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Record W4321496433 · doi:10.1111/acv.12865

Towards a standardized framework for managing lost species

2023· article· en· W4321496433 on OpenAlexaff
Thomas E. Martin, G. C. Bennett, Andrew Fairbairn, Arne Ø. Mooers

Bibliographic record

VenueAnimal Conservation · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGeographyEcologyBiology

Abstract

fetched live from OpenAlex

We thank Biggs, Long, & Rodríguez (2023) and Fisher (2023) for their thoughtful commentaries on our simple overview of lost terrestrial vertebrate species (Martin et al., 2022). We agree with these authors that reducing the Latimerian knowledge shortfall is dependent on both co-ordinated fieldwork efforts and the development of a well-managed, standardized framework for administering lost species data. We focus this response on further discussion of such a framework and highlight some of the challenges involved. We agree that important steps towards developing a robust lost species management framework include a systematic expansion of last-seen dates on IUCN Red List accounts, standardizing terminology regarding lost species (Long & Rodríguez, 2022), and curating a single, authoritative list. We also agree with including species that have been lost for less than 50 years on such a list. As a recent example, the Critically Endangered Bahama Nuthatch (Sitta insularis) was last observed <4 years ago at the time of writing, but there are fears it could already be extinct following the impacts of Hurricane Dorian in 2019 (Mlodinow et al., 2021). Highlighting such species of conservation concern after a short absence can be valuable. However, the call of Biggs et al. (2023) to produce an annually updated list of all taxa unobserved for >5 years may prove challenging for several reasons. First, systematically obtaining data on species missing for shorter time periods (e.g. 5 years) may prove difficult. As Fisher (2023) reminds us, most of the players involved (including the Species Specialist Groups that coordinate the red list assessments) are volunteers, such that new species accounts are often asynchronously updated on longer time scales - unless, like the Bahama Nuthatch, the species in question is of high conservation concern. For more conspicuous taxa, citizen scientist platforms may provide useful data on recent records, although limitations of citizen scientists' detection skills may be a real impediment for more cryptic taxa (Kremen, Ullmann, & Thorp, 2011). Indeed, groups sufficiently cryptic to be dependent on expert-level identification are rarely uploaded to such platforms (Haelewaters et al., n.d.). Second, the number of species in need of curation increases rapidly when shifting to shorter timeframes and when including taxa beyond terrestrial vertebrates. In addition, large proportions of under-studied taxa are likely to de facto qualify as lost species. For example, a case study of a poorly-researched fungi taxon (Laboulbeniomycetes) by Haelewaters et al. (n.d.) found that, from a sample of 1117 species, 73% have no published records in the literature or on online data platforms after their initial description, and 51% had not been observed in >50 years. If similar patterns occur elsewhere (e.g., in marine invertebrates), this would present an extremely unwieldy number of lost species to administer. It may also be difficult to assign categorical reasons behind the lost status of many of these cryptic species, given so little information is known about them. Finally (and not independently), many groups of organisms possess a complex and often chaotic taxonomy (Garnett & Christidis, 2017). Even generally well-studied taxa have their problems: Fisher (2023) points out that some 40 species of mammals had only a single record by 2012; many of these would be the putative type specimen. From our database (based on IUCN data for species unobserved for >50 years) we identified 47 mammals (slightly higher than reported by Fisher; in likelihood due to subsequent taxonomic splitting), three birds, 54 amphibians, and 123 reptiles known only from their holotypes; many of these could also possess dubious taxonomies. Another issue to consider is what ‘counts’ as a species rediscovery (Fisher, 2023). Long & Rodríguez (2022) suggest guidelines, but nuances may require further discussion. Peer-reviewed publications or information on Red List accounts remain a gold standard for this, but these may not always be available, especially for species rediscovered in shorter timeframes. Is direct physical evidence via a specimen, photograph, or video necessary (with consideration to the fact that even such media can be controversial – see, e.g., discussion in Troy & Jones, 2022)? What about expert observations without supporting evidence? Can sound recordings, tracks and signs, or eDNA signals provide sufficient evidence in isolation? Are records on citizen science platforms such as iNaturalist (https://www.inaturalist.org/) acceptable? And indeed, are strict guidelines regarding what constitutes a rediscovery desirable, or is it better to assess data on a case-by-case basis, e.g., via a committee? Biggs et al. (2023) highlight important actions towards developing a standardized framework for the management of lost species, and Fisher (2023) reminds us of the strengths and weaknesses of the Red List. We suggest that further steps could involve decision making on which species to prioritize, how to refine the ‘rules’ on rediscovery, and where best to curate an authoritative list. Given the importance of Red List ‘last seen’ dates for keeping track of many lost species, and because species included on the Red List benefit from having a set taxonomy and at least some published information available (not least regarding their conservation status), it may be practical for a centralized list to primarily focus on species with a Red List account (at least initially). This would still represent a daunting undertaking given the IUCN curates data on >42,100 species, but is probably realistic. Indeed, given the central importance of the IUCN and its IT infrastructure for both obtaining data on lost species and for implementing recommendations regarding the status of these species, it may be advantageous for any decision-making committee on lost species to be integrated into the organization, perhaps through the creation of an IUCN-sanctioned Specialist Group or Task Force under the Species Survival Commission. Regardless of who convenes this, the establishment of a global standardized framework for the management of lost species seems a worthy, perhaps pressing, endeavour, and we hope the IUCN, organizations like Re:Wild (Biggs et al., 2023), specialist field scientists (see, e.g., https://www.lostsharkguy.com/about), and conservation experts will help spearhead the process. Given what we know (Fisher, 2023; Martin et al., 2022), we could start with Reptilia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.197

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.114
GPT teacher head0.273
Teacher spread0.159 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
Has abstractyes

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