Maternal care and early rearing environment influence puppy behaviour and cognition - Emily E. Bray; Laura E.L.C. Douglas; Kerinne M. Levy; Gitanjali E. Gnanadesikan; Daniel J. Horschler; Brenda S. Kennedy; Evan L. MacLean (Animal Behaviour, 2026)

Multimorbidity as a predictor of mortality in companion dogs - Eric R. Foreman; Laura Corlin; Kate E. Creevy; Jessica M. Hoffman; Joshua M. Akey; Rozalyn M. Anderson; Elhanan Borenstein; Marta G. Castelhano; Amanda E. Coleman; Matthew D. Dunbar; Virginia R. Fajt; Erica C Jonlin; et al. (GeroScience, 2026)

A retrospective analysis over 25 years of modelling corn ethanol in the US to inform prospective life cycle assessment - Praveen Siluvai Antony; Joule Bergerson; Heather L. MacLean; Bradley A. Saville; I. Daniel Posen (The International Journal of Life Cycle Assessment, 2026)

Indiscriminate sociality: puppies do not preferentially re-engage a human partner after joint social play is interrupted - Dara S. Jonkoski; Laura E.L.C. Douglas; Daniel J. Horschler; Alyssa M. Klensin; Brenda S. Kennedy; Evan L. MacLean; Emily E. Bray (Animal Behaviour, 2026)

Functional assessments of short-term spatial memory in the Dog Aging Project identify strong associations with age that are not moderated by body mass - Stephanie H. Hargrave; Amber J. Keyser; Emma Kristal; Gene E. Alexander; Theadora A. Block; Emily E. Bray; Laura E. L. C. Douglas; Brenda S. Kennedy; Daniel E. L. Promislow; David A. Raichlen; Dog Aging Project Consortium; Evan L. MacLean (GeroScience, 2025)

Aquatic occurrence, fate and potential ecotoxicity of insecticide and fungicide residues originating from a biofuels production facility using pesticide-treated seeds - Jascika A.A. Maclean; Shannon Bartelt-Hunt; Daniel D. Snow; Josephus F. Borsuah; Raquel Wielens Becker; Moushumi Hazra (Journal of Hazardous Materials, 2025)

Test of Rapamycin in Aging Dogs (TRIAD): study design and rationale for a prospective, parallel-group, double-masked, randomized, placebo-controlled, multicenter trial of rapamycin in healthy middle-aged dogs from the Dog Aging Project - Amanda E. Coleman; Kate E. Creevy; Rozalyn Anderson; May J. Reed; Virginia R. Fajt; Kathleen M. Aicher; Genna Atiee; Brian G. Barnett; Ryan D. Baumwart; Beth Boudreau; Suzanne M. Cunningham; Matthew D. Dunbar; et al. (GeroScience, 2025)

How do we decarbonize one billion vehicles by 2050? Insights from a comparative life cycle assessment of electrifying light-duty vehicle fleets in the United States, China, and the United Kingdom - Da Huo; Ben Davies; Jianxin Li; Nadine Alzaghrini; Xin Sun; Fanran Meng; Amir F.N. Abdul-Manan; Jon McKechnie; I. Daniel Posen; Heather L. MacLean (Energy Policy, 2024)

Characterizing dog cognitive aging using spontaneous problem-solving measures: development of a battery of tests from the Dog Aging Project - Stephanie H. Hargrave; Emily E. Bray; Stephanie McGrath; Gene E. Alexander; Theadora A. Block; Naomi Chao; Martin Darvas; Laura E. L. C. Douglas; Janet Galante; Brenda S. Kennedy; Breonna Kusick; Julie A. Moreno; et al. (GeroScience, 2024)

New cathodes now, recycling later: Dynamic scenarios to reduce battery material use and greenhouse gas emissions from U.S. light-duty electric vehicle fleet - Bassel Tarabay; Alexandre Milovanoff; Amir F.N. Abdul-Manan; Jon McKechnie; Heather L. MacLean; I. Daniel Posen (Resources, Conservation and Recycling, 2023)

Associations between physical activity and cognitive dysfunction in older companion dogs: results from the Dog Aging Project - Emily E. Bray; David A. Raichlen; Kiersten K. Forsyth; Daniel E. L. Promislow; Gene E. Alexander; Evan L. MacLean; Joshua M. Akey; Brooke Benton; Elhanan Borenstein; Marta G. Castelhano; Amanda E. Coleman; Kate E. Creevy; et al. (GeroScience, 2023)

Global maps of soil temperature - Jonas J. Lembrechts; Johan van den Hoogen; Juha Aalto; Michael B. Ashcroft; Pieter De Frenne; Julia Kemppinen; Martin Kopecký; Miska Luoto; Ilya M. D. Maclean; Thomas W. Crowther; Joseph J. Bailey; Stef Haesen; et al. (Global Change Biology, 2022)
Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1‐km 2 resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1‐km 2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse‐grained air temperature estimates from ERA5‐Land (an atmospheric reanalysis by the European Centre for Medium‐Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome‐specific offsets emphasize that the projected impacts of climate and climate change on near‐surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil‐related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

Quantifying environmental impacts of primary aluminum ingot production and consumption: A trade‐linked multilevel life cycle assessment - Alexandre Milovanoff; I. Daniel Posen; Heather L. MacLean (Journal of Industrial Ecology, 2021)

Global variation in diurnal asymmetry in temperature, cloud cover, specific humidity and precipitation and its association with leaf area index - Daniel T. C. Cox; Ilya M. D. Maclean; Alexandra S. Gardner; Kevin J. Gaston (Global Change Biology, 2020)
The impacts of the changing climate on the biological world vary across latitudes, habitats and spatial scales. By contrast, the time of day at which these changes are occurring has received relatively little attention. As biologically significant organismal activities often occur at particular times of day, any asymmetry in the rate of change between the daytime and night‐time will skew the climatic pressures placed on them, and this could have profound impacts on the natural world. Here we determine global spatial variation in the difference in the mean annual rate at which near‐surface daytime maximum and night‐time minimum temperatures and mean daytime and mean night‐time cloud cover, specific humidity and precipitation have changed over land. For the years 1983–2017, we derived hourly climate data and assigned each hour as occurring during daylight or darkness. In regions that showed warming asymmetry of >0.5°C (equivalent to mean surface temperature warming during the 20th century) we investigated corresponding changes in cloud cover, specific humidity and precipitation. We then examined the proportional change in leaf area index (LAI) as one potential biological response to diel warming asymmetry. We demonstrate that where night‐time temperatures increased by >0.5°C more than daytime temperatures, cloud cover, specific humidity and precipitation increased. Conversely, where daytime temperatures increased by >0.5°C more than night‐time temperatures, cloud cover, specific humidity and precipitation decreased. Driven primarily by increased cloud cover resulting in a dampening of daytime temperatures, over twice the area of land has experienced night‐time warming by >0.25°C more than daytime warming, and has become wetter, with important consequences for plant phenology and species interactions. Conversely, greater daytime relative to night‐time warming is associated with hotter, drier conditions, increasing species vulnerability to heat stress and water budgets. This was demonstrated by a divergent response of LAI to warming asymmetry.

The problem of scale in predicting biological responses to climate - Luca Bütikofer; Karen Anderson; Daniel P. Bebber; Jonathan J. Bennie; Regan I. Early; Ilya M. D. Maclean (Global Change Biology, 2020)
Many analyses of biological responses to climate rely on gridded climate data derived from weather stations, which differ from the conditions experienced by organisms in at least two respects. First, the microclimate recorded by a weather station is often quite different to that near the ground surface, where many organisms live. Second, the temporal and spatial resolutions of gridded climate datasets derived from weather stations are often too coarse to capture the conditions experienced by organisms. Temporally and spatially coarse data have clear benefits in terms of reduced model size and complexity, but here we argue that coarse‐grained data introduce errors that, in biological studies, are too often ignored. However, in contrast to common perception, these errors are not necessarily caused directly by a spatial mismatch between the size of organisms and the scale at which climate data are collected. Rather, errors and biases are primarily due to (a) systematic discrepancies between the climate used in analysis and that experienced by organisms under study; and (b) the non‐linearity of most biological responses in combination with differences in climate variance between locations and time periods for which models are fitted and those for which projections are made. We discuss when exactly problems of scale can be expected to arise and highlight the potential to circumvent these by spatially and temporally down‐scaling climate. We also suggest ways in which adjustments to deal with issues of scale could be made without the need to run high‐resolution models over wide extents.

Life cycle assessment of emerging technologies: Evaluation techniques at different stages of market and technical maturity - Joule A. Bergerson; Adam Brandt; Joe Cresko; Michael Carbajales‐Dale; Heather L. MacLean; H. Scott Matthews; Sean McCoy; Marcelle McManus; Shelie A. Miller; William R. Morrow; I. Daniel Posen; Thomas Seager; et al. (Journal of Industrial Ecology, 2020)
Life cycle assessment (LCA) analysts are increasingly being asked to conduct life cycle‐based systems level analysis at the earliest stages of technology development. While early assessments provide the greatest opportunity to influence design and ultimately environmental performance, it is the stage with the least available data, greatest uncertainty, and a paucity of analytic tools for addressing these challenges. While the fundamental approach to conducting an LCA of emerging technologies is akin to that of LCA of existing technologies, emerging technologies pose additional challenges. In this paper, we present a broad set of market and technology characteristics that typically influence an LCA of emerging technologies and identify questions that researchers must address to account for the most important aspects of the systems they are studying. The paper presents: (a) guidance to identify the specific technology characteristics and dynamic market context that are most relevant and unique to a particular study, (b) an overview of the challenges faced by early stage assessments that are unique because of these conditions, (c) questions that researchers should ask themselves for such a study to be conducted, and (d) illustrative examples from the transportation sector to demonstrate the factors to consider when conducting LCAs of emerging technologies. The paper is intended to be used as an organizing platform to synthesize existing methods, procedures and insights and guide researchers, analysts and technology developer to better recognize key study design elements and to manage expectations of study outcomes.

Requirements for labelling forest polygons in an object-based image analysis classification - Meghan Graham MacLean; Michael J. Campbell; Daniel S. Maynard; Mark J. Ducey; Russell G. Congalton (International Journal of Remote Sensing, 2013)

Co-regulation of nuclear genes encoding plastid ribosomal proteins by light and plastid signals during seedling development in tobacco and Arabidopsis - Daniel MacLean; Cheryl A. Jerome; Anthony P. C. Brown; John C. Gray (Plant Molecular Biology, 2008)

Disruption of essential plastid gene expression caused by T7 RNA polymerase-mediated transcription of plastid transgenes during early seedling development - Alan M. Magee; Daniel MacLean; John C. Gray; Tony A. Kavanagh (Transgenic Research, 2007)

Effects of long-term creatine feeding and running on isometric functional measures and myosin heavy chain content of rat skeletal muscles - Maria Gallo; Tessa Gordon; Daniel Syrotuik; Yang Shu; Neil Tyreman; Ian MacLean; Zoltan Kenwell; Charles T. Putman (Pflügers Archiv - European Journal of Physiology, 2006)

s of papers presented at the 81st annual meeting of The Potato Association of America Charlottetown, P.E.I., Canada August 3 – 7, 1997 - S. T. Ali-Khan; C. Hetner; L. Ladislav; V. Currie; Walter J. Arsenault; A. Naseer Aziz; Janet E. A. Seabrook; George C. C. Tai; J. B. Bamberg; Alfonso H. del Rio; Max Martin; J. B. Bamberg; et al. (American Journal of Potato Research, 1997)