Ecology

SamplesWe examined 1,022 avian species (~ 10% recorded species) in this study, representing 418 genera, from 91 families (37% recorded families) and 29 orders (73% of all orders). Specimens were from the skin collection of the World Museum Liverpool, Tring Natural History Museum, Manchester Museum and Wollaton Hall Museum, all situated in the United Kingdom. All work was carried out in accordance with ethical regulations at Manchester Metropolitan University and with the permission of all aforementioned museums. Only the best-preserved adult specimens (no signs of cut off feathers or holes in the skin near the beak) were chosen for this study to ensure accurate measurements of bristle length, shape and presence, which should not be affected by the process of skin removal and specimen conservation. Species were randomly chosen, without targeting our sampling towards species known a priori to have bristles. Where possible, two specimens per species were measured (occurring in 82% of all species examined). Specimens of each sex were measured when present; however, this was not always possible since labelling was often inaccurate or missing. In total, the sample included 508 males, 412 females and 374 individuals of unknown sex. Both sexes were examined in 274 species and there was no difference whatsoever between the presence of bristles on male or female species (n = 97 with bristles present and n = 180 with bristles absent for both males and females). Length (Mann–Whitney U test, W = 37,962, N = 552, P = 0.94) and shape (Chi-square test, χ2 = 0, N = 552, df = 3, P = 1) of rictal bristles also did not significantly differ between males and females. Therefore, rictal bristles are likely to be sexually monomorphic and data for males and females was pooled for further analyses. Overall, rictal bristles were absent in 64% of species examined (n = 656) and just over a third of species (n = 366) had bristles present.Bristle descriptionsFacial bristles were initially identified by sight and touch in each specimen. Bristles were recorded as either present or absent from the upper rictal, lorial, lower rictal, narial and interramal regions (Fig. 1a). We use the term ‘rictal bristle’ here for bristles on both the upper rictal and/or the lorial region, since there was no clear differentiation and morphological differences between the bristles found in these regions forming a continuum of bristles above the edge of the beak. When present, rictal bristle shape was recorded as: (i) unbranched rictal bristles, (ii) rictal bristles with barbs only at the base (“Base”) and (iii) branched rictal bristles (“Branched”), i.e. barbs and barbules present along the bristle rachis (Fig. 1b). The three longest rictal bristles were measured on both sides of the head of each specimen using digital callipers, and these lengths were averaged to provide a mean length of rictal bristles per species. In species lacking rictal bristles, a length of “0” and a shape category of “Absent” was recorded.Ancestral reconstruction of facial bristle presenceFollowing Felice et al.19, a single consensus phylogenetic tree was generated from the Hackett posterior distribution of trees from Birdtree.org20 with a sample size of 10,000 post burn-in, using the TreeAnnotator utility in BEAST software21 with a burn-in of 0. Maximum Clade Credibility (MCC) with the option “-heights ca” was selected as the method of reconstruction. The common ancestor trees option (-heights ca) builds a consensus tree by summarising clade ages across all posterior trees. Both the consensus tree and posterior distribution of 10,000 trees were imported into RStudio v. 1.2.5 for R22,23 and pruned so that only species present in the dataset of this study remained in the phylogeny. Taxon names were modified where necessary to match those from the Birdtree.org (http://birdtree.org) species record. Negative terminal branches in our consensus tree were slightly lengthened to be positive using ‘edge.length[tree$edge.length  More

Pristine ecosystems such as mangrove forests protect against the effects of climate change.Credit: Karine Aigner/Nature Picture Library

The Paris climate agreement, signed in December 2015, ranks as one of the most momentous global treaties ever negotiated, setting a crucial goal to seek to limit warming to 1.5–2 °C above pre-industrial levels. At the time, the opening ceremony of the COP21 climate-change conference that led to the agreement also held the record for the largest number of world leaders ever to attend a United Nations event in a single day — more than 150. The two things are probably more than coincidence.Now biodiversity is hoping for its Paris moment. The long-delayed COP15 conference, starting on 7 December in Montreal, Canada, aims to seal a bold new international deal committing countries to precise targets to curb species loss and to protect and restore nature.Many factors suggest the time is ripe. The problem of biodiversity loss is more prominent than ever before. As ecologist Sandra Díaz wrote in Nature last week, researchers have assembled the strongest evidence base yet ahead of COP15, the Fifteenth Conference of the Parties to the Convention on Biological Diversity (S. Díaz Nature 612, 9; 2022). Initiatives such as the Dasgupta Review, commissioned by the UK government, have made plain that the protection of biodiversity is an economic necessity.
COP15 biodiversity plan risks being alarmingly diluted
There is also much greater public awareness of how pollution and habitat destruction threaten the health of ecosystems on which we depend for food, clean water and disease prevention, and a better understanding of nature’s crucial role in mitigating climate change — for example, by storing carbon in soils and trees — as well as in helping us to adapt to its impacts. Mangrove forests, for instance, are hugely effective in stopping influxes of seawater from tsunamis and sea-level rise.But when it comes to getting stalled negotiations motoring again, the scale of support by world leaders that was a feature of climate’s road to Paris is currently lacking.Change cannot come too soon. Nature is on the brink. Of 20 decadal targets to preserve nature that were set in Aichi, Japan, in 2010, not a single one had been fully met by 2020. That, coupled with underfunding and lack of regard for the rights of Indigenous peoples who steward much of the world’s remaining biodiversity, means more species than ever are at risk of extinction. Serious impacts on human wealth and health from biodiversity loss loom ever larger. Yet over the past three years, four difficult rounds of negotiations aiming to agree on a framework to replace Aichi have not yielded results. Hundreds of issues remain unresolved.
COVID delays are frustrating the world’s plans to save biodiversity
Many experts worry that the lacklustre progress made at COP27, the climate summit held last month in Sharm El-Sheikh, Egypt, augur badly for the biodiversity meeting. But there is also reason for hope. The agreement made at COP27 to establish a ‘loss and damage’ fund to compensate low- and middle-income countries (LMICs) for climate impacts indicates that richer nations are open to talking about funding, which has also been a major sticking point in biodiversity negotiations.Global funding for biodiversity is severely in the red. A UN estimate published last week suggests that only US$154 billion per year flows to ‘nature-based solutions’ from all sources, including government aid and private investment — a number the UN says needs to triple by 2030. Many LMICs — which are home to much of the world’s remaining biodiversity — would like rich nations to put fresh finance into a new multilateral fund. One option is that such a fund could compensate LMICs for bio-diversity loss and associated damages driven by the consumption of products in rich nations through international trade.A second major sticking point is how to fairly and equitably share the benefits of digital sequence information — genetic data collected from plants, animals and other organisms. Communities in biodiversity-rich regions where genetic material is collected have little control over the commercialization of the data, and no way to recoup financial or other benefits. A multipurpose fund for bio-diversity could provide a simple and effective way to share the benefits of these data and support other conservation needs of LMICs.Another reason to hope for a breakthrough is the forthcoming change in Brazil’s leadership. Conservation organizations such as the wildlife charity WWF have accused the world’s most biodiverse nation of deliberately obstructing previous negotiations, holding up agreement on targets such as protecting at least 30% of the world’s land and seas by 2030. But Brazil’s incoming president, Luiz Inácio Lula da Silva, has signalled that the environment is one of his top priorities. Although he does not take over until January 2023, he is thought to be sending an interim team of negotiators to Montreal.
Crucial biodiversity summit will go ahead in Canada, not China: what scientists think
All negotiators face a Herculean task to get a deal over the line at COP15, with many issues in the text still unresolved and contested. What’s needed above all is global leadership to empower national negotiators to reach a strong deal, including a new fund of some kind for biodiversity. More than 90 heads of state and heads of government have signed a pledge to tackle the nature crisis. At the time of writing, only Justin Trudeau, the host nation’s prime minster, has confirmed that he is to attend in person.The no-shows send the wrong signal. It’s also true at the time of writing that neither Canada nor China — the original intended host of COP15 and still the meeting’s chair — has issued formal invitations. But leaders have regularly attended climate COPs for more than a decade. This shows in the ambition of climate agreements, if not in their implementation. Research communities and civil society must continue to pressure leaders to engage similarly with the biodiversity agenda. Otherwise, the world risks failing to grasp this opportunity to secure the kind of ambitious deal that nature — and humanity — desperately needs. More

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Parties to the Convention on Biological Diversity will meet this month to finalize the post-2020 Global Biodiversity Framework and the text for the stand-alone target on gender equality (Target 22). This target aims to reshape conservation policy and practice to make them more inclusive, equitable and effective.
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The categorisation system was developed through an iterative procedure including a careful evaluation at each stage. This was necessary because each of three rounds yielded substantial feedback from the expert taggers, identifying issues to be resolved and proposing improvements to the system. This process led to a much clearer understanding of the structure of sensitive data resources and a wider agreement on definitions to be applied in the tagging process. In addition, the allocation of exactly one tag per category improved during the development for many categories, indicating that the selection process was straightforward for most resources and categories. As a result, the categorisation system could be simplified and the structure improved, appropriately representing a trans-disciplinary effort. This may also be important from the user perspective. At the end of the day, the system should be so intuitive that the users searching for terms would have the same logic as the experts entered the tags.To be beneficial for the domain of LS, the categorisation system and the toolbox requires broad community approval38,39. In the project, we began the approval process with nominated experts from 6 LS RIs, embedded in a larger working group of the H2020-funded project EOSC-Life, covering 13 LS RIs. Though this can be seen as a useful starting point, the toolbox obviously needs community approval at a much larger scale. As the categorisation system is specifying a part of essential metadata for resources about sensitive data, it will be relevant to the FAIR Digital Objects (FDO) Forum for a « resources in the life sciences » FDO. The categorisation system can be used to derive FDO attributes and values for such FDOs. FDOs for the sensitive data itself, when levels of sensitivity and specific access protocols need to be specified is an interesting possible extension, and the categorisation system could support as a backbone information for access governance and technical choices. FDOs are to be “machine actionable”, so desirable mappings between different categorisation systems will be operationalisable. New European projects such as FAIRCORE4EOSC (https://faircore4eosc.eu/), FAIR-IMPACT (https://fair-impact.eu/) and other projects working on pragmatic semantic improvements for FAIR appliance will provide possibilities for registering metadata schemas and mappings that should reuse interdisciplinary approaches in the heterogeneous field of life sciences.The RDA has established and is maintaining a Metadata Standards Catalogue (MSC) (https://rdamsc.bath.ac.uk/mapping-index,5). An appropriate goal for the categorisation system would be to be included in this catalogue, after further refinement and alignment with other vocabularies addressing sensitive data in the life sciences. In any case, the work on the categorisation system can contribute to discussions on methodologies for aligning metadata schemas across scientific domains, while the categorisation system itself can be seen as an important contribution to the process of developing the most useful and appropriate cross-disciplinary terms and categories for describing sensitive data. We keep in mind that similar approaches have been applied via long and iterative processes in other scientific domains, such as understanding and predicting the evolution of climate (essential climate variables, https://public.wmo.int/en/programmes/global-climate-observing-system/essential-climate-variables) and essential biodiversity variables for mapping and monitoring species populations40. There are biases and gaps in the existing system that need to be tackled in the future. The initial content of the toolbox demonstrator, consisting of 110 resources related to sensitive data, has been primarily selected by four RIs with a focus on clinical and biomedical research (BBMRI, EATRIS, ECRIN, Euro-Bioimaging). Other areas and sensitive data types, such as environmental, classified, and proprietary data are under-represented, as are some disciplines, such as zoology, ecology, plant and mycological sciences, and microbiology. This indicates a need for a broader coverage of resources linked to sensitive data in the future work. Another question that needs to be investigated is how interoperable the categorisation system is with other domains outside the LS that systematically deal with sensitive data, for example, the Social Science and Humanities41). In addition, systematic data on the usability/user-friendliness of the toolbox from a broad sample of potential users from the life sciences are needed. Initial and informal evaluation of these aspects by the experts involved so far has been very positive but is clearly limited in scale and needs to be supplemented by more evidence.There are major challenges to the sharing of sensitive data, including interoperability, accessibility, and governance. The primary objective of the toolbox is to improve discoverability of resources and digital objects linked to the sharing and re-use of sensitive data (F in FAIR)4. The systematic application of a standardised typology for resources about sensitive data, as defined by the categorisation system, helps to better structure, and organise the issues and results in metadata enrichment (F4, R1.3 of the FAIR principles in Supplementary, Table S1). The toolbox alone will not be enough for the ‘I’ of the FAIR principles, but it may become a useful backbone for building more interoperable classification systems for sensitive data resources.It is perhaps more common to base findability on a tagging system using keywords (plus title text). That is, for example, how PubMed works—it does not categorise resources, it adds MESH terms to them (https://pubmed.ncbi.nlm.nih.gov/). Another option would have been to try to derive keywords from text or title. In our case, a categorisation system with pre-defined dimensions and pre-listed tags was preferred by the expert group. Keywords, in isolation, suffer from several disadvantages:

(a)

A range of equivalent terms may be used to mean the same thing – making searching for that concept difficult, requiring multiple ‘Or’ statements.

(b)

They may have multiple meanings (polysemy) especially if “drawn from”, or “applied to”, a wide range of scientific disciplines.

(c)

The different aspects of the resource covered by keywords, i.e., the types or dimensions of keyword applied, may be inconsistent and / or incomplete.

The categorisation system, on the other hand, guarantees that all 7 validated dimensions required are used in the tagging process and that the tags selected are standardised and defined. The toolbox categories also aid browsing of results by enabling sequential filtering using the categories and tags.In addition, there is a useful link between developing community approved categories for metadata, in this case for characterising resources dealing with sensitive data, and community understood (but implicit) ontologies used in the same area. Categories and ontologies can complement each other—without a common underlying ontology, metadata terms can be interpreted inconsistently, and without defining metadata categories, ontologies may remain implicit and inconsistent. We found, for example, that discussions on the best categorisation to use for scientific disciplines, or data types, exposed the implicit (and different) ontologies being used by different people and is based on the personal views of those in the group. Those would have been obviously rooted in and / or influenced by the language and working assumptions of their discipline(s), and their roles and experiences, (current and previous). That will be more and more the case with interdisciplinary research development and development in research careers. Developing categories in metadata can therefore play an important role in describing, understanding and, ultimately, harmonising the implicit ontologies scientists use in thinking about the area of sensitive data.In the development of the categorisation system, existing ontologies, classifications, and terminologies were taken into consideration (Table 2). However, many more have relationships to the categorisation system. An example is the Subject Resource Application Ontology (SRAO), an application ontology describing subject areas/academic disciplines used within FAIRsharing records by curators and the user community42. A first crosswalk has demonstrated considerable agreement between the toolbox category “research field” and subsections of SRAO42 and EDAM15. The toolbox has been registered as a resource (database) at FAIRsharing, a curated, informative, and educational resource on data and metadata standards, inter-related to databases and data policies (https://fairsharing.org/3577). It is planned to create a collection group of resources (standards, databases, policies) in FAIRsharing linked to the toolbox and the underlying categorisation system. This will also cover relationships to ontologies and classifications.There is a need to explore the applicability of the toolbox to specific domains. One example could be the European Joint Programme on Rare Diseases (EJP RD), where resources are made progressively FAIR at the record level to support innovative basic, translational and clinical research (https://www.ejprarediseases.org/coordinated-access-data-services/fairification-support/). The goal is to identify, refine and expose core standards for dataset interoperability, asset (data, sample, subject) discovery, and responsible data sharing, concentrating on data level rather than resource level information. Knowledge exchange between EJP RD and the toolbox could be of benefit in exploring the complementary of both approaches in adequately characterising resources linked to sensitive data and thus improving data discoverability.The first pilot study demonstrated major variation in tagging of resources if independent taggers are assessing the same resource (inter-observer variation). The example of BBMRI has shown that this variation can be considerably reduced if adequate training is performed; which in return is resource intense. Thus, to arrive at a valid and reliable tagging process, there is a necessity for adequate training and support to reduce inter-observer variation. Specific training sets and training programs as well as intercalibration tools need to be developed and implemented and approved by the community.Another option to be explored should be AI—or ML-algorithms to support automatic (or at least semi-automatic) tagging of resources. It is not easy to use AI/ML in this field due to the multilingualism and the misinterpretation of terms. Often there are different meanings between scientific disciplines and a common backbone for the application of AI/ML is difficult to achieve. It is necessary to come to a common understanding between people involved in the assessment of resources related to sensitive data in all life sciences. Nevertheless, the toolbox can become of major importance for research and application of AI/ML techniques in this field. It may serve as a resource for AI/ML to better find resources in the field by serving as a kind of gold standard to compare with. Another promising approach would be to consider a knowledge graph as an intelligent representation. For the categorisation system the approach could be used to interlink categories to a resource (e.g., “source related to sensitive data” has “geographical scope”) and to link individual tags between categories if possible (e.g., “clinical research data” result from “clinical research”). This would give a richer representation of the knowledge behind the categorisation system and the option to be integrated in existing approaches (e.g., OpenAIRE, https://www.openaire.eu/). Therefore, we will consider knowledge graphs as an intelligent knowledge representation of the categorisation system in the future.A major challenge will be the transition of the toolbox demonstrator to a mature toolbox and ultimately its maintenance, extension, and sustainability. Development of the toolbox demonstrator has been financed by EOSC-Life, but this project will end in 2023. Discussion on sustainability has been initiated with several life-science infrastructures (e.g., BBMRI, EATRIS, ECRIN and ELIXIR, another European Life-Science Infrastructure). Key aspects of sustainability that need to be considered are maintenance of the toolbox portal and tagging tool and of the toolbox content including expert time for tagging as well as human resources to maintain the system. Different approaches are under evaluation: an organization considering the resource core to its operations and taking full responsibility, or a joint ownership across multiple organisations (e.g., multiple RIs) or a community taking responsibility, either funded by future grants or through in-kind contributions from motivated research parties/individuals. Further costs to be covered will include system maintenance, input from a toolbox manager, tagging of resources by experts, as well as advertisement to the envisioned user groups, hardware costs and costs for debugging and major extension of functionality if needed. More

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