 335–351. https://doi.org/10.2307/5464 (1991).Article 

Google Scholar 
71.Naef-Daenzer, B. & Grüebler, M. U. Post-fledging survival of altricial birds: Ecological determinants and adaptation. J. Field Ornithol. 87, 227–250. https://doi.org/10.1111/jofo.12157 (2016).Article 

Google Scholar 
72.Winkler, D. W., Luo, M. K. & Rakhimberdiev, E. Temperature effects on food supply and chick mortality in tree swallows (Tachycineta bicolor). Oecologia 173, 129–138. https://doi.org/10.1007/s00442-013-2605-z (2013).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
73.Hylton, R. A., Frederick, P. C., de la Fuente, T. E. & Spalding, M. G. Effects of nestling health on postfledging survival of wood storks. Condor 108, 97–106. https://doi.org/10.1650/0010-5422(2006)108[0097:Eonhop]2.0.Co;2 (2006).Article 

Google Scholar 
74.Imlay, T. L., Mann, H. A. R. & Leonard, M. L. No effect of insect abundance on nestling survival or mass for three aerial insectivores. Avian Conserv. Ecol. https://doi.org/10.5751/ace-01092-120219 (2017).Article 

Google Scholar 
75.Nooker, J. K., Dunn, P. O. & Whittingham, L. A. Effects of food abundance, weather, and female condition on reproduction in tree swallows (Tachycineta bicolor). Auk 122, 1225–1238. https://doi.org/10.1642/0004-8038(2005)122[1225:eofawa]2.0.co;2 (2005).Article 

Google Scholar 
76.Perrig, M., Gruebler, M. U., Keil, H. & Naef-Daenzer, B. Experimental food supplementation affects the physical development, behaviour and survival of little owl Athene noctua nestlings. Ibis 156, 755–767. https://doi.org/10.1111/ibi.12171 (2014).Article 

Google Scholar 
77.Perrig, M., Gruebler, M. U., Keil, H. & Naef-Daenzer, B. Post-fledging survival of little owls Athene noctua in relation to nestling food supply. Ibis 159, 532–540. https://doi.org/10.1111/ibi.12477 (2017).Article 

Google Scholar 
78.McDonald, P. G., Olsen, P. D. & Cockburn, A. Sex allocation and nestling survival in a dimorphic raptor: Does size matter? Behav. Ecol. 16, 922–930. https://doi.org/10.1093/beheco/ari071 (2005).Article 

Google Scholar 
79.Morosinotto, C. et al. Fledging mass is color morph specific and affects local recruitment in a wild bird. Am. Nat. 196, 609–619. https://doi.org/10.1086/710708 (2020).Article 
PubMed 

Google Scholar 
80.Overskaug, K., Bolstad, J. P., Sunde, P. & Øien, I. J. Fledgling behavior and survival in northern tawny owls. Condor 101, 169–174 (1999).Article 

Google Scholar 
81.Todd, L. D., Poulin, R. G., Wellicome, T. I. & Brigham, R. M. Post-fledging survival of burrowing owls in Saskatchewan. J. Wildl. Manage. 67, 512–519. https://doi.org/10.2307/3802709 (2003).Article 

Google Scholar 
82.Cox, W. A., Thompson, F. R., Cox, A. S. & Faaborg, J. Post-fledging survival in passerine birds and the value of post-fledging studies to conservation. J. Wildl. Manage. 78, 183–193. https://doi.org/10.1002/jwmg.670 (2014).Article 

Google Scholar 
83.Korpimäki, E. Timing of breeding of Tengmalm’s owl Aegolius funereus in relation to vole dynamics in western Finland. Ibis 129, 58–68 (1987).Article 

Google Scholar 
84.Pigeault, R., Cozzarolo, C. S., Glaizot, O. & Christe, P. Effect of age, haemosporidian infection and body condition on pair composition and reproductive success in great tits Parus major. Ibis 162, 613–626. https://doi.org/10.1111/ibi.12774 (2020).Article 

Google Scholar 
85.Hakkarainen, H. & Korpimäki, E. The effect of female body-size on clutch volume of Tengmalm’s owls Aegolius funereus in varying food conditions. Ornis Fenn. 70, 189–195 (1993).
Google Scholar 
86.Hanauska-Brown, L. A., Dufty, A. M. & Roloff, G. J. Blood chemistry, cytology, and body condition in adult northern goshawks (Accipiter gentilis). J. Raptor Res. 37, 299–306 (2003).
Google Scholar 
87.Chastel, O., Weimerskirch, H. & Jouventin, P. Body condition and seabird reproductive performance: A study of three petrel species. Ecology 76, 2240–2246. https://doi.org/10.2307/1941698 (1995).Article 

Google Scholar 
88.Grilli, M. G., Pari, M. & Ibanez, A. Poor body conditions during the breeding period in a seabird population with low breeding success. Mar. Biol. https://doi.org/10.1007/s00227-018-3401-4 (2018).Article 

Google Scholar 
89.Toland, B. Hunting success of some Missouri raptors. Wilson Bull. 98, 116–125 (1986).
Google Scholar 
90.Masoero, G., Morosinotto, C., Laaksonen, T. & Korpimäki, E. Food hoarding of an avian predator: Sex- and age-related differences under fluctuating food conditions. Behav. Ecol. Sociobiol. https://doi.org/10.1007/s00265-00018-02571-x (2018).Article 

Google Scholar 
91.Masoero, G., Laaksonen, T., Morosinotto, C. & Korpimäki, E. Age and sex differences in numerical responses, dietary shifts, and total responses of a generalist predator to population dynamics of main prey. Oecologia 192, 699–711. https://doi.org/10.1007/s00442-020-04607-x (2020).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
92.Norrdahl, K. & Korpimäki, E. Changes in population structure and reproduction during a 3-year population cycle of voles. Oikos 96, 331–345. https://doi.org/10.1034/j.1600-0706.2002.970319.x (2002).Article 

Google Scholar 
93.Merritt, J. F., Lima, M. & Bozinovic, F. Seasonal regulation in fluctuating small mammal populations: Feedback structure and climate. Oikos 94, 505–514. https://doi.org/10.1034/j.1600-0706.2001.940312.x (2001).Article 

Google Scholar 
94.Solonen, T. Overwinter population change of small mammals in southern Finland. Ann. Zool. Fenn. 43, 295–302 (2006).
Google Scholar 
95.Haapakoski, M. & Ylönen, H. Snow evens fragmentation effects and food determines overwintering success in ground-dwelling voles. Ecol. Res. 28, 307–315. https://doi.org/10.1007/s11284-012-1020-y (2013).Article 

Google Scholar 
96.Berlioz, J. & Bergman, G. (eds) Proc., XII International Ornithological Congress, Helsinki 5–12 Vol. 158, 586–591 (Tilgmannin Kirjapaino, 1960).
Google Scholar 
97.Fraixedas, S., Linden, A. & Lehikoinen, A. Population trends of common breeding forest birds in southern Finland are consistent with trends in forest management and climate change. Ornis Fenn. 92, 187–203 (2015).
Google Scholar 
98.Virkkala, R. Long-term decline of southern boreal forest birds: Consequence of habitat alteration or climate change? Biodivers. Conserv. 25, 151–167. https://doi.org/10.1007/s10531-015-1043-0 (2016).Article 

Google Scholar 
99.Björklund, H., Valkama, J., Tomppo, E. & Laaksonen, T. Habitat effects on the breeding performance of three forest-dwelling hawks. PLoS ONE 10(9), e0137877. https://doi.org/10.1371/journal.pone.0137877 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
100.Koskimäki, J. et al. Are habitat loss, predation risk and climate related to the drastic decline in a Siberian flying squirrel population? A 15-year study. Popul. Ecol. 56, 341–348. https://doi.org/10.1007/s10144-013-0411-4 (2014).Article 

Google Scholar 
101.Suzuki, N. & Parker, K. L. Proactive conservation of high-value habitat for woodland caribou and grizzly bears in the boreal zone of British Columbia, Canada. Biol. Conserv. 230, 91–103. https://doi.org/10.1016/j.biocon.2018.12.013 (2019).Article 

Google Scholar 
102.Venier, L. A. et al. Effects of natural resource development on the terrestrial biodiversity of Canadian boreal forests. Environ. Rev. 22, 457–490. https://doi.org/10.1139/er-2013-0075 (2014).Article 

Google Scholar 
103.Thomas, J. W. et al. A Conservation Strategy for the Northern Spotted Owl (US Government Printing Office 791-171/20026, 1990).
Google Scholar 
104.Laaksonen, T. & Lehikoinen, A. Population trends in boreal birds: Continuing declines in agricultural, northern, and long-distance migrant species. Biol. Conserv. 168, 99–107. https://doi.org/10.1016/j.biocon.2013.09.007 (2013).Article 

Google Scholar  More

1.Dengler, J. et al. Biodiversity of palaearctic grasslands: A synthesis. Agric. Ecosyst. Environ. 182(1), 1–14 (2014).Article 

Google Scholar 
2.Wang, H. et al. Alpine grassland plants grow earlier and faster but biomass remains unchanged over 35 years of climate change. Ecol. Lett. 23, 701–710 (2020).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Torok, P. et al. Step(pe) up! Raising the profile of the Palaearctic natural grasslands. Biodivers. Conserv. 25(12), 2187–2195 (2016).Article 

Google Scholar 
4.Kuss, F. R. & Graefe, A. R. Effects of recreation trampling on natural area vegetation. J. Leis. Res. 17, 165–183 (1985).Article 

Google Scholar 
5.Buckley, R. C. & Pannell, J. Environmental impacts of tourism and recreation in national parks and conservation reserves. J. Tourism Stud. 1, 24–32 (1990).
Google Scholar 
6.Yorks, T. et al. Toleration of traffic by vegetation: Life form conclusions and summary extracts from a comprehensive data base. Environ. Manage. 21, 12–131 (1997).Article 

Google Scholar 
7.Gouvenain, R. C. Indirect impacts of soil trampling on tree growth and plant succession in the north cascade mountains of Washington (1996).8.Xu, L., Yu, F. H. & Drunen, E. V. Trampling, defoliation and physiological integration affect growth, morphological and mechanical properties of a root-suckering clonal tree. Ann. Bot. 109, 1001–1008 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
9.Bayfield, N. G. Use and deterioration of some Scottish hill paths. J. Appl. Ecol. 10, 639–648 (1973).Article 

Google Scholar 
10.Liddle, M. J. A selective review of the ecological effects of human trampling on natural ecosystems. Biol. Cons. 7, 17–36 (1975).Article 

Google Scholar 
11.Frissell, S. S. Judging recreational impacts on wilderness campsites. J. Forest. 76, 481–483 (1978).
Google Scholar 
12.Wagar, J. A. How to predict which vegetated areas will stand up best under ‘active’ recreation. Am. Recreat. J. 1, 20–21 (1961).
Google Scholar 
13.Cole, D. N. & Bayfield, N. G. Recreational trampling of vegetation: Standard experimental procedures. Biol. Cons. 63(3), 209–215 (1993).Article 

Google Scholar 
14.Prescott, O. & Stewart, G. Assessing the impacts of human trampling on vegetation: A systematic review and meta-analysis of experimental evidence. PeerJ 2, e360 (2014).Article 

Google Scholar 
15.Loreau, M. et al. Biodiversity and ecosystem functioning: Current knowledge and future challenges. Science 294, 804–808 (2001).ADS 
CAS 
PubMed 
Article 

Google Scholar 
16.Vandewalle, M. et al. Functional traits as indicators of biodiversity response to land use changes across ecosystems and organisms. Biodivers. Conserv. 19, 2921–2947 (2010).Article 

Google Scholar 
17.Petchey, O. L. & Gaston, K. J. Functional diversity, species richness and composition. Ecol. Lett. 5, 402–411 (2002).Article 

Google Scholar 
18.Cadotte, M. W., Carscadden, K. & Mirotchnick, N. Beyond species: Functional diversity and the maintenance of ecological processes and services. J. Appl. Ecol. 48(5), 1079–1087 (2011).Article 

Google Scholar 
19.Mouillot, D. et al. A functional approach reveals community responses to disturbances. Trends Ecol. Evol. 28, 167–177 (2013).PubMed 
Article 

Google Scholar 
20.Carmona, C. P. et al. Traits without borders: Integrating functional diversity across scales. Trends Ecol. Evol. 31(5), 382–394 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Conradi, T. et al. Impacts of visitor trampling on the taxonomic and functional community structure of calcareous grassland. Appl. Veg. Sci. 18, 359–367 (2015).Article 

Google Scholar 
22.Pickering, C. M. & Barros, A. Using functional traits to assess the resistance of subalpine grassland to trampling by mountain biking and hiking. J. Environ. Manage. 164, 129–136 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
23.Baraloto, C., Herault, B. & Paine, C. E. T. Contrasting taxonomic and functional responses of a tropic tree community to selective logging. J. Appl. Ecol. 49, 861–870 (2012).Article 

Google Scholar 
24.Magnago, L. F. S. et al. Functional attributes change but functional richness is unchanged after fragmentation of Brazilian Atlantic forests. J. Ecol. 102, 475–485 (2014).Article 

Google Scholar 
25.Marion, J. L. & Cole, D. N. Spatial and temporal variation in soil and vegetation impacts on campsites. Ecol. Appl. 6, 520–530 (1996).Article 

Google Scholar 
26.Roovers, P. et al. Experimental trampling and vegetation recovery in some forest and heathland communities. Appl. Veg. Sci. 7, 111–118 (2004).Article 

Google Scholar 
27.Conradi, T. et al. Impacts of visitor trampling on the taxonomic and functional community structure of calcareous grassland. Appl. Veg. Sci. 18(3), 359–367 (2015).Article 

Google Scholar 
28.Zamora, R. Functional equivalence in plant-animal interactions: Ecological and evolutionary consequences. Oikos 88(2), 442–447 (2000).Article 

Google Scholar 
29.Hubbell, S. P. Neutral theory in community ecology and the hypothesis of functional equivalence. Funct. Ecol. 19, 166–172 (2005).Article 

Google Scholar 
30.Mouchet, M. A. et al. Functional diversity measures: An overview of their redundancy and their ability to discriminate community assembly rules. Funct. Ecol. 24, 867–876 (2010).Article 

Google Scholar 
31.Diaz, S. et al. Incorporating plant functional diversity effects in ecosystem service assessments. Proc. Natl. Acad. Sci. U.S.A. 104, 20684–20689 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Suding, K. N. & Goldstein, L. J. Testing the Holy Grail framework: Using functional traits to predict ecosystem change. New Phytol. 180, 559–562 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
33.De Bello, F. et al. Towards an assessment of multiple ecosystem processes and services via functional traits. Biodivers. Conserv. 19, 2873–2893 (2010).Article 

Google Scholar 
34.Devictor, V. et al. Spatial mismatch and congruence between taxonomic, phylogenetic and functional diversity: The need for integrative conservation strategies in a changing world. Ecol. Lett. 13, 1030–1040 (2010).PubMed 
PubMed Central 

Google Scholar 
35.Tian, K., Guo, H. J. & Yang, Y. M. Ecological structures and functions of plateau wetlands in China (Chinese Science Press, 2009).
Google Scholar 
36.Garnier, E. et al. standardized protocol for the determination of specific leaf size and leaf dry matter content. Funct. Ecol. 15, 688–695 (2001).Article 

Google Scholar 
37.Cornelissen, J. H. C. et al. A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Aust. J. Bot. 51, 335–380 (2003).Article 

Google Scholar 
38.Mason, N. W. H. et al. An index of functional diversity. J. Veg. Sci. 14, 571–578 (2003).Article 

Google Scholar 
39.Villeger, S., Mason, N. W. H. & Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89, 2290–2301 (2008).PubMed 
Article 

Google Scholar 
40.Lavorel, S. & Garnier, E. Predicting changes in community composition and ecosystem functioning from plant traits: Revisiting the Holy Grail. Funct. Ecol. 16, 545–556 (2002).Article 

Google Scholar 
41.Cianciaruso, M. V. et al. Including intraspecific variability in functional diversity. Ecology 90, 81–89 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Lepš, J. et al. Community trait response to environment: Disentangling species turnover vs intraspecific trait variability effects. Ecography 34, 856–863 (2011).Article 

Google Scholar 
43.Garnier, E. et al. Assessing the effects of land-use change on plant traits, communities and ecosystem functioning in grasslands: A standardized methodology and lessons from an application to 11 European sites. Ann. Bot. 99, 967–985 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Pakeman, R. J. & Quested, H. M. Sampling plant functional traits: What proportion of the species need to be measured?. Appl. Veg. Sci. 10, 91–96 (2007).Article 

Google Scholar 
45.Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363 (2008).MathSciNet 
PubMed 
MATH 
Article 
PubMed Central 

Google Scholar 
46.Oksanen, J., Blanchet, F. G., Friendly, M. et al. Vegan: Community Ecology Package. R package version 2.5–7 (2020).47.Laliberté, E., Legendre, P., Shipley, B. FD: measuring functional diversity from multiple traits, and other tools for functional ecology. R package version 1.0–12 (2014). More