Browsing by Author "Johnston, Alice S. A."

Now showing 1 - 4 of 4
  • Thumbnail Image
    ItemOpen Access
    Greater local cooling effects of trees across globally distributed urban green spaces
    (Elsevier, 2023-11-24) Kim, Jiyoung; Khouakhi, Abdou; Corstanje, Ronald; Johnston, Alice S. A.
    Urban green spaces (UGS) are an effective mitigation strategy for urban heat islands (UHIs) through their evapotranspiration and shading effects. Yet, the extent to which local UGS cooling effects vary across different background climates, plant characteristics and urban settings across global cities is not well understood. This study analysed 265 local air temperature (TA) measurements from 58 published studies across globally distributed sites to infer the potential influence of background climate, plant and urban variables among different UGS types (trees, grass, green roofs and walls). We show that trees were more effective at reducing local TA, with reductions 2–3 times greater than grass and green roofs and walls. We use a hierarchical linear mixed effects model to reveal that background climate (mean annual temperature) and plant characteristics (specific leaf area vegetation index) had the greatest influence on cooling effects across UGS types, while urban characteristics did not significantly influence the cooling effects of UGS. Notably, trees dominated the overall local cooling effects across global cities, indicating that greater tree growth in mild climates with lower mean annual temperatures has the greatest mitigation potential against UHIs. Our findings provide insights for urban heat mitigation using UGS interventions, particularly trees across cities worldwide with diverse climatic and environmental conditions and highlight the essential role of trees in creating healthy urban living environments for citizens under extreme weather conditions.
  • Thumbnail Image
    ItemOpen Access
    Predicting emergent animal biodiversity patterns across multiple scales
    (Wiley, 2024-07-10) Johnston, Alice S. A.
    Restoring biodiversity-based resilience and ecosystem multi-functionality needs to be informed by more accurate predictions of animal biodiversity responses to environmental change. Ecological models make a substantial contribution to this understanding, especially when they encode the biological mechanisms and processes that give rise to emergent patterns (population, community, ecosystem properties and dynamics). Here, a distinction between ‘mechanistic’ and ‘process-based’ ecological models is established to review existing approaches. Mechanistic and process-based ecological models have made key advances to understanding the structure, function and dynamics of animal biodiversity, but are typically designed to account for specific levels of biological organisation and spatiotemporal scales. Cross-scale ecological models, which predict emergent co-occurring biodiversity patterns at interacting scales of space, time and biological organisation, is a critical next step in predictive ecology. A way forward is to first capitalise on existing models to systematically evaluate the ability of scale-explicit mechanisms and processes to predict emergent patterns at alternative scales. Such model intercomparisons will reveal mechanism to process transitions across fine to broad scales, overcome approach-specific barriers to model realism or tractability and identify gaps which necessitate the development of new fundamental principles. Key challenges surrounding model complexity and uncertainty would need to be addressed, and while opportunities from big data can streamline the integration of multiple scale-explicit biodiversity patterns, ambitious cross-scale field studies are also needed. Crucially, overcoming cross-scale ecological modelling challenges would unite disparate fields of ecology with the common goal of improving the evidence-base to safeguard biodiversity and ecosystems under novel environmental change.
  • Thumbnail Image
    ItemOpen Access
    Temperature thresholds of ecosystem respiration at a global scale
    (Nature Publishing, 2021-02-22) Johnston, Alice S. A.; Meade, Andrew; Ardö, Jonas; Arriga, Nicola; Black, Andy; Blanken, Peter D.; Bonal, Damien; Brümmer, Christian; Cescatti, Alessandro; Dušek, Jiří; Graf, Alexander; Gioli, Beniamino; Goded, Ignacio; Gough, Christopher M.; Ikawa, Hiroki; Jassal, Rachhpal; Kobayashi, Hideki; Magliulo, Vincenzo; Manca, Giovanni; Montagnani, Leonardo; Moyano, Fernando E.; Olesen, Jørgen E.; Sachs, Torsten; Shao, Changliang; Tagesson, Torbern; Wohlfahrt, Georg; Wolf, Sebastian; Woodgate, William; Varlagin, Andrej; Venditti, Chris
    Ecosystem respiration is a major component of the global terrestrial carbon cycle and is strongly influenced by temperature. The global extent of the temperature–ecosystem respiration relationship, however, has not been fully explored. Here, we test linear and threshold models of ecosystem respiration across 210 globally distributed eddy covariance sites over an extensive temperature range. We find thresholds to the global temperature–ecosystem respiration relationship at high and low air temperatures and mid soil temperatures, which represent transitions in the temperature dependence and sensitivity of ecosystem respiration. Annual ecosystem respiration rates show a markedly reduced temperature dependence and sensitivity compared to half-hourly rates, and a single mid-temperature threshold for both air and soil temperature. Our study indicates a distinction in the influence of environmental factors, including temperature, on ecosystem respiration between latitudinal and climate gradients at short (half-hourly) and long (annual) timescales. Such climatological differences in the temperature sensitivity of ecosystem respiration have important consequences for the terrestrial net carbon sink under ongoing climate change.
  • Thumbnail Image
    ItemOpen Access
    Three questions to ask before using model outputs for decision support
    (Nature Publishing Group, 2020-09-30) Grimm, Volker; Johnston, Alice S. A.; Thulke, H.-H.; Forbes, V. E.; Thorbek, P.
    Decision makers must have sufficient confidence in models if they are to influence their decisions. We propose three screening questions to critically evaluate models with respect to their purpose, organization, and evidence. They enable a more transparent, robust, and secure use of model outputs.