Optimizing automated photo identification for population assessments - Philip T. Patton; Krishna Pacifici; Robin W. Baird; Erin M. Oleson; Jason B. Allen; Erin Ashe; Aline Athayde; Charla J. Basran; Elsa Cabrera; John Calambokidis; Júlio Cardoso; Emma L. Carroll; et al. (Conservation Biology, 2025)
Several legal acts mandate that management agencies regularly assess biological populations. For species with distinct markings, these assessments can be conducted noninvasively via capture‐recapture and photographic identification (photo‐ID), which involves processing considerable quantities of photographic data. To ease this burden, agencies increasingly rely on automated identification (ID) algorithms. Identification algorithms present agencies with an opportunity—reducing the cost of population assessments—and a challenge—propagating misidentifications into abundance estimates at a large scale. We explored several strategies for generating capture histories with an ID algorithm, evaluating trade‐offs between labor costs and estimation error in a hypothetical population assessment. To that end, we conducted a simulation study informed by 39 photo‐ID datasets representing 24 cetacean species. We fed the results into a custom optimization tool to discern the optimal strategy for each dataset. Our strategies included choosing between truly and partially automated photo‐ID and, in the case of the latter, choosing the number of suggested matches to inspect. True automation was optimal for datasets for which the algorithm identified individuals well. As identification performance declined, the optimization recommended that users inspect more suggested matches from the ID algorithm, particularly for small datasets. False negatives (i.e., individual was resighted but erroneously marked as a first capture) strongly predicted estimation error. A 2% increase in the false negative rate translated to a 5% increase in the relative bias in abundance estimates. Our framework can be used to estimate expected error of the abundance estimate, project labor effort, and find the optimal strategy for a dataset and algorithm. We recommend estimating a strategy's false negative rate before implementing the strategy in a population assessment. Our framework provides organizations with insights into the conservation benefits and consequences of automation as conservation enters a new era of artificial intelligence for population assessments.

Multinational evaluation of genetic diversity indicators for the Kunming‐Montreal Global Biodiversity Framework - Alicia Mastretta‐Yanes; Jessica M. da Silva; Catherine E. Grueber; Luis Castillo‐Reina; Viktoria Köppä; Brenna R. Forester; W. Chris Funk; Myriam Heuertz; Fumiko Ishihama; Rebecca Jordan; Joachim Mergeay; Ivan Paz‐Vinas; et al. (Ecology Letters, 2024)
Under the recently adopted Kunming‐Montreal Global Biodiversity Framework, 196 Parties committed to reporting the status of genetic diversity for all species. To facilitate reporting, three genetic diversity indicators were developed, two of which focus on processes contributing to genetic diversity conservation: maintaining genetically distinct populations and ensuring populations are large enough to maintain genetic diversity. The major advantage of these indicators is that they can be estimated with or without DNA‐based data. However, demonstrating their feasibility requires addressing the methodological challenges of using data gathered from diverse sources, across diverse taxonomic groups, and for countries of varying socio‐economic status and biodiversity levels. Here, we assess the genetic indicators for 919 taxa, representing 5271 populations across nine countries, including megadiverse countries and developing economies. Eighty‐three percent of the taxa assessed had data available to calculate at least one indicator. Our results show that although the majority of species maintain most populations, 58% of species have populations too small to maintain genetic diversity. Moreover, genetic indicator values suggest that IUCN Red List status and other initiatives fail to assess genetic status, highlighting the critical importance of genetic indicators.

Homoeology of Thinopyrum junceum and Elymus rectisetus chromosomes to wheat and disease resistance conferred by the Thinopyrum and Elymus chromosomes in wheat - Rachel I. McArthur; Xianwen Zhu; Rebekah E. Oliver; Daryl L. Klindworth; Steven S. Xu; Robert W. Stack; Richard R.-C. Wang; Xiwen Cai (Chromosome Research, 2012)

Molecular cytogenetic characterization of four partial wheat-Thinopyrum ponticum amphiploids and their reactions to Fusarium head blight, tan spot, and Stagonospora nodorum blotch - R. E. Oliver; S. S. Xu; R. W. Stack; T. L. Friesen; Y. Jin; X. Cai (Theoretical and Applied Genetics, 2006)

DNA markers for Fusarium head blight resistance QTLs in two wheat populations - J. A. Anderson; R. W. Stack; S. Liu; B. L. Waldron; A. D. Fjeld; C. Coyne; B. Moreno-Sevilla; J. Mitchell Fetch; Q. J. Song; P. B. Cregan; R. C. Frohberg (Theoretical and Applied Genetics, 2001)

Reaction Of Spring Wheats Incorporating Sumai#3-Derived Resistance to Inoculation with Seven Fusarium Species - R. W. Stack; R. C. Frohberg; H. Casper (Cereal Research Communications, 1997)

Fusarium-Damaged Kernels and Don in the 1993 Wheat Crop in Eastern North Dakota and Northwestern Minnesota, USA - R. W. Stack; M. P. McMullen; H. Casper (Cereal Research Communications, 1997)