Ecosystem services in connected catchment to coast ecosystems: Monitoring to detect emerging trends - Christopher J. Brown; Chantal Saint Ange; Rod M. Connolly; Syezlin Hasan; Sue Jackson; Joseph M. McMahon; James C.R. Smart (Science of The Total Environment, 2023)

Interactive effects of multiple stressors vary with consumer interactions, stressor dynamics and magnitude - Mischa P. Turschwell; Sean R. Connolly; Ralf B. Schäfer; Frederik De Laender; Max D. Campbell; Chrystal Mantyka‐Pringle; Michelle C. Jackson; Mira Kattwinkel; Michael Sievers; Roman Ashauer; Isabelle M. Côté; Rod M. Connolly; et al. (Ecology Letters, 2022)
Predicting the impacts of multiple stressors is important for informing ecosystem management but is impeded by a lack of a general framework for predicting whether stressors interact synergistically, additively or antagonistically. Here, we use process‐based models to study how interactions generalise across three levels of biological organisation (physiological, population and consumer‐resource) for a two‐stressor experiment on a seagrass model system. We found that the same underlying processes could result in synergistic, additive or antagonistic interactions, with interaction type depending on initial conditions, experiment duration, stressor dynamics and consumer presence. Our results help explain why meta‐analyses of multiple stressor experimental results have struggled to identify predictors of consistently non‐additive interactions in the natural environment. Experiments run over extended temporal scales, with treatments across gradients of stressor magnitude, are needed to identify the processes that underpin how stressors interact and provide useful predictions to management.

Expert range maps of global mammal distributions harmonised to three taxonomic authorities - Charles J. Marsh; Yanina V. Sica; Connor J. Burgin; Wendy A. Dorman; Robert C. Anderson; Isabel del Toro Mijares; Jessica G. Vigneron; Vijay Barve; Victoria L. Dombrowik; Michelle Duong; Robert Guralnick; Julie A. Hart; et al. (Journal of Biogeography, 2022)
Aim Comprehensive, global information on species' occurrences is an essential biodiversity variable and central to a range of applications in ecology, evolution, biogeography and conservation. Expert range maps often represent a species' only available distributional information and play an increasing role in conservation assessments and macroecology. We provide global range maps for the native ranges of all extant mammal species harmonised to the taxonomy of the Mammal Diversity Database (MDD) mobilised from two sources, the Handbook of the Mammals of the World (HMW) and the Illustrated Checklist of the Mammals of the World (CMW). Location Global. Taxon All extant mammal species. Methods Range maps were digitally interpreted, georeferenced, error‐checked and subsequently taxonomically aligned between the HMW (6253 species), the CMW (6431 species) and the MDD taxonomies (6362 species). Results Range maps can be evaluated and visualised in an online map browser at Map of Life ( mol.org ) and accessed for individual or batch download for non‐commercial use. Main conclusion Expert maps of species' global distributions are limited in their spatial detail and temporal specificity, but form a useful basis for broad‐scale characterizations and model‐based integration with other data. We provide georeferenced range maps for the native ranges of all extant mammal species as shapefiles, with species‐level metadata and source information packaged together in geodatabase format. Across the three taxonomic sources our maps entail, there are 1784 taxonomic name differences compared to the maps currently available on the IUCN Red List website. The expert maps provided here are harmonised to the MDD taxonomic authority and linked to a community of online tools that will enable transparent future updates and version control.

Cardiovascular risk prediction in healthy older people - Johannes T. Neumann; Le T. P. Thao; Emily Callander; Enayet Chowdhury; Jeff D. Williamson; Mark R. Nelson; Geoffrey Donnan; Robyn L. Woods; Christopher M. Reid; Katrina K. Poppe; Rod Jackson; Andrew M. Tonkin; et al. (GeroScience, 2022)
Identification of individuals with increased risk of major adverse cardiovascular events (MACE) is important. However, algorithms specific to the elderly are lacking. Data were analysed from a randomised trial involving 18,548 participants ≥ 70 years old (mean age 75.4 years), without prior cardiovascular disease events, dementia or physical disability. MACE included coronary heart disease death, fatal or nonfatal ischaemic stroke or myocardial infarction. Potential predictors tested were based on prior evidence and using a machine-learning approach. Cox regression analyses were used to calculate 5-year predicted risk, and discrimination evaluated from receiver operating characteristic curves. Calibration was also assessed, and the findings internally validated using bootstrapping. External validation was performed in 25,138 healthy, elderly individuals in the primary care environment. During median follow-up of 4.7 years, 594 MACE occurred. Predictors in the final model included age, sex, smoking, systolic blood pressure, high-density lipoprotein cholesterol (HDL-c), non-HDL-c, serum creatinine, diabetes and intake of antihypertensive agents. With variable selection based on machine-learning, age, sex and creatinine were the most important predictors. The final model resulted in an area under the curve (AUC) of 68.1 (95% confidence intervals 65.9; 70.4). The model had an AUC of 67.5 in internal and 64.2 in external validation. The model rank-ordered risk well but underestimated absolute risk in the external validation cohort. A model predicting incident MACE in healthy, elderly individuals includes well-recognised, potentially reversible risk factors and notably, renal function. Calibration would be necessary when used in other populations.

Improved Accuracy for Automated Counting of a Fish in Baited Underwater Videos for Stock Assessment - Rod M. Connolly; David V. Fairclough; Eric L. Jinks; Ellen M. Ditria; Gary Jackson; Sebastian Lopez-Marcano; Andrew D. Olds; Kristin I. Jinks (Frontiers in Marine Science, 2021)
The ongoing need to sustainably manage fishery resources can benefit from fishery-independent monitoring of fish stocks. Camera systems, particularly baited remote underwater video system (BRUVS), are a widely used and repeatable method for monitoring relative abundance, required for building stock assessment models. The potential for BRUVS-based monitoring is restricted, however, by the substantial costs of manual data extraction from videos. Computer vision, in particular deep learning (DL) models, are increasingly being used to automatically detect and count fish at low abundances in videos. One of the advantages of BRUVS is that bait attractants help to reliably detect species in relatively short deployments (e.g., 1 h). The high abundances of fish attracted to BRUVS, however, make computer vision more difficult, because fish often obscure other fish. We build upon existing DL methods for identifying and counting a target fisheries species across a wide range of fish abundances. Using BRUVS imagery targeting a recovering fishery species, Australasian snapper ( Chrysophrys auratus ), we tested combinations of three further mathematical steps likely to generate accurate, efficient automation: (1) varying confidence thresholds (CTs), (2) on/off use of sequential non-maximum suppression (Seq-NMS), and (3) statistical correction equations. Output from the DL model was more accurate at low abundances of snapper than at higher abundances (>15 fish per frame) where the model over-predicted counts by as much as 50%. The procedure providing the most accurate counts across all fish abundances, with counts either correct or within 1–2 of manual counts ( R 2 = 88%), used Seq-NMS, a 45% CT, and a cubic polynomial corrective equation. The optimised modelling provides an automated procedure offering an effective and efficient method for accurately identifying and counting snapper in the BRUV footage on which it was tested. Additional evaluation will be required to test and refine the procedure so that automated counts of snapper are accurate in the survey region over time, and to determine the applicability to other regions within the distributional range of this species. For monitoring stocks of fishery species more generally, the specific equations will differ but the procedure demonstrated here could help to increase the usefulness of BRUVS.

Seagrass Restoration Is Possible: Insights and Lessons From Australia and New Zealand - Yi Mei Tan; Oliver Dalby; Gary A. Kendrick; John Statton; Elizabeth A. Sinclair; Matthew W. Fraser; Peter I. Macreadie; Chris L. Gillies; Rhys A. Coleman; Michelle Waycott; Kor-jent van Dijk; Adriana Vergés; et al. (Frontiers in Marine Science, 2020)

Collaborative research partnerships inform monitoring and management of aquatic ecosystems by Indigenous rangers - Rebecca J. Dobbs; Christy L. Davies; Michelle L. Walker; Neil E. Pettit; Bradley J. Pusey; Paul G. Close; Yoshi Akune; Ninjana Walsham; Brendan Smith; Albert Wiggan; Preston Cox; Douglas P. Ward; et al. (Reviews in Fish Biology and Fisheries, 2016)

The role of digital data entry in participatory environmental monitoring - Jeremy R. Brammer; Nicolas D. Brunet; A. Cole Burton; Alain Cuerrier; Finn Danielsen; Kanwaljeet Dewan; Thora Martina Herrmann; Micha V. Jackson; Rod Kennett; Guillaume Larocque; Monica Mulrennan; Arun Kumar Pratihast; et al. (Conservation Biology, 2016)
Many argue that monitoring conducted exclusively by scientists is insufficient to address ongoing environmental challenges. One solution entails the use of mobile digital devices in participatory monitoring (PM) programs. But how digital data entry affects programs with varying levels of stakeholder participation, from nonscientists collecting field data to nonscientists administering every step of a monitoring program, remains unclear. We reviewed the successes, in terms of management interventions and sustainability, of 107 monitoring programs described in the literature (hereafter programs) and compared these with case studies from our PM experiences in Australia, Canada, Ethiopia, Ghana, Greenland, and Vietnam (hereafter cases). Our literature review showed that participatory programs were less likely to use digital devices, and 2 of our 3 more participatory cases were also slow to adopt digital data entry. Programs that were participatory and used digital devices were more likely to report management actions, which was consistent with cases in Ethiopia, Greenland, and Australia. Programs engaging volunteers were more frequently reported as ongoing, but those involving digital data entry were less often sustained when data collectors were volunteers. For the Vietnamese and Canadian cases, sustainability was undermined by a mismatch in stakeholder objectives. In the Ghanaian case, complex field protocols diminished monitoring sustainability. Innovative technologies attract interest, but the foundation of effective participatory adaptive monitoring depends more on collaboratively defined questions, objectives, conceptual models, and monitoring approaches. When this foundation is built through effective partnerships, digital data entry can enable the collection of more data of higher quality. Without this foundation, or when implemented ineffectively or unnecessarily, digital data entry can be an additional expense that distracts from core monitoring objectives and undermines project sustainability. The appropriate role of digital data entry in PM likely depends more on the context in which it is used and less on the technology itself.

Molecular and Cytological Characterization of Centromeric Retrotransposons in a Wild Relative of Rice, Oryza granulata - Dongying Gao; Zhiyun Gong; Rod A. Wing; Jiming Jiang; Scott A. Jackson (Tropical Plant Biology, 2011)

BAC-end Sequence Analysis and a Draft Physical Map of the Common Bean (Phaseolus vulgaris L.) Genome - Jessica A. Schlueter; Jose Luis Goicoechea; Kristi Collura; Navdeep Gill; Jer-Young Lin; Yeisoo Yu; Dave Kudrna; Andrea Zuccolo; C. Eduardo Vallejos; Monica Muñoz-Torres; Matthew W. Blair; Joe Tohme; et al. (Tropical Plant Biology, 2008)

The Oryza Map Alignment Project: The Golden Path to Unlocking the Genetic Potential of Wild Rice Species - Rod A. Wing; Jetty S. S. Ammiraju; Meizhong Luo; HyeRan Kim; Yeisoo Yu; Dave Kudrna; Jose L. Goicoechea; Wenming Wang; Will Nelson; Kiran Rao; Darshan Brar; Dave J. Mackill; et al. (Plant Molecular Biology, 2005)